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README.md
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|
1 |
+
---
|
2 |
+
base_model: infgrad/stella-base-en-v2
|
3 |
+
language:
|
4 |
+
- en
|
5 |
+
license: mit
|
6 |
+
tags:
|
7 |
+
- sentence-transformers
|
8 |
+
- feature-extraction
|
9 |
+
- sentence-similarity
|
10 |
+
- mteb
|
11 |
+
- llama-cpp
|
12 |
+
- gguf-my-repo
|
13 |
+
model-index:
|
14 |
+
- name: stella-base-en-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: 77.19402985074628
|
27 |
+
- type: ap
|
28 |
+
value: 40.43267503017359
|
29 |
+
- type: f1
|
30 |
+
value: 71.15585210518594
|
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: 93.256675
|
42 |
+
- type: ap
|
43 |
+
value: 90.00824833079179
|
44 |
+
- type: f1
|
45 |
+
value: 93.2473146151734
|
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: 49.612
|
57 |
+
- type: f1
|
58 |
+
value: 48.530785631574304
|
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 |
+
value: 37.411
|
70 |
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- type: map_at_10
|
71 |
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value: 52.673
|
72 |
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|
73 |
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value: 53.410999999999994
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74 |
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- type: map_at_1000
|
75 |
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value: 53.415
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76 |
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|
77 |
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value: 48.495
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78 |
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|
79 |
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value: 51.183
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80 |
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|
81 |
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value: 37.838
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82 |
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|
83 |
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value: 52.844
|
84 |
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- type: mrr_at_100
|
85 |
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value: 53.581999999999994
|
86 |
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- type: mrr_at_1000
|
87 |
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value: 53.586
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88 |
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|
89 |
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value: 48.672
|
90 |
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|
91 |
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value: 51.272
|
92 |
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|
93 |
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value: 37.411
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94 |
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- type: ndcg_at_10
|
95 |
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value: 60.626999999999995
|
96 |
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|
97 |
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value: 63.675000000000004
|
98 |
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- type: ndcg_at_1000
|
99 |
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value: 63.776999999999994
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100 |
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- type: ndcg_at_3
|
101 |
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value: 52.148
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102 |
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- type: ndcg_at_5
|
103 |
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value: 57.001999999999995
|
104 |
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- type: precision_at_1
|
105 |
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value: 37.411
|
106 |
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- type: precision_at_10
|
107 |
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value: 8.578
|
108 |
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- type: precision_at_100
|
109 |
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value: 0.989
|
110 |
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- type: precision_at_1000
|
111 |
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value: 0.1
|
112 |
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- type: precision_at_3
|
113 |
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value: 20.91
|
114 |
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- type: precision_at_5
|
115 |
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value: 14.908
|
116 |
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- type: recall_at_1
|
117 |
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value: 37.411
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118 |
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- type: recall_at_10
|
119 |
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value: 85.775
|
120 |
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- type: recall_at_100
|
121 |
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value: 98.86200000000001
|
122 |
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- type: recall_at_1000
|
123 |
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value: 99.644
|
124 |
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- type: recall_at_3
|
125 |
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value: 62.731
|
126 |
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- type: recall_at_5
|
127 |
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value: 74.53800000000001
|
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: 47.24219029437865
|
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: 40.474604844291726
|
150 |
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- task:
|
151 |
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type: Reranking
|
152 |
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dataset:
|
153 |
+
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: 62.720542706366054
|
161 |
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- type: mrr
|
162 |
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value: 75.59633733456448
|
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
|
169 |
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split: test
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170 |
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revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
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171 |
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metrics:
|
172 |
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- type: cos_sim_pearson
|
173 |
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value: 86.31345008397868
|
174 |
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- type: cos_sim_spearman
|
175 |
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value: 85.94292212320399
|
176 |
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- type: euclidean_pearson
|
177 |
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value: 85.03974302774525
|
178 |
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- type: euclidean_spearman
|
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value: 85.88087251659051
|
180 |
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- type: manhattan_pearson
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value: 84.91900996712951
|
182 |
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- type: manhattan_spearman
|
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value: 85.96701905781116
|
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
|
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: 84.72727272727273
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195 |
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- type: f1
|
196 |
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value: 84.29572512364581
|
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: 39.55532460397536
|
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
|
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: 35.91195973591251
|
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 |
+
value: 32.822
|
230 |
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- type: map_at_10
|
231 |
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value: 44.139
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232 |
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|
233 |
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value: 45.786
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234 |
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235 |
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value: 45.906000000000006
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236 |
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238 |
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value: 42.575
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240 |
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241 |
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value: 41.059
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242 |
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243 |
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value: 50.751000000000005
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244 |
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245 |
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value: 51.548
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246 |
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247 |
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value: 51.583999999999996
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249 |
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value: 48.236000000000004
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251 |
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value: 49.838
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252 |
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253 |
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value: 41.059
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254 |
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255 |
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value: 50.573
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256 |
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257 |
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value: 56.25
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258 |
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259 |
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value: 58.004
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260 |
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261 |
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value: 45.995000000000005
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262 |
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263 |
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value: 48.18
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264 |
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265 |
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value: 41.059
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266 |
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|
267 |
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value: 9.757
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268 |
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269 |
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value: 1.609
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270 |
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271 |
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value: 0.20600000000000002
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272 |
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273 |
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value: 22.222
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274 |
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- type: precision_at_5
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275 |
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value: 16.023
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276 |
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277 |
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value: 32.822
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278 |
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279 |
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value: 61.794000000000004
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280 |
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281 |
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value: 85.64699999999999
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282 |
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283 |
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value: 96.836
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284 |
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285 |
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value: 47.999
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286 |
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287 |
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value: 54.376999999999995
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288 |
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289 |
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value: 29.579
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290 |
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291 |
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value: 39.787
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292 |
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293 |
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value: 40.976
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294 |
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value: 41.108
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296 |
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297 |
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value: 36.819
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298 |
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299 |
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value: 38.437
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300 |
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301 |
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value: 37.516
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302 |
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303 |
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value: 45.822
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304 |
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305 |
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value: 46.454
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306 |
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307 |
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value: 46.495999999999995
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308 |
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309 |
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value: 43.556
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310 |
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311 |
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value: 44.814
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312 |
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313 |
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value: 37.516
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314 |
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315 |
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value: 45.5
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316 |
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317 |
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value: 49.707
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318 |
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319 |
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value: 51.842
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320 |
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321 |
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value: 41.369
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322 |
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323 |
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value: 43.161
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324 |
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325 |
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value: 37.516
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326 |
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327 |
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value: 8.713
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328 |
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329 |
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value: 1.38
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330 |
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331 |
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value: 0.188
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332 |
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333 |
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334 |
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335 |
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value: 14.280000000000001
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336 |
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337 |
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value: 29.579
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338 |
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339 |
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value: 55.458
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340 |
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346 |
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347 |
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348 |
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350 |
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351 |
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352 |
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353 |
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354 |
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355 |
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356 |
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357 |
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358 |
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359 |
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360 |
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362 |
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365 |
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366 |
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368 |
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369 |
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370 |
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372 |
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374 |
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375 |
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378 |
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380 |
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382 |
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386 |
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395 |
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399 |
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400 |
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405 |
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439 |
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446 |
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451 |
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|
453 |
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454 |
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|
455 |
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value: 10.237
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456 |
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458 |
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462 |
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value: 91.932
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464 |
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|
465 |
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value: 39.804
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|
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|
469 |
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value: 16.683
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470 |
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|
471 |
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value: 25.013999999999996
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472 |
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473 |
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474 |
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475 |
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476 |
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|
477 |
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value: 22.357
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478 |
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|
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481 |
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value: 20.896
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482 |
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- type: mrr_at_10
|
483 |
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value: 29.758000000000003
|
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882 |
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903 |
+
value: 38.924
|
904 |
+
- type: mrr_at_100
|
905 |
+
value: 39.95
|
906 |
+
- type: mrr_at_1000
|
907 |
+
value: 40.003
|
908 |
+
- type: mrr_at_3
|
909 |
+
value: 36.594
|
910 |
+
- type: mrr_at_5
|
911 |
+
value: 37.701
|
912 |
+
- type: ndcg_at_1
|
913 |
+
value: 31.028
|
914 |
+
- type: ndcg_at_10
|
915 |
+
value: 39.848
|
916 |
+
- type: ndcg_at_100
|
917 |
+
value: 45.721000000000004
|
918 |
+
- type: ndcg_at_1000
|
919 |
+
value: 48.424
|
920 |
+
- type: ndcg_at_3
|
921 |
+
value: 35.329
|
922 |
+
- type: ndcg_at_5
|
923 |
+
value: 36.779
|
924 |
+
- type: precision_at_1
|
925 |
+
value: 31.028
|
926 |
+
- type: precision_at_10
|
927 |
+
value: 7.51
|
928 |
+
- type: precision_at_100
|
929 |
+
value: 1.478
|
930 |
+
- type: precision_at_1000
|
931 |
+
value: 0.24
|
932 |
+
- type: precision_at_3
|
933 |
+
value: 16.337
|
934 |
+
- type: precision_at_5
|
935 |
+
value: 11.383000000000001
|
936 |
+
- type: recall_at_1
|
937 |
+
value: 25.523
|
938 |
+
- type: recall_at_10
|
939 |
+
value: 50.735
|
940 |
+
- type: recall_at_100
|
941 |
+
value: 76.593
|
942 |
+
- type: recall_at_1000
|
943 |
+
value: 93.771
|
944 |
+
- type: recall_at_3
|
945 |
+
value: 37.574000000000005
|
946 |
+
- type: recall_at_5
|
947 |
+
value: 41.602
|
948 |
+
- type: map_at_1
|
949 |
+
value: 20.746000000000002
|
950 |
+
- type: map_at_10
|
951 |
+
value: 28.557
|
952 |
+
- type: map_at_100
|
953 |
+
value: 29.575000000000003
|
954 |
+
- type: map_at_1000
|
955 |
+
value: 29.659000000000002
|
956 |
+
- type: map_at_3
|
957 |
+
value: 25.753999999999998
|
958 |
+
- type: map_at_5
|
959 |
+
value: 27.254
|
960 |
+
- type: mrr_at_1
|
961 |
+
value: 22.736
|
962 |
+
- type: mrr_at_10
|
963 |
+
value: 30.769000000000002
|
964 |
+
- type: mrr_at_100
|
965 |
+
value: 31.655
|
966 |
+
- type: mrr_at_1000
|
967 |
+
value: 31.717000000000002
|
968 |
+
- type: mrr_at_3
|
969 |
+
value: 28.065
|
970 |
+
- type: mrr_at_5
|
971 |
+
value: 29.543999999999997
|
972 |
+
- type: ndcg_at_1
|
973 |
+
value: 22.736
|
974 |
+
- type: ndcg_at_10
|
975 |
+
value: 33.545
|
976 |
+
- type: ndcg_at_100
|
977 |
+
value: 38.743
|
978 |
+
- type: ndcg_at_1000
|
979 |
+
value: 41.002
|
980 |
+
- type: ndcg_at_3
|
981 |
+
value: 28.021
|
982 |
+
- type: ndcg_at_5
|
983 |
+
value: 30.586999999999996
|
984 |
+
- type: precision_at_1
|
985 |
+
value: 22.736
|
986 |
+
- type: precision_at_10
|
987 |
+
value: 5.416
|
988 |
+
- type: precision_at_100
|
989 |
+
value: 0.8710000000000001
|
990 |
+
- type: precision_at_1000
|
991 |
+
value: 0.116
|
992 |
+
- type: precision_at_3
|
993 |
+
value: 11.953
|
994 |
+
- type: precision_at_5
|
995 |
+
value: 8.651
|
996 |
+
- type: recall_at_1
|
997 |
+
value: 20.746000000000002
|
998 |
+
- type: recall_at_10
|
999 |
+
value: 46.87
|
1000 |
+
- type: recall_at_100
|
1001 |
+
value: 71.25200000000001
|
1002 |
+
- type: recall_at_1000
|
1003 |
+
value: 88.26
|
1004 |
+
- type: recall_at_3
|
1005 |
+
value: 32.029999999999994
|
1006 |
+
- type: recall_at_5
|
1007 |
+
value: 38.21
|
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: 12.105
|
1019 |
+
- type: map_at_10
|
1020 |
+
value: 20.577
|
1021 |
+
- type: map_at_100
|
1022 |
+
value: 22.686999999999998
|
1023 |
+
- type: map_at_1000
|
1024 |
+
value: 22.889
|
1025 |
+
- type: map_at_3
|
1026 |
+
value: 17.174
|
1027 |
+
- type: map_at_5
|
1028 |
+
value: 18.807
|
1029 |
+
- type: mrr_at_1
|
1030 |
+
value: 27.101
|
1031 |
+
- type: mrr_at_10
|
1032 |
+
value: 38.475
|
1033 |
+
- type: mrr_at_100
|
1034 |
+
value: 39.491
|
1035 |
+
- type: mrr_at_1000
|
1036 |
+
value: 39.525
|
1037 |
+
- type: mrr_at_3
|
1038 |
+
value: 34.886
|
1039 |
+
- type: mrr_at_5
|
1040 |
+
value: 36.922
|
1041 |
+
- type: ndcg_at_1
|
1042 |
+
value: 27.101
|
1043 |
+
- type: ndcg_at_10
|
1044 |
+
value: 29.002
|
1045 |
+
- type: ndcg_at_100
|
1046 |
+
value: 37.218
|
1047 |
+
- type: ndcg_at_1000
|
1048 |
+
value: 40.644000000000005
|
1049 |
+
- type: ndcg_at_3
|
1050 |
+
value: 23.464
|
1051 |
+
- type: ndcg_at_5
|
1052 |
+
value: 25.262
|
1053 |
+
- type: precision_at_1
|
1054 |
+
value: 27.101
|
1055 |
+
- type: precision_at_10
|
1056 |
+
value: 9.179
|
1057 |
+
- type: precision_at_100
|
1058 |
+
value: 1.806
|
1059 |
+
- type: precision_at_1000
|
1060 |
+
value: 0.244
|
1061 |
+
- type: precision_at_3
|
1062 |
+
value: 17.394000000000002
|
1063 |
+
- type: precision_at_5
|
1064 |
+
value: 13.342
|
1065 |
+
- type: recall_at_1
|
1066 |
+
value: 12.105
|
1067 |
+
- type: recall_at_10
|
1068 |
+
value: 35.143
|
1069 |
+
- type: recall_at_100
|
1070 |
+
value: 63.44499999999999
|
1071 |
+
- type: recall_at_1000
|
1072 |
+
value: 82.49499999999999
|
1073 |
+
- type: recall_at_3
|
1074 |
+
value: 21.489
|
1075 |
+
- type: recall_at_5
|
1076 |
+
value: 26.82
|
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: 8.769
|
1088 |
+
- type: map_at_10
|
1089 |
+
value: 18.619
|
1090 |
+
- type: map_at_100
|
1091 |
+
value: 26.3
|
1092 |
+
- type: map_at_1000
|
1093 |
+
value: 28.063
|
1094 |
+
- type: map_at_3
|
1095 |
+
value: 13.746
|
1096 |
+
- type: map_at_5
|
1097 |
+
value: 16.035
|
1098 |
+
- type: mrr_at_1
|
1099 |
+
value: 65.25
|
1100 |
+
- type: mrr_at_10
|
1101 |
+
value: 73.678
|
1102 |
+
- type: mrr_at_100
|
1103 |
+
value: 73.993
|
1104 |
+
- type: mrr_at_1000
|
1105 |
+
value: 74.003
|
1106 |
+
- type: mrr_at_3
|
1107 |
+
value: 72.042
|
1108 |
+
- type: mrr_at_5
|
1109 |
+
value: 72.992
|
1110 |
+
- type: ndcg_at_1
|
1111 |
+
value: 53.625
|
1112 |
+
- type: ndcg_at_10
|
1113 |
+
value: 39.638
|
1114 |
+
- type: ndcg_at_100
|
1115 |
+
value: 44.601
|
1116 |
+
- type: ndcg_at_1000
|
1117 |
+
value: 52.80200000000001
|
1118 |
+
- type: ndcg_at_3
|
1119 |
+
value: 44.727
|
1120 |
+
- type: ndcg_at_5
|
1121 |
+
value: 42.199
|
1122 |
+
- type: precision_at_1
|
1123 |
+
value: 65.25
|
1124 |
+
- type: precision_at_10
|
1125 |
+
value: 31.025000000000002
|
1126 |
+
- type: precision_at_100
|
1127 |
+
value: 10.174999999999999
|
1128 |
+
- type: precision_at_1000
|
1129 |
+
value: 2.0740000000000003
|
1130 |
+
- type: precision_at_3
|
1131 |
+
value: 48.083
|
1132 |
+
- type: precision_at_5
|
1133 |
+
value: 40.6
|
1134 |
+
- type: recall_at_1
|
1135 |
+
value: 8.769
|
1136 |
+
- type: recall_at_10
|
1137 |
+
value: 23.910999999999998
|
1138 |
+
- type: recall_at_100
|
1139 |
+
value: 51.202999999999996
|
1140 |
+
- type: recall_at_1000
|
1141 |
+
value: 77.031
|
1142 |
+
- type: recall_at_3
|
1143 |
+
value: 15.387999999999998
|
1144 |
+
- type: recall_at_5
|
1145 |
+
value: 18.919
|
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: 54.47
|
1157 |
+
- type: f1
|
1158 |
+
value: 48.21839043361556
|
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: 63.564
|
1170 |
+
- type: map_at_10
|
1171 |
+
value: 74.236
|
1172 |
+
- type: map_at_100
|
1173 |
+
value: 74.53699999999999
|
1174 |
+
- type: map_at_1000
|
1175 |
+
value: 74.557
|
1176 |
+
- type: map_at_3
|
1177 |
+
value: 72.556
|
1178 |
+
- type: map_at_5
|
1179 |
+
value: 73.656
|
1180 |
+
- type: mrr_at_1
|
1181 |
+
value: 68.497
|
1182 |
+
- type: mrr_at_10
|
1183 |
+
value: 78.373
|
1184 |
+
- type: mrr_at_100
|
1185 |
+
value: 78.54299999999999
|
1186 |
+
- type: mrr_at_1000
|
1187 |
+
value: 78.549
|
1188 |
+
- type: mrr_at_3
|
1189 |
+
value: 77.03
|
1190 |
+
- type: mrr_at_5
|
1191 |
+
value: 77.938
|
1192 |
+
- type: ndcg_at_1
|
1193 |
+
value: 68.497
|
1194 |
+
- type: ndcg_at_10
|
1195 |
+
value: 79.12599999999999
|
1196 |
+
- type: ndcg_at_100
|
1197 |
+
value: 80.319
|
1198 |
+
- type: ndcg_at_1000
|
1199 |
+
value: 80.71199999999999
|
1200 |
+
- type: ndcg_at_3
|
1201 |
+
value: 76.209
|
1202 |
+
- type: ndcg_at_5
|
1203 |
+
value: 77.90700000000001
|
1204 |
+
- type: precision_at_1
|
1205 |
+
value: 68.497
|
1206 |
+
- type: precision_at_10
|
1207 |
+
value: 9.958
|
1208 |
+
- type: precision_at_100
|
1209 |
+
value: 1.077
|
1210 |
+
- type: precision_at_1000
|
1211 |
+
value: 0.11299999999999999
|
1212 |
+
- type: precision_at_3
|
1213 |
+
value: 29.908
|
1214 |
+
- type: precision_at_5
|
1215 |
+
value: 18.971
|
1216 |
+
- type: recall_at_1
|
1217 |
+
value: 63.564
|
1218 |
+
- type: recall_at_10
|
1219 |
+
value: 90.05199999999999
|
1220 |
+
- type: recall_at_100
|
1221 |
+
value: 95.028
|
1222 |
+
- type: recall_at_1000
|
1223 |
+
value: 97.667
|
1224 |
+
- type: recall_at_3
|
1225 |
+
value: 82.17999999999999
|
1226 |
+
- type: recall_at_5
|
1227 |
+
value: 86.388
|
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: 19.042
|
1239 |
+
- type: map_at_10
|
1240 |
+
value: 30.764999999999997
|
1241 |
+
- type: map_at_100
|
1242 |
+
value: 32.678000000000004
|
1243 |
+
- type: map_at_1000
|
1244 |
+
value: 32.881
|
1245 |
+
- type: map_at_3
|
1246 |
+
value: 26.525
|
1247 |
+
- type: map_at_5
|
1248 |
+
value: 28.932000000000002
|
1249 |
+
- type: mrr_at_1
|
1250 |
+
value: 37.653999999999996
|
1251 |
+
- type: mrr_at_10
|
1252 |
+
value: 46.597
|
1253 |
+
- type: mrr_at_100
|
1254 |
+
value: 47.413
|
1255 |
+
- type: mrr_at_1000
|
1256 |
+
value: 47.453
|
1257 |
+
- type: mrr_at_3
|
1258 |
+
value: 43.775999999999996
|
1259 |
+
- type: mrr_at_5
|
1260 |
+
value: 45.489000000000004
|
1261 |
+
- type: ndcg_at_1
|
1262 |
+
value: 37.653999999999996
|
1263 |
+
- type: ndcg_at_10
|
1264 |
+
value: 38.615
|
1265 |
+
- type: ndcg_at_100
|
1266 |
+
value: 45.513999999999996
|
1267 |
+
- type: ndcg_at_1000
|
1268 |
+
value: 48.815999999999995
|
1269 |
+
- type: ndcg_at_3
|
1270 |
+
value: 34.427
|
1271 |
+
- type: ndcg_at_5
|
1272 |
+
value: 35.954
|
1273 |
+
- type: precision_at_1
|
1274 |
+
value: 37.653999999999996
|
1275 |
+
- type: precision_at_10
|
1276 |
+
value: 10.864
|
1277 |
+
- type: precision_at_100
|
1278 |
+
value: 1.7850000000000001
|
1279 |
+
- type: precision_at_1000
|
1280 |
+
value: 0.23800000000000002
|
1281 |
+
- type: precision_at_3
|
1282 |
+
value: 22.788
|
1283 |
+
- type: precision_at_5
|
1284 |
+
value: 17.346
|
1285 |
+
- type: recall_at_1
|
1286 |
+
value: 19.042
|
1287 |
+
- type: recall_at_10
|
1288 |
+
value: 45.707
|
1289 |
+
- type: recall_at_100
|
1290 |
+
value: 71.152
|
1291 |
+
- type: recall_at_1000
|
1292 |
+
value: 90.7
|
1293 |
+
- type: recall_at_3
|
1294 |
+
value: 30.814000000000004
|
1295 |
+
- type: recall_at_5
|
1296 |
+
value: 37.478
|
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: 38.001000000000005
|
1308 |
+
- type: map_at_10
|
1309 |
+
value: 59.611000000000004
|
1310 |
+
- type: map_at_100
|
1311 |
+
value: 60.582
|
1312 |
+
- type: map_at_1000
|
1313 |
+
value: 60.646
|
1314 |
+
- type: map_at_3
|
1315 |
+
value: 56.031
|
1316 |
+
- type: map_at_5
|
1317 |
+
value: 58.243
|
1318 |
+
- type: mrr_at_1
|
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value: 76.003
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1320 |
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1321 |
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value: 82.15400000000001
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1322 |
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1323 |
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1324 |
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1325 |
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1326 |
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1327 |
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1328 |
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1329 |
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value: 81.742
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1330 |
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1331 |
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value: 76.003
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1332 |
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|
1333 |
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value: 68.216
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1334 |
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|
1335 |
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value: 71.601
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1336 |
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1337 |
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value: 72.821
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1338 |
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1339 |
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value: 63.109
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1341 |
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value: 65.902
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1342 |
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1343 |
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value: 76.003
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1344 |
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1345 |
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value: 14.379
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1346 |
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1347 |
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value: 1.702
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1348 |
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1349 |
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value: 0.186
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|
1351 |
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value: 40.396
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1352 |
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|
1353 |
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value: 26.442
|
1354 |
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- type: recall_at_1
|
1355 |
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value: 38.001000000000005
|
1356 |
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- type: recall_at_10
|
1357 |
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value: 71.897
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1358 |
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1359 |
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value: 85.105
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1360 |
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- type: recall_at_1000
|
1361 |
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value: 93.133
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1362 |
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- type: recall_at_3
|
1363 |
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value: 60.594
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1364 |
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- type: recall_at_5
|
1365 |
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value: 66.104
|
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
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1373 |
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1374 |
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metrics:
|
1375 |
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- type: accuracy
|
1376 |
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value: 91.31280000000001
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1377 |
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- type: ap
|
1378 |
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value: 87.53723467501632
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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
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1387 |
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split: dev
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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: 21.917
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1392 |
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- type: map_at_10
|
1393 |
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value: 34.117999999999995
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1394 |
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1395 |
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value: 35.283
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1396 |
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1397 |
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value: 35.333999999999996
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1399 |
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value: 30.330000000000002
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1400 |
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1401 |
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value: 32.461
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1402 |
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1403 |
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value: 22.579
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1404 |
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|
1405 |
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value: 34.794000000000004
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1406 |
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- type: mrr_at_100
|
1407 |
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value: 35.893
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1408 |
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- type: mrr_at_1000
|
1409 |
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value: 35.937000000000005
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1410 |
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- type: mrr_at_3
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1411 |
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value: 31.091
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1412 |
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1413 |
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value: 33.173
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1414 |
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1415 |
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value: 22.579
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1416 |
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- type: ndcg_at_10
|
1417 |
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value: 40.951
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1418 |
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- type: ndcg_at_100
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1419 |
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value: 46.558
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1420 |
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- type: ndcg_at_1000
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1421 |
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value: 47.803000000000004
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1422 |
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- type: ndcg_at_3
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1423 |
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value: 33.262
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1424 |
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1425 |
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value: 37.036
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1426 |
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1427 |
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value: 22.579
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1428 |
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|
1429 |
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value: 6.463000000000001
|
1430 |
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- type: precision_at_100
|
1431 |
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value: 0.928
|
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: 14.174000000000001
|
1436 |
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- type: precision_at_5
|
1437 |
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value: 10.421
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1438 |
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- type: recall_at_1
|
1439 |
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value: 21.917
|
1440 |
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- type: recall_at_10
|
1441 |
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value: 61.885
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1442 |
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- type: recall_at_100
|
1443 |
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value: 87.847
|
1444 |
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- type: recall_at_1000
|
1445 |
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value: 97.322
|
1446 |
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- type: recall_at_3
|
1447 |
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value: 41.010000000000005
|
1448 |
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- type: recall_at_5
|
1449 |
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value: 50.031000000000006
|
1450 |
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- task:
|
1451 |
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type: Classification
|
1452 |
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dataset:
|
1453 |
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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: 93.49521203830369
|
1461 |
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- type: f1
|
1462 |
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value: 93.30882341740241
|
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
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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: 71.0579115367077
|
1474 |
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- type: f1
|
1475 |
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value: 51.2368258319339
|
1476 |
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- task:
|
1477 |
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type: Classification
|
1478 |
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dataset:
|
1479 |
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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
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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: 73.88029589778077
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1487 |
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- type: f1
|
1488 |
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value: 72.34422048584663
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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: 78.2817753866846
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1500 |
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- type: f1
|
1501 |
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value: 77.87746050004304
|
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
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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: 33.247341454119216
|
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: 31.9647477166234
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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
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1529 |
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config: default
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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: 31.90698374676892
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1535 |
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- type: mrr
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1536 |
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value: 33.07523683771251
|
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
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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: 6.717
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1548 |
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- type: map_at_10
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1549 |
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value: 14.566
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1550 |
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1551 |
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value: 18.465999999999998
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1552 |
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1553 |
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value: 20.033
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1554 |
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1555 |
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value: 10.863
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1556 |
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1557 |
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value: 12.589
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1558 |
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1559 |
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value: 49.845
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1560 |
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1561 |
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value: 58.385
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1562 |
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1563 |
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value: 58.989999999999995
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1564 |
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1565 |
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1566 |
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1567 |
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1568 |
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1569 |
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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: 37.511
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1574 |
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1575 |
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value: 34.537
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1576 |
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1577 |
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1578 |
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1579 |
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value: 43.713
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1580 |
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1581 |
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value: 41.303
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1582 |
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1583 |
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value: 49.845
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1584 |
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1585 |
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value: 27.307
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1586 |
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1587 |
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value: 8.746
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1588 |
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1589 |
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value: 2.182
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1590 |
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1591 |
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value: 40.764
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1592 |
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1593 |
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value: 35.232
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1594 |
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- type: recall_at_1
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1595 |
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value: 6.717
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1596 |
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- type: recall_at_10
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1597 |
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value: 18.107
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1598 |
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- type: recall_at_100
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1599 |
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value: 33.759
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1600 |
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- type: recall_at_1000
|
1601 |
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value: 67.31
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1602 |
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- type: recall_at_3
|
1603 |
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value: 11.68
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1604 |
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- type: recall_at_5
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1605 |
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value: 14.557999999999998
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1606 |
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- task:
|
1607 |
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type: Retrieval
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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: 27.633999999999997
|
1617 |
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- type: map_at_10
|
1618 |
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value: 42.400999999999996
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1619 |
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- type: map_at_100
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1620 |
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value: 43.561
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1621 |
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1622 |
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value: 43.592
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1623 |
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1624 |
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value: 37.865
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1625 |
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1626 |
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value: 40.650999999999996
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1627 |
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1628 |
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value: 31.286
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1629 |
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|
1630 |
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value: 44.996
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1631 |
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1632 |
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value: 45.889
|
1633 |
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- type: mrr_at_1000
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1634 |
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value: 45.911
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1635 |
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- type: mrr_at_3
|
1636 |
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value: 41.126000000000005
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1637 |
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- type: mrr_at_5
|
1638 |
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value: 43.536
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1639 |
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- type: ndcg_at_1
|
1640 |
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value: 31.257
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1641 |
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|
1642 |
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value: 50.197
|
1643 |
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- type: ndcg_at_100
|
1644 |
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value: 55.062
|
1645 |
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- type: ndcg_at_1000
|
1646 |
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value: 55.81700000000001
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1647 |
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- type: ndcg_at_3
|
1648 |
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value: 41.650999999999996
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1649 |
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|
1650 |
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value: 46.324
|
1651 |
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- type: precision_at_1
|
1652 |
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value: 31.257
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1653 |
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- type: precision_at_10
|
1654 |
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value: 8.508000000000001
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1655 |
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- type: precision_at_100
|
1656 |
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value: 1.121
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1657 |
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|
1658 |
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value: 0.11900000000000001
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1659 |
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- type: precision_at_3
|
1660 |
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value: 19.1
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1661 |
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- type: precision_at_5
|
1662 |
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value: 14.16
|
1663 |
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- type: recall_at_1
|
1664 |
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value: 27.633999999999997
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1665 |
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- type: recall_at_10
|
1666 |
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value: 71.40100000000001
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1667 |
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- type: recall_at_100
|
1668 |
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value: 92.463
|
1669 |
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- type: recall_at_1000
|
1670 |
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value: 98.13199999999999
|
1671 |
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- type: recall_at_3
|
1672 |
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value: 49.382
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1673 |
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- type: recall_at_5
|
1674 |
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value: 60.144
|
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 |
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metrics:
|
1684 |
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- type: map_at_1
|
1685 |
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value: 71.17099999999999
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1686 |
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1687 |
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value: 85.036
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1688 |
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1689 |
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value: 85.67099999999999
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1690 |
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1691 |
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value: 85.68599999999999
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1692 |
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1693 |
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value: 82.086
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1694 |
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1695 |
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value: 83.956
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1696 |
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1697 |
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value: 82.04
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1698 |
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1699 |
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value: 88.018
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1700 |
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- type: mrr_at_100
|
1701 |
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value: 88.114
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1702 |
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- type: mrr_at_1000
|
1703 |
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value: 88.115
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1704 |
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- type: mrr_at_3
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1705 |
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value: 87.047
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1706 |
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1707 |
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value: 87.73100000000001
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1708 |
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- type: ndcg_at_1
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1709 |
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value: 82.03
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1710 |
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- type: ndcg_at_10
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1711 |
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value: 88.717
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1712 |
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- type: ndcg_at_100
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1713 |
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value: 89.904
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1714 |
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- type: ndcg_at_1000
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1715 |
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value: 89.991
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1716 |
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- type: ndcg_at_3
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1717 |
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value: 85.89099999999999
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1718 |
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- type: ndcg_at_5
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1719 |
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value: 87.485
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1720 |
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- type: precision_at_1
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1721 |
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value: 82.03
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1722 |
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- type: precision_at_10
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1723 |
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value: 13.444999999999999
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1724 |
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- type: precision_at_100
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1725 |
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value: 1.533
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1726 |
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- type: precision_at_1000
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1727 |
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value: 0.157
|
1728 |
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- type: precision_at_3
|
1729 |
+
value: 37.537
|
1730 |
+
- type: precision_at_5
|
1731 |
+
value: 24.692
|
1732 |
+
- type: recall_at_1
|
1733 |
+
value: 71.17099999999999
|
1734 |
+
- type: recall_at_10
|
1735 |
+
value: 95.634
|
1736 |
+
- type: recall_at_100
|
1737 |
+
value: 99.614
|
1738 |
+
- type: recall_at_1000
|
1739 |
+
value: 99.99
|
1740 |
+
- type: recall_at_3
|
1741 |
+
value: 87.48
|
1742 |
+
- type: recall_at_5
|
1743 |
+
value: 91.996
|
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: 55.067219624685315
|
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: 62.121822992300444
|
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: 4.153
|
1777 |
+
- type: map_at_10
|
1778 |
+
value: 11.024000000000001
|
1779 |
+
- type: map_at_100
|
1780 |
+
value: 13.233
|
1781 |
+
- type: map_at_1000
|
1782 |
+
value: 13.62
|
1783 |
+
- type: map_at_3
|
1784 |
+
value: 7.779999999999999
|
1785 |
+
- type: map_at_5
|
1786 |
+
value: 9.529
|
1787 |
+
- type: mrr_at_1
|
1788 |
+
value: 20.599999999999998
|
1789 |
+
- type: mrr_at_10
|
1790 |
+
value: 31.361
|
1791 |
+
- type: mrr_at_100
|
1792 |
+
value: 32.738
|
1793 |
+
- type: mrr_at_1000
|
1794 |
+
value: 32.792
|
1795 |
+
- type: mrr_at_3
|
1796 |
+
value: 28.15
|
1797 |
+
- type: mrr_at_5
|
1798 |
+
value: 30.085
|
1799 |
+
- type: ndcg_at_1
|
1800 |
+
value: 20.599999999999998
|
1801 |
+
- type: ndcg_at_10
|
1802 |
+
value: 18.583
|
1803 |
+
- type: ndcg_at_100
|
1804 |
+
value: 27.590999999999998
|
1805 |
+
- type: ndcg_at_1000
|
1806 |
+
value: 34.001
|
1807 |
+
- type: ndcg_at_3
|
1808 |
+
value: 17.455000000000002
|
1809 |
+
- type: ndcg_at_5
|
1810 |
+
value: 15.588
|
1811 |
+
- type: precision_at_1
|
1812 |
+
value: 20.599999999999998
|
1813 |
+
- type: precision_at_10
|
1814 |
+
value: 9.74
|
1815 |
+
- type: precision_at_100
|
1816 |
+
value: 2.284
|
1817 |
+
- type: precision_at_1000
|
1818 |
+
value: 0.381
|
1819 |
+
- type: precision_at_3
|
1820 |
+
value: 16.533
|
1821 |
+
- type: precision_at_5
|
1822 |
+
value: 14.02
|
1823 |
+
- type: recall_at_1
|
1824 |
+
value: 4.153
|
1825 |
+
- type: recall_at_10
|
1826 |
+
value: 19.738
|
1827 |
+
- type: recall_at_100
|
1828 |
+
value: 46.322
|
1829 |
+
- type: recall_at_1000
|
1830 |
+
value: 77.378
|
1831 |
+
- type: recall_at_3
|
1832 |
+
value: 10.048
|
1833 |
+
- type: recall_at_5
|
1834 |
+
value: 14.233
|
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: 85.07097501003639
|
1846 |
+
- type: cos_sim_spearman
|
1847 |
+
value: 81.05827848407056
|
1848 |
+
- type: euclidean_pearson
|
1849 |
+
value: 82.6279003372546
|
1850 |
+
- type: euclidean_spearman
|
1851 |
+
value: 81.00031515279802
|
1852 |
+
- type: manhattan_pearson
|
1853 |
+
value: 82.59338284959495
|
1854 |
+
- type: manhattan_spearman
|
1855 |
+
value: 80.97432711064945
|
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: 86.28991993621685
|
1867 |
+
- type: cos_sim_spearman
|
1868 |
+
value: 78.71828082424351
|
1869 |
+
- type: euclidean_pearson
|
1870 |
+
value: 83.4881331520832
|
1871 |
+
- type: euclidean_spearman
|
1872 |
+
value: 78.51746826842316
|
1873 |
+
- type: manhattan_pearson
|
1874 |
+
value: 83.4109223774324
|
1875 |
+
- type: manhattan_spearman
|
1876 |
+
value: 78.431544382179
|
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: 83.16651661072123
|
1888 |
+
- type: cos_sim_spearman
|
1889 |
+
value: 84.88094386637867
|
1890 |
+
- type: euclidean_pearson
|
1891 |
+
value: 84.3547603585416
|
1892 |
+
- type: euclidean_spearman
|
1893 |
+
value: 84.85148665860193
|
1894 |
+
- type: manhattan_pearson
|
1895 |
+
value: 84.29648369879266
|
1896 |
+
- type: manhattan_spearman
|
1897 |
+
value: 84.76074870571124
|
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: 83.40596254292149
|
1909 |
+
- type: cos_sim_spearman
|
1910 |
+
value: 83.10699573133829
|
1911 |
+
- type: euclidean_pearson
|
1912 |
+
value: 83.22794776876958
|
1913 |
+
- type: euclidean_spearman
|
1914 |
+
value: 83.22583316084712
|
1915 |
+
- type: manhattan_pearson
|
1916 |
+
value: 83.15899233935681
|
1917 |
+
- type: manhattan_spearman
|
1918 |
+
value: 83.17668293648019
|
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: 87.27977121352563
|
1930 |
+
- type: cos_sim_spearman
|
1931 |
+
value: 88.73903130248591
|
1932 |
+
- type: euclidean_pearson
|
1933 |
+
value: 88.30685958438735
|
1934 |
+
- type: euclidean_spearman
|
1935 |
+
value: 88.79755484280406
|
1936 |
+
- type: manhattan_pearson
|
1937 |
+
value: 88.30305607758652
|
1938 |
+
- type: manhattan_spearman
|
1939 |
+
value: 88.80096577072784
|
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: 84.08819031430218
|
1951 |
+
- type: cos_sim_spearman
|
1952 |
+
value: 86.35414445951125
|
1953 |
+
- type: euclidean_pearson
|
1954 |
+
value: 85.4683192388315
|
1955 |
+
- type: euclidean_spearman
|
1956 |
+
value: 86.2079674669473
|
1957 |
+
- type: manhattan_pearson
|
1958 |
+
value: 85.35835702257341
|
1959 |
+
- type: manhattan_spearman
|
1960 |
+
value: 86.08483380002187
|
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: 87.36149449801478
|
1972 |
+
- type: cos_sim_spearman
|
1973 |
+
value: 87.7102980757725
|
1974 |
+
- type: euclidean_pearson
|
1975 |
+
value: 88.16457177837161
|
1976 |
+
- type: euclidean_spearman
|
1977 |
+
value: 87.6598652482716
|
1978 |
+
- type: manhattan_pearson
|
1979 |
+
value: 88.23894728971618
|
1980 |
+
- type: manhattan_spearman
|
1981 |
+
value: 87.74470156709361
|
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: 64.54023758394433
|
1993 |
+
- type: cos_sim_spearman
|
1994 |
+
value: 66.28491960187773
|
1995 |
+
- type: euclidean_pearson
|
1996 |
+
value: 67.0853128483472
|
1997 |
+
- type: euclidean_spearman
|
1998 |
+
value: 66.10307543766307
|
1999 |
+
- type: manhattan_pearson
|
2000 |
+
value: 66.7635365592556
|
2001 |
+
- type: manhattan_spearman
|
2002 |
+
value: 65.76408004780167
|
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: 85.15858398195317
|
2014 |
+
- type: cos_sim_spearman
|
2015 |
+
value: 87.44850004752102
|
2016 |
+
- type: euclidean_pearson
|
2017 |
+
value: 86.60737082550408
|
2018 |
+
- type: euclidean_spearman
|
2019 |
+
value: 87.31591549824242
|
2020 |
+
- type: manhattan_pearson
|
2021 |
+
value: 86.56187011429977
|
2022 |
+
- type: manhattan_spearman
|
2023 |
+
value: 87.23854795795319
|
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: 86.66210488769109
|
2035 |
+
- type: mrr
|
2036 |
+
value: 96.23100664767331
|
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: 56.094
|
2048 |
+
- type: map_at_10
|
2049 |
+
value: 67.486
|
2050 |
+
- type: map_at_100
|
2051 |
+
value: 67.925
|
2052 |
+
- type: map_at_1000
|
2053 |
+
value: 67.949
|
2054 |
+
- type: map_at_3
|
2055 |
+
value: 64.857
|
2056 |
+
- type: map_at_5
|
2057 |
+
value: 66.31
|
2058 |
+
- type: mrr_at_1
|
2059 |
+
value: 58.667
|
2060 |
+
- type: mrr_at_10
|
2061 |
+
value: 68.438
|
2062 |
+
- type: mrr_at_100
|
2063 |
+
value: 68.733
|
2064 |
+
- type: mrr_at_1000
|
2065 |
+
value: 68.757
|
2066 |
+
- type: mrr_at_3
|
2067 |
+
value: 66.389
|
2068 |
+
- type: mrr_at_5
|
2069 |
+
value: 67.456
|
2070 |
+
- type: ndcg_at_1
|
2071 |
+
value: 58.667
|
2072 |
+
- type: ndcg_at_10
|
2073 |
+
value: 72.506
|
2074 |
+
- type: ndcg_at_100
|
2075 |
+
value: 74.27
|
2076 |
+
- type: ndcg_at_1000
|
2077 |
+
value: 74.94800000000001
|
2078 |
+
- type: ndcg_at_3
|
2079 |
+
value: 67.977
|
2080 |
+
- type: ndcg_at_5
|
2081 |
+
value: 70.028
|
2082 |
+
- type: precision_at_1
|
2083 |
+
value: 58.667
|
2084 |
+
- type: precision_at_10
|
2085 |
+
value: 9.767000000000001
|
2086 |
+
- type: precision_at_100
|
2087 |
+
value: 1.073
|
2088 |
+
- type: precision_at_1000
|
2089 |
+
value: 0.11299999999999999
|
2090 |
+
- type: precision_at_3
|
2091 |
+
value: 27.0
|
2092 |
+
- type: precision_at_5
|
2093 |
+
value: 17.666999999999998
|
2094 |
+
- type: recall_at_1
|
2095 |
+
value: 56.094
|
2096 |
+
- type: recall_at_10
|
2097 |
+
value: 86.68900000000001
|
2098 |
+
- type: recall_at_100
|
2099 |
+
value: 94.333
|
2100 |
+
- type: recall_at_1000
|
2101 |
+
value: 99.667
|
2102 |
+
- type: recall_at_3
|
2103 |
+
value: 74.522
|
2104 |
+
- type: recall_at_5
|
2105 |
+
value: 79.611
|
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: 99.83069306930693
|
2117 |
+
- type: cos_sim_ap
|
2118 |
+
value: 95.69184662911199
|
2119 |
+
- type: cos_sim_f1
|
2120 |
+
value: 91.4027149321267
|
2121 |
+
- type: cos_sim_precision
|
2122 |
+
value: 91.91102123356926
|
2123 |
+
- type: cos_sim_recall
|
2124 |
+
value: 90.9
|
2125 |
+
- type: dot_accuracy
|
2126 |
+
value: 99.69405940594059
|
2127 |
+
- type: dot_ap
|
2128 |
+
value: 90.21674151456216
|
2129 |
+
- type: dot_f1
|
2130 |
+
value: 84.4489179667841
|
2131 |
+
- type: dot_precision
|
2132 |
+
value: 85.00506585612969
|
2133 |
+
- type: dot_recall
|
2134 |
+
value: 83.89999999999999
|
2135 |
+
- type: euclidean_accuracy
|
2136 |
+
value: 99.83069306930693
|
2137 |
+
- type: euclidean_ap
|
2138 |
+
value: 95.67760109671087
|
2139 |
+
- type: euclidean_f1
|
2140 |
+
value: 91.19754350051177
|
2141 |
+
- type: euclidean_precision
|
2142 |
+
value: 93.39622641509435
|
2143 |
+
- type: euclidean_recall
|
2144 |
+
value: 89.1
|
2145 |
+
- type: manhattan_accuracy
|
2146 |
+
value: 99.83267326732673
|
2147 |
+
- type: manhattan_ap
|
2148 |
+
value: 95.69771347732625
|
2149 |
+
- type: manhattan_f1
|
2150 |
+
value: 91.32420091324201
|
2151 |
+
- type: manhattan_precision
|
2152 |
+
value: 92.68795056642637
|
2153 |
+
- type: manhattan_recall
|
2154 |
+
value: 90.0
|
2155 |
+
- type: max_accuracy
|
2156 |
+
value: 99.83267326732673
|
2157 |
+
- type: max_ap
|
2158 |
+
value: 95.69771347732625
|
2159 |
+
- type: max_f1
|
2160 |
+
value: 91.4027149321267
|
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: 64.47378332953092
|
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: 33.79602531604151
|
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: 53.80707639107175
|
2194 |
+
- type: mrr
|
2195 |
+
value: 54.64886522790935
|
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: 30.852448373051395
|
2207 |
+
- type: cos_sim_spearman
|
2208 |
+
value: 32.51821499493775
|
2209 |
+
- type: dot_pearson
|
2210 |
+
value: 30.390650062190456
|
2211 |
+
- type: dot_spearman
|
2212 |
+
value: 30.588836159667636
|
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: 0.198
|
2224 |
+
- type: map_at_10
|
2225 |
+
value: 1.51
|
2226 |
+
- type: map_at_100
|
2227 |
+
value: 8.882
|
2228 |
+
- type: map_at_1000
|
2229 |
+
value: 22.181
|
2230 |
+
- type: map_at_3
|
2231 |
+
value: 0.553
|
2232 |
+
- type: map_at_5
|
2233 |
+
value: 0.843
|
2234 |
+
- type: mrr_at_1
|
2235 |
+
value: 74.0
|
2236 |
+
- type: mrr_at_10
|
2237 |
+
value: 84.89999999999999
|
2238 |
+
- type: mrr_at_100
|
2239 |
+
value: 84.89999999999999
|
2240 |
+
- type: mrr_at_1000
|
2241 |
+
value: 84.89999999999999
|
2242 |
+
- type: mrr_at_3
|
2243 |
+
value: 84.0
|
2244 |
+
- type: mrr_at_5
|
2245 |
+
value: 84.89999999999999
|
2246 |
+
- type: ndcg_at_1
|
2247 |
+
value: 68.0
|
2248 |
+
- type: ndcg_at_10
|
2249 |
+
value: 64.792
|
2250 |
+
- type: ndcg_at_100
|
2251 |
+
value: 51.37199999999999
|
2252 |
+
- type: ndcg_at_1000
|
2253 |
+
value: 47.392
|
2254 |
+
- type: ndcg_at_3
|
2255 |
+
value: 68.46900000000001
|
2256 |
+
- type: ndcg_at_5
|
2257 |
+
value: 67.084
|
2258 |
+
- type: precision_at_1
|
2259 |
+
value: 74.0
|
2260 |
+
- type: precision_at_10
|
2261 |
+
value: 69.39999999999999
|
2262 |
+
- type: precision_at_100
|
2263 |
+
value: 53.080000000000005
|
2264 |
+
- type: precision_at_1000
|
2265 |
+
value: 21.258
|
2266 |
+
- type: precision_at_3
|
2267 |
+
value: 76.0
|
2268 |
+
- type: precision_at_5
|
2269 |
+
value: 73.2
|
2270 |
+
- type: recall_at_1
|
2271 |
+
value: 0.198
|
2272 |
+
- type: recall_at_10
|
2273 |
+
value: 1.7950000000000002
|
2274 |
+
- type: recall_at_100
|
2275 |
+
value: 12.626999999999999
|
2276 |
+
- type: recall_at_1000
|
2277 |
+
value: 44.84
|
2278 |
+
- type: recall_at_3
|
2279 |
+
value: 0.611
|
2280 |
+
- type: recall_at_5
|
2281 |
+
value: 0.959
|
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: 1.4949999999999999
|
2293 |
+
- type: map_at_10
|
2294 |
+
value: 8.797
|
2295 |
+
- type: map_at_100
|
2296 |
+
value: 14.889
|
2297 |
+
- type: map_at_1000
|
2298 |
+
value: 16.309
|
2299 |
+
- type: map_at_3
|
2300 |
+
value: 4.389
|
2301 |
+
- type: map_at_5
|
2302 |
+
value: 6.776
|
2303 |
+
- type: mrr_at_1
|
2304 |
+
value: 18.367
|
2305 |
+
- type: mrr_at_10
|
2306 |
+
value: 35.844
|
2307 |
+
- type: mrr_at_100
|
2308 |
+
value: 37.119
|
2309 |
+
- type: mrr_at_1000
|
2310 |
+
value: 37.119
|
2311 |
+
- type: mrr_at_3
|
2312 |
+
value: 30.612000000000002
|
2313 |
+
- type: mrr_at_5
|
2314 |
+
value: 33.163
|
2315 |
+
- type: ndcg_at_1
|
2316 |
+
value: 16.326999999999998
|
2317 |
+
- type: ndcg_at_10
|
2318 |
+
value: 21.9
|
2319 |
+
- type: ndcg_at_100
|
2320 |
+
value: 34.705000000000005
|
2321 |
+
- type: ndcg_at_1000
|
2322 |
+
value: 45.709
|
2323 |
+
- type: ndcg_at_3
|
2324 |
+
value: 22.7
|
2325 |
+
- type: ndcg_at_5
|
2326 |
+
value: 23.197000000000003
|
2327 |
+
- type: precision_at_1
|
2328 |
+
value: 18.367
|
2329 |
+
- type: precision_at_10
|
2330 |
+
value: 21.02
|
2331 |
+
- type: precision_at_100
|
2332 |
+
value: 7.714
|
2333 |
+
- type: precision_at_1000
|
2334 |
+
value: 1.504
|
2335 |
+
- type: precision_at_3
|
2336 |
+
value: 26.531
|
2337 |
+
- type: precision_at_5
|
2338 |
+
value: 26.122
|
2339 |
+
- type: recall_at_1
|
2340 |
+
value: 1.4949999999999999
|
2341 |
+
- type: recall_at_10
|
2342 |
+
value: 15.504000000000001
|
2343 |
+
- type: recall_at_100
|
2344 |
+
value: 47.978
|
2345 |
+
- type: recall_at_1000
|
2346 |
+
value: 81.56
|
2347 |
+
- type: recall_at_3
|
2348 |
+
value: 5.569
|
2349 |
+
- type: recall_at_5
|
2350 |
+
value: 9.821
|
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: 72.99279999999999
|
2362 |
+
- type: ap
|
2363 |
+
value: 15.459189680101492
|
2364 |
+
- type: f1
|
2365 |
+
value: 56.33023271441895
|
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: 63.070175438596486
|
2377 |
+
- type: f1
|
2378 |
+
value: 63.28070758709465
|
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: 50.076231309703054
|
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: 87.21463908922931
|
2401 |
+
- type: cos_sim_ap
|
2402 |
+
value: 77.67287017966282
|
2403 |
+
- type: cos_sim_f1
|
2404 |
+
value: 70.34412955465588
|
2405 |
+
- type: cos_sim_precision
|
2406 |
+
value: 67.57413709285368
|
2407 |
+
- type: cos_sim_recall
|
2408 |
+
value: 73.35092348284961
|
2409 |
+
- type: dot_accuracy
|
2410 |
+
value: 85.04500208618943
|
2411 |
+
- type: dot_ap
|
2412 |
+
value: 70.4075203869744
|
2413 |
+
- type: dot_f1
|
2414 |
+
value: 66.18172537008678
|
2415 |
+
- type: dot_precision
|
2416 |
+
value: 64.08798813643104
|
2417 |
+
- type: dot_recall
|
2418 |
+
value: 68.41688654353561
|
2419 |
+
- type: euclidean_accuracy
|
2420 |
+
value: 87.17887584192646
|
2421 |
+
- type: euclidean_ap
|
2422 |
+
value: 77.5774128274464
|
2423 |
+
- type: euclidean_f1
|
2424 |
+
value: 70.09307972480777
|
2425 |
+
- type: euclidean_precision
|
2426 |
+
value: 71.70852884349986
|
2427 |
+
- type: euclidean_recall
|
2428 |
+
value: 68.54881266490766
|
2429 |
+
- type: manhattan_accuracy
|
2430 |
+
value: 87.28020504261787
|
2431 |
+
- type: manhattan_ap
|
2432 |
+
value: 77.57835820297892
|
2433 |
+
- type: manhattan_f1
|
2434 |
+
value: 70.23063591521131
|
2435 |
+
- type: manhattan_precision
|
2436 |
+
value: 70.97817299919159
|
2437 |
+
- type: manhattan_recall
|
2438 |
+
value: 69.49868073878628
|
2439 |
+
- type: max_accuracy
|
2440 |
+
value: 87.28020504261787
|
2441 |
+
- type: max_ap
|
2442 |
+
value: 77.67287017966282
|
2443 |
+
- type: max_f1
|
2444 |
+
value: 70.34412955465588
|
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: 88.96650754841464
|
2456 |
+
- type: cos_sim_ap
|
2457 |
+
value: 86.00185968965064
|
2458 |
+
- type: cos_sim_f1
|
2459 |
+
value: 77.95861256351718
|
2460 |
+
- type: cos_sim_precision
|
2461 |
+
value: 74.70712773465067
|
2462 |
+
- type: cos_sim_recall
|
2463 |
+
value: 81.50600554357868
|
2464 |
+
- type: dot_accuracy
|
2465 |
+
value: 87.36950362867233
|
2466 |
+
- type: dot_ap
|
2467 |
+
value: 82.22071181147555
|
2468 |
+
- type: dot_f1
|
2469 |
+
value: 74.85680716698488
|
2470 |
+
- type: dot_precision
|
2471 |
+
value: 71.54688377316114
|
2472 |
+
- type: dot_recall
|
2473 |
+
value: 78.48783492454572
|
2474 |
+
- type: euclidean_accuracy
|
2475 |
+
value: 88.99561454573679
|
2476 |
+
- type: euclidean_ap
|
2477 |
+
value: 86.15882097229648
|
2478 |
+
- type: euclidean_f1
|
2479 |
+
value: 78.18463125322332
|
2480 |
+
- type: euclidean_precision
|
2481 |
+
value: 74.95408956067241
|
2482 |
+
- type: euclidean_recall
|
2483 |
+
value: 81.70619032953496
|
2484 |
+
- type: manhattan_accuracy
|
2485 |
+
value: 88.96650754841464
|
2486 |
+
- type: manhattan_ap
|
2487 |
+
value: 86.13133111232099
|
2488 |
+
- type: manhattan_f1
|
2489 |
+
value: 78.10771470160115
|
2490 |
+
- type: manhattan_precision
|
2491 |
+
value: 74.05465084184377
|
2492 |
+
- type: manhattan_recall
|
2493 |
+
value: 82.63012011087157
|
2494 |
+
- type: max_accuracy
|
2495 |
+
value: 88.99561454573679
|
2496 |
+
- type: max_ap
|
2497 |
+
value: 86.15882097229648
|
2498 |
+
- type: max_f1
|
2499 |
+
value: 78.18463125322332
|
2500 |
+
---
|
2501 |
+
|
2502 |
+
# djuna/stella-base-en-v2-Q5_K_M-GGUF
|
2503 |
+
This model was converted to GGUF format from [`infgrad/stella-base-en-v2`](https://huggingface.co/infgrad/stella-base-en-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/infgrad/stella-base-en-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 djuna/stella-base-en-v2-Q5_K_M-GGUF --hf-file stella-base-en-v2-q5_k_m.gguf -p "The meaning to life and the universe is"
|
2518 |
+
```
|
2519 |
+
|
2520 |
+
### Server:
|
2521 |
+
```bash
|
2522 |
+
llama-server --hf-repo djuna/stella-base-en-v2-Q5_K_M-GGUF --hf-file stella-base-en-v2-q5_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 djuna/stella-base-en-v2-Q5_K_M-GGUF --hf-file stella-base-en-v2-q5_k_m.gguf -p "The meaning to life and the universe is"
|
2540 |
+
```
|
2541 |
+
or
|
2542 |
+
```
|
2543 |
+
./llama-server --hf-repo djuna/stella-base-en-v2-Q5_K_M-GGUF --hf-file stella-base-en-v2-q5_k_m.gguf -c 2048
|
2544 |
+
```
|