Commit
•
e91216a
1
Parent(s):
07ed167
Add MTEB
Browse files
README.md
CHANGED
@@ -4,6 +4,2398 @@ tags:
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4 |
- sentence-transformers
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5 |
- feature-extraction
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6 |
- sentence-similarity
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7 |
---
|
8 |
|
9 |
# SGPT-2.7B-weightedmean-msmarco-specb-bitfit
|
|
|
4 |
- sentence-transformers
|
5 |
- feature-extraction
|
6 |
- sentence-similarity
|
7 |
+
- mteb
|
8 |
+
model-index:
|
9 |
+
- name: SGPT-2.7B-weightedmean-msmarco-specb-bitfit
|
10 |
+
results:
|
11 |
+
- task:
|
12 |
+
type: Classification
|
13 |
+
dataset:
|
14 |
+
type: mteb/amazon_counterfactual
|
15 |
+
name: MTEB AmazonCounterfactualClassification (en)
|
16 |
+
metrics:
|
17 |
+
- type: accuracy
|
18 |
+
value: 67.56716417910448
|
19 |
+
- type: ap
|
20 |
+
value: 30.75574629595259
|
21 |
+
- type: f1
|
22 |
+
value: 61.805121301858655
|
23 |
+
- task:
|
24 |
+
type: Classification
|
25 |
+
dataset:
|
26 |
+
type: mteb/amazon_polarity
|
27 |
+
name: MTEB AmazonPolarityClassification
|
28 |
+
metrics:
|
29 |
+
- type: accuracy
|
30 |
+
value: 71.439575
|
31 |
+
- type: ap
|
32 |
+
value: 65.91341330532453
|
33 |
+
- type: f1
|
34 |
+
value: 70.90561852619555
|
35 |
+
- task:
|
36 |
+
type: Classification
|
37 |
+
dataset:
|
38 |
+
type: mteb/amazon_reviews_multi
|
39 |
+
name: MTEB AmazonReviewsClassification (en)
|
40 |
+
metrics:
|
41 |
+
- type: accuracy
|
42 |
+
value: 35.748000000000005
|
43 |
+
- type: f1
|
44 |
+
value: 35.48576287186347
|
45 |
+
- task:
|
46 |
+
type: Retrieval
|
47 |
+
dataset:
|
48 |
+
type: arguana
|
49 |
+
name: MTEB ArguAna
|
50 |
+
metrics:
|
51 |
+
- type: map_at_1
|
52 |
+
value: 25.96
|
53 |
+
- type: map_at_10
|
54 |
+
value: 41.619
|
55 |
+
- type: map_at_100
|
56 |
+
value: 42.673
|
57 |
+
- type: map_at_1000
|
58 |
+
value: 42.684
|
59 |
+
- type: map_at_3
|
60 |
+
value: 36.569
|
61 |
+
- type: map_at_5
|
62 |
+
value: 39.397
|
63 |
+
- type: mrr_at_1
|
64 |
+
value: 26.316
|
65 |
+
- type: mrr_at_10
|
66 |
+
value: 41.772
|
67 |
+
- type: mrr_at_100
|
68 |
+
value: 42.82
|
69 |
+
- type: mrr_at_1000
|
70 |
+
value: 42.83
|
71 |
+
- type: mrr_at_3
|
72 |
+
value: 36.724000000000004
|
73 |
+
- type: mrr_at_5
|
74 |
+
value: 39.528999999999996
|
75 |
+
- type: ndcg_at_1
|
76 |
+
value: 25.96
|
77 |
+
- type: ndcg_at_10
|
78 |
+
value: 50.491
|
79 |
+
- type: ndcg_at_100
|
80 |
+
value: 54.864999999999995
|
81 |
+
- type: ndcg_at_1000
|
82 |
+
value: 55.10699999999999
|
83 |
+
- type: ndcg_at_3
|
84 |
+
value: 40.053
|
85 |
+
- type: ndcg_at_5
|
86 |
+
value: 45.134
|
87 |
+
- type: precision_at_1
|
88 |
+
value: 25.96
|
89 |
+
- type: precision_at_10
|
90 |
+
value: 7.8950000000000005
|
91 |
+
- type: precision_at_100
|
92 |
+
value: 0.9780000000000001
|
93 |
+
- type: precision_at_1000
|
94 |
+
value: 0.1
|
95 |
+
- type: precision_at_3
|
96 |
+
value: 16.714000000000002
|
97 |
+
- type: precision_at_5
|
98 |
+
value: 12.489
|
99 |
+
- type: recall_at_1
|
100 |
+
value: 25.96
|
101 |
+
- type: recall_at_10
|
102 |
+
value: 78.947
|
103 |
+
- type: recall_at_100
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|
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|
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|
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|
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|
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123 |
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|
125 |
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126 |
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|
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|
130 |
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131 |
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name: MTEB AskUbuntuDupQuestions
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|
133 |
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448 |
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type: BeIR/cqadupstack
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449 |
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514 |
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type: BeIR/cqadupstack
|
515 |
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dataset:
|
580 |
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type: BeIR/cqadupstack
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581 |
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name: MTEB CQADupstackProgrammersRetrieval
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dataset:
|
646 |
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type: BeIR/cqadupstack
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dataset:
|
712 |
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type: BeIR/cqadupstack
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name: MTEB CQADupstackStatsRetrieval
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metrics:
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715 |
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717 |
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731 |
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775 |
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dataset:
|
778 |
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type: BeIR/cqadupstack
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|
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dataset:
|
844 |
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type: BeIR/cqadupstack
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845 |
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name: MTEB CQADupstackUnixRetrieval
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849 |
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901 |
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905 |
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906 |
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907 |
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|
908 |
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909 |
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dataset:
|
910 |
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type: BeIR/cqadupstack
|
911 |
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name: MTEB CQADupstackWebmastersRetrieval
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913 |
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914 |
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953 |
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954 |
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955 |
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957 |
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959 |
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963 |
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965 |
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966 |
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967 |
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969 |
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971 |
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972 |
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value: 38.673
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973 |
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|
974 |
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975 |
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dataset:
|
976 |
+
type: BeIR/cqadupstack
|
977 |
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name: MTEB CQADupstackWordpressRetrieval
|
978 |
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metrics:
|
979 |
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980 |
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value: 18.555
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981 |
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995 |
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998 |
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999 |
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1002 |
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1007 |
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1008 |
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1020 |
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1022 |
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1040 |
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1041 |
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dataset:
|
1042 |
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type: climate-fever
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1043 |
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name: MTEB ClimateFEVER
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1044 |
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1045 |
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1047 |
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1059 |
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1060 |
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1062 |
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1064 |
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1065 |
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1071 |
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1072 |
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1073 |
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1074 |
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1077 |
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1085 |
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1095 |
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value: 57.375
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1099 |
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1100 |
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value: 76.79
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1101 |
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1102 |
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1103 |
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1104 |
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value: 26.215
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1105 |
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- task:
|
1106 |
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1107 |
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dataset:
|
1108 |
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type: dbpedia-entity
|
1109 |
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name: MTEB DBPedia
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1110 |
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metrics:
|
1111 |
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|
1112 |
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value: 8.246
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1113 |
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1114 |
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1115 |
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1117 |
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1118 |
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1119 |
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1120 |
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1121 |
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1122 |
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1123 |
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1125 |
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1126 |
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1127 |
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1128 |
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1129 |
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1130 |
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1131 |
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1132 |
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1133 |
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1134 |
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1135 |
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1137 |
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1138 |
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1139 |
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1140 |
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1141 |
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1142 |
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1143 |
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1144 |
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1145 |
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1146 |
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1147 |
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1148 |
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1149 |
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1150 |
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1151 |
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1152 |
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value: 7.969999999999999
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1153 |
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1154 |
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1155 |
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1156 |
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1157 |
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1158 |
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1159 |
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1160 |
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1161 |
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1162 |
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1164 |
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1165 |
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1166 |
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1167 |
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1170 |
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1171 |
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|
1172 |
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type: Classification
|
1173 |
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dataset:
|
1174 |
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type: mteb/emotion
|
1175 |
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name: MTEB EmotionClassification
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1176 |
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metrics:
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1177 |
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|
1178 |
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value: 49.214999999999996
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1179 |
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1180 |
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1181 |
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|
1182 |
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1183 |
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dataset:
|
1184 |
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type: fever
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1185 |
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name: MTEB FEVER
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1186 |
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1187 |
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1188 |
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1189 |
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1190 |
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1191 |
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1192 |
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1193 |
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1194 |
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1195 |
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1196 |
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1197 |
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1198 |
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1199 |
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1200 |
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1201 |
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1202 |
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1203 |
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1204 |
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1205 |
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1206 |
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1207 |
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1208 |
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1209 |
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1216 |
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1221 |
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1224 |
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1228 |
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1231 |
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1232 |
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1233 |
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1234 |
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1236 |
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1237 |
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1238 |
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1240 |
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1243 |
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1244 |
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1245 |
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1246 |
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value: 80.17
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1247 |
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|
1248 |
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1249 |
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dataset:
|
1250 |
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type: fiqa
|
1251 |
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name: MTEB FiQA2018
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1252 |
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metrics:
|
1253 |
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1254 |
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1255 |
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1256 |
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1257 |
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1258 |
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1259 |
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1260 |
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1261 |
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1262 |
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1263 |
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1265 |
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1267 |
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1268 |
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1269 |
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1270 |
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1271 |
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1273 |
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1274 |
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1275 |
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1276 |
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1277 |
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1279 |
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1281 |
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1282 |
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1293 |
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1297 |
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1299 |
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1300 |
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1301 |
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1302 |
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1303 |
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1304 |
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1305 |
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1306 |
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1307 |
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1308 |
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1309 |
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1311 |
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1312 |
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1313 |
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|
1314 |
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1315 |
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dataset:
|
1316 |
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type: hotpotqa
|
1317 |
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name: MTEB HotpotQA
|
1318 |
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1319 |
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1320 |
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1321 |
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1331 |
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1333 |
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1334 |
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1335 |
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1336 |
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1337 |
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1338 |
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1339 |
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1340 |
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1341 |
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1342 |
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1347 |
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1349 |
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1351 |
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1352 |
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1353 |
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1355 |
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1356 |
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1357 |
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1358 |
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1359 |
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1360 |
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1361 |
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1362 |
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|
1363 |
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value: 14.026
|
1779 |
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- type: precision_at_1
|
1780 |
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value: 19.900000000000002
|
1781 |
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- type: precision_at_10
|
1782 |
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value: 8.450000000000001
|
1783 |
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- type: precision_at_100
|
1784 |
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value: 1.872
|
1785 |
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- type: precision_at_1000
|
1786 |
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value: 0.313
|
1787 |
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- type: precision_at_3
|
1788 |
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value: 14.667
|
1789 |
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- type: precision_at_5
|
1790 |
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value: 12.32
|
1791 |
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- type: recall_at_1
|
1792 |
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value: 4.053
|
1793 |
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- type: recall_at_10
|
1794 |
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value: 17.169999999999998
|
1795 |
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- type: recall_at_100
|
1796 |
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value: 38.025
|
1797 |
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- type: recall_at_1000
|
1798 |
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value: 63.571999999999996
|
1799 |
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- type: recall_at_3
|
1800 |
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value: 8.903
|
1801 |
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- type: recall_at_5
|
1802 |
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value: 12.477
|
1803 |
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- task:
|
1804 |
+
type: STS
|
1805 |
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dataset:
|
1806 |
+
type: mteb/sickr-sts
|
1807 |
+
name: MTEB SICK-R
|
1808 |
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metrics:
|
1809 |
+
- type: cos_sim_pearson
|
1810 |
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value: 77.7548748519677
|
1811 |
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- type: cos_sim_spearman
|
1812 |
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value: 68.19926431966059
|
1813 |
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- type: euclidean_pearson
|
1814 |
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value: 71.69016204991725
|
1815 |
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- type: euclidean_spearman
|
1816 |
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value: 66.98099673026834
|
1817 |
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- type: manhattan_pearson
|
1818 |
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value: 71.62994072488664
|
1819 |
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- type: manhattan_spearman
|
1820 |
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value: 67.03435950744577
|
1821 |
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- task:
|
1822 |
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type: STS
|
1823 |
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dataset:
|
1824 |
+
type: mteb/sts12-sts
|
1825 |
+
name: MTEB STS12
|
1826 |
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metrics:
|
1827 |
+
- type: cos_sim_pearson
|
1828 |
+
value: 75.91051402657887
|
1829 |
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- type: cos_sim_spearman
|
1830 |
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value: 66.99390786191645
|
1831 |
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- type: euclidean_pearson
|
1832 |
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value: 71.54128036454578
|
1833 |
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- type: euclidean_spearman
|
1834 |
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value: 69.25605675649068
|
1835 |
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- type: manhattan_pearson
|
1836 |
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value: 71.60981030780171
|
1837 |
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- type: manhattan_spearman
|
1838 |
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value: 69.27513670128046
|
1839 |
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- task:
|
1840 |
+
type: STS
|
1841 |
+
dataset:
|
1842 |
+
type: mteb/sts13-sts
|
1843 |
+
name: MTEB STS13
|
1844 |
+
metrics:
|
1845 |
+
- type: cos_sim_pearson
|
1846 |
+
value: 77.23835466417793
|
1847 |
+
- type: cos_sim_spearman
|
1848 |
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value: 77.57623085766706
|
1849 |
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- type: euclidean_pearson
|
1850 |
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value: 77.5090992200725
|
1851 |
+
- type: euclidean_spearman
|
1852 |
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value: 77.88601688144924
|
1853 |
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- type: manhattan_pearson
|
1854 |
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value: 77.39045060647423
|
1855 |
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- type: manhattan_spearman
|
1856 |
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value: 77.77552718279098
|
1857 |
+
- task:
|
1858 |
+
type: STS
|
1859 |
+
dataset:
|
1860 |
+
type: mteb/sts14-sts
|
1861 |
+
name: MTEB STS14
|
1862 |
+
metrics:
|
1863 |
+
- type: cos_sim_pearson
|
1864 |
+
value: 77.91692485139602
|
1865 |
+
- type: cos_sim_spearman
|
1866 |
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value: 72.78258293483495
|
1867 |
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- type: euclidean_pearson
|
1868 |
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value: 74.64773017077789
|
1869 |
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- type: euclidean_spearman
|
1870 |
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value: 71.81662299104619
|
1871 |
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- type: manhattan_pearson
|
1872 |
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value: 74.71043337995533
|
1873 |
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- type: manhattan_spearman
|
1874 |
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value: 71.83960860845646
|
1875 |
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- task:
|
1876 |
+
type: STS
|
1877 |
+
dataset:
|
1878 |
+
type: mteb/sts15-sts
|
1879 |
+
name: MTEB STS15
|
1880 |
+
metrics:
|
1881 |
+
- type: cos_sim_pearson
|
1882 |
+
value: 82.13422113617578
|
1883 |
+
- type: cos_sim_spearman
|
1884 |
+
value: 82.61707296911949
|
1885 |
+
- type: euclidean_pearson
|
1886 |
+
value: 81.42487480400861
|
1887 |
+
- type: euclidean_spearman
|
1888 |
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value: 82.17970991273835
|
1889 |
+
- type: manhattan_pearson
|
1890 |
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value: 81.41985055477845
|
1891 |
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- type: manhattan_spearman
|
1892 |
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value: 82.15823204362937
|
1893 |
+
- task:
|
1894 |
+
type: STS
|
1895 |
+
dataset:
|
1896 |
+
type: mteb/sts16-sts
|
1897 |
+
name: MTEB STS16
|
1898 |
+
metrics:
|
1899 |
+
- type: cos_sim_pearson
|
1900 |
+
value: 79.07989542843826
|
1901 |
+
- type: cos_sim_spearman
|
1902 |
+
value: 80.09839524406284
|
1903 |
+
- type: euclidean_pearson
|
1904 |
+
value: 76.43186028364195
|
1905 |
+
- type: euclidean_spearman
|
1906 |
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value: 76.76720323266471
|
1907 |
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- type: manhattan_pearson
|
1908 |
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value: 76.4674747409161
|
1909 |
+
- type: manhattan_spearman
|
1910 |
+
value: 76.81797407068667
|
1911 |
+
- task:
|
1912 |
+
type: STS
|
1913 |
+
dataset:
|
1914 |
+
type: mteb/sts17-crosslingual-sts
|
1915 |
+
name: MTEB STS17 (en-en)
|
1916 |
+
metrics:
|
1917 |
+
- type: cos_sim_pearson
|
1918 |
+
value: 87.0420983224933
|
1919 |
+
- type: cos_sim_spearman
|
1920 |
+
value: 87.25017540413702
|
1921 |
+
- type: euclidean_pearson
|
1922 |
+
value: 84.56384596473421
|
1923 |
+
- type: euclidean_spearman
|
1924 |
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value: 84.72557417564886
|
1925 |
+
- type: manhattan_pearson
|
1926 |
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value: 84.7329954474549
|
1927 |
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- type: manhattan_spearman
|
1928 |
+
value: 84.75071371008909
|
1929 |
+
- task:
|
1930 |
+
type: STS
|
1931 |
+
dataset:
|
1932 |
+
type: mteb/sts22-crosslingual-sts
|
1933 |
+
name: MTEB STS22 (en)
|
1934 |
+
metrics:
|
1935 |
+
- type: cos_sim_pearson
|
1936 |
+
value: 68.47031320016424
|
1937 |
+
- type: cos_sim_spearman
|
1938 |
+
value: 68.7486910762485
|
1939 |
+
- type: euclidean_pearson
|
1940 |
+
value: 71.30330985913915
|
1941 |
+
- type: euclidean_spearman
|
1942 |
+
value: 71.59666258520735
|
1943 |
+
- type: manhattan_pearson
|
1944 |
+
value: 71.4423884279027
|
1945 |
+
- type: manhattan_spearman
|
1946 |
+
value: 71.67460706861044
|
1947 |
+
- task:
|
1948 |
+
type: STS
|
1949 |
+
dataset:
|
1950 |
+
type: mteb/stsbenchmark-sts
|
1951 |
+
name: MTEB STSBenchmark
|
1952 |
+
metrics:
|
1953 |
+
- type: cos_sim_pearson
|
1954 |
+
value: 80.79514366062675
|
1955 |
+
- type: cos_sim_spearman
|
1956 |
+
value: 79.20585637461048
|
1957 |
+
- type: euclidean_pearson
|
1958 |
+
value: 78.6591557395699
|
1959 |
+
- type: euclidean_spearman
|
1960 |
+
value: 77.86455794285718
|
1961 |
+
- type: manhattan_pearson
|
1962 |
+
value: 78.67754806486865
|
1963 |
+
- type: manhattan_spearman
|
1964 |
+
value: 77.88178687200732
|
1965 |
+
- task:
|
1966 |
+
type: Reranking
|
1967 |
+
dataset:
|
1968 |
+
type: mteb/scidocs-reranking
|
1969 |
+
name: MTEB SciDocsRR
|
1970 |
+
metrics:
|
1971 |
+
- type: map
|
1972 |
+
value: 77.71580844366375
|
1973 |
+
- type: mrr
|
1974 |
+
value: 93.04215845882513
|
1975 |
+
- task:
|
1976 |
+
type: Retrieval
|
1977 |
+
dataset:
|
1978 |
+
type: scifact
|
1979 |
+
name: MTEB SciFact
|
1980 |
+
metrics:
|
1981 |
+
- type: map_at_1
|
1982 |
+
value: 56.39999999999999
|
1983 |
+
- type: map_at_10
|
1984 |
+
value: 65.701
|
1985 |
+
- type: map_at_100
|
1986 |
+
value: 66.32000000000001
|
1987 |
+
- type: map_at_1000
|
1988 |
+
value: 66.34100000000001
|
1989 |
+
- type: map_at_3
|
1990 |
+
value: 62.641999999999996
|
1991 |
+
- type: map_at_5
|
1992 |
+
value: 64.342
|
1993 |
+
- type: mrr_at_1
|
1994 |
+
value: 58.667
|
1995 |
+
- type: mrr_at_10
|
1996 |
+
value: 66.45299999999999
|
1997 |
+
- type: mrr_at_100
|
1998 |
+
value: 66.967
|
1999 |
+
- type: mrr_at_1000
|
2000 |
+
value: 66.988
|
2001 |
+
- type: mrr_at_3
|
2002 |
+
value: 64.11099999999999
|
2003 |
+
- type: mrr_at_5
|
2004 |
+
value: 65.411
|
2005 |
+
- type: ndcg_at_1
|
2006 |
+
value: 58.667
|
2007 |
+
- type: ndcg_at_10
|
2008 |
+
value: 70.165
|
2009 |
+
- type: ndcg_at_100
|
2010 |
+
value: 72.938
|
2011 |
+
- type: ndcg_at_1000
|
2012 |
+
value: 73.456
|
2013 |
+
- type: ndcg_at_3
|
2014 |
+
value: 64.79
|
2015 |
+
- type: ndcg_at_5
|
2016 |
+
value: 67.28
|
2017 |
+
- type: precision_at_1
|
2018 |
+
value: 58.667
|
2019 |
+
- type: precision_at_10
|
2020 |
+
value: 9.4
|
2021 |
+
- type: precision_at_100
|
2022 |
+
value: 1.087
|
2023 |
+
- type: precision_at_1000
|
2024 |
+
value: 0.11299999999999999
|
2025 |
+
- type: precision_at_3
|
2026 |
+
value: 24.889
|
2027 |
+
- type: precision_at_5
|
2028 |
+
value: 16.667
|
2029 |
+
- type: recall_at_1
|
2030 |
+
value: 56.39999999999999
|
2031 |
+
- type: recall_at_10
|
2032 |
+
value: 83.122
|
2033 |
+
- type: recall_at_100
|
2034 |
+
value: 95.667
|
2035 |
+
- type: recall_at_1000
|
2036 |
+
value: 99.667
|
2037 |
+
- type: recall_at_3
|
2038 |
+
value: 68.378
|
2039 |
+
- type: recall_at_5
|
2040 |
+
value: 74.68299999999999
|
2041 |
+
- task:
|
2042 |
+
type: PairClassification
|
2043 |
+
dataset:
|
2044 |
+
type: mteb/sprintduplicatequestions-pairclassification
|
2045 |
+
name: MTEB SprintDuplicateQuestions
|
2046 |
+
metrics:
|
2047 |
+
- type: cos_sim_accuracy
|
2048 |
+
value: 99.76831683168317
|
2049 |
+
- type: cos_sim_ap
|
2050 |
+
value: 93.47124923047998
|
2051 |
+
- type: cos_sim_f1
|
2052 |
+
value: 88.06122448979592
|
2053 |
+
- type: cos_sim_precision
|
2054 |
+
value: 89.89583333333333
|
2055 |
+
- type: cos_sim_recall
|
2056 |
+
value: 86.3
|
2057 |
+
- type: dot_accuracy
|
2058 |
+
value: 99.57326732673268
|
2059 |
+
- type: dot_ap
|
2060 |
+
value: 84.06577868167207
|
2061 |
+
- type: dot_f1
|
2062 |
+
value: 77.82629791363416
|
2063 |
+
- type: dot_precision
|
2064 |
+
value: 75.58906691800189
|
2065 |
+
- type: dot_recall
|
2066 |
+
value: 80.2
|
2067 |
+
- type: euclidean_accuracy
|
2068 |
+
value: 99.74257425742574
|
2069 |
+
- type: euclidean_ap
|
2070 |
+
value: 92.1904681653555
|
2071 |
+
- type: euclidean_f1
|
2072 |
+
value: 86.74821610601427
|
2073 |
+
- type: euclidean_precision
|
2074 |
+
value: 88.46153846153845
|
2075 |
+
- type: euclidean_recall
|
2076 |
+
value: 85.1
|
2077 |
+
- type: manhattan_accuracy
|
2078 |
+
value: 99.74554455445545
|
2079 |
+
- type: manhattan_ap
|
2080 |
+
value: 92.4337790809948
|
2081 |
+
- type: manhattan_f1
|
2082 |
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value: 86.86765457332653
|
2083 |
+
- type: manhattan_precision
|
2084 |
+
value: 88.81922675026124
|
2085 |
+
- type: manhattan_recall
|
2086 |
+
value: 85.0
|
2087 |
+
- type: max_accuracy
|
2088 |
+
value: 99.76831683168317
|
2089 |
+
- type: max_ap
|
2090 |
+
value: 93.47124923047998
|
2091 |
+
- type: max_f1
|
2092 |
+
value: 88.06122448979592
|
2093 |
+
- task:
|
2094 |
+
type: Clustering
|
2095 |
+
dataset:
|
2096 |
+
type: mteb/stackexchange-clustering
|
2097 |
+
name: MTEB StackExchangeClustering
|
2098 |
+
metrics:
|
2099 |
+
- type: v_measure
|
2100 |
+
value: 59.194098673976484
|
2101 |
+
- task:
|
2102 |
+
type: Clustering
|
2103 |
+
dataset:
|
2104 |
+
type: mteb/stackexchange-clustering-p2p
|
2105 |
+
name: MTEB StackExchangeClusteringP2P
|
2106 |
+
metrics:
|
2107 |
+
- type: v_measure
|
2108 |
+
value: 32.5744032578115
|
2109 |
+
- task:
|
2110 |
+
type: Reranking
|
2111 |
+
dataset:
|
2112 |
+
type: mteb/stackoverflowdupquestions-reranking
|
2113 |
+
name: MTEB StackOverflowDupQuestions
|
2114 |
+
metrics:
|
2115 |
+
- type: map
|
2116 |
+
value: 49.61186384154483
|
2117 |
+
- type: mrr
|
2118 |
+
value: 50.55424253034547
|
2119 |
+
- task:
|
2120 |
+
type: Summarization
|
2121 |
+
dataset:
|
2122 |
+
type: mteb/summeval
|
2123 |
+
name: MTEB SummEval
|
2124 |
+
metrics:
|
2125 |
+
- type: cos_sim_pearson
|
2126 |
+
value: 26.047224542079068
|
2127 |
+
- type: cos_sim_spearman
|
2128 |
+
value: 27.870478281195467
|
2129 |
+
- type: dot_pearson
|
2130 |
+
value: 25.182420685701217
|
2131 |
+
- type: dot_spearman
|
2132 |
+
value: 25.116243491984985
|
2133 |
+
- task:
|
2134 |
+
type: Retrieval
|
2135 |
+
dataset:
|
2136 |
+
type: trec-covid
|
2137 |
+
name: MTEB TRECCOVID
|
2138 |
+
metrics:
|
2139 |
+
- type: map_at_1
|
2140 |
+
value: 0.22300000000000003
|
2141 |
+
- type: map_at_10
|
2142 |
+
value: 1.762
|
2143 |
+
- type: map_at_100
|
2144 |
+
value: 9.984
|
2145 |
+
- type: map_at_1000
|
2146 |
+
value: 24.265
|
2147 |
+
- type: map_at_3
|
2148 |
+
value: 0.631
|
2149 |
+
- type: map_at_5
|
2150 |
+
value: 0.9950000000000001
|
2151 |
+
- type: mrr_at_1
|
2152 |
+
value: 88.0
|
2153 |
+
- type: mrr_at_10
|
2154 |
+
value: 92.833
|
2155 |
+
- type: mrr_at_100
|
2156 |
+
value: 92.833
|
2157 |
+
- type: mrr_at_1000
|
2158 |
+
value: 92.833
|
2159 |
+
- type: mrr_at_3
|
2160 |
+
value: 92.333
|
2161 |
+
- type: mrr_at_5
|
2162 |
+
value: 92.833
|
2163 |
+
- type: ndcg_at_1
|
2164 |
+
value: 83.0
|
2165 |
+
- type: ndcg_at_10
|
2166 |
+
value: 75.17
|
2167 |
+
- type: ndcg_at_100
|
2168 |
+
value: 55.432
|
2169 |
+
- type: ndcg_at_1000
|
2170 |
+
value: 49.482
|
2171 |
+
- type: ndcg_at_3
|
2172 |
+
value: 82.184
|
2173 |
+
- type: ndcg_at_5
|
2174 |
+
value: 79.712
|
2175 |
+
- type: precision_at_1
|
2176 |
+
value: 88.0
|
2177 |
+
- type: precision_at_10
|
2178 |
+
value: 78.60000000000001
|
2179 |
+
- type: precision_at_100
|
2180 |
+
value: 56.56
|
2181 |
+
- type: precision_at_1000
|
2182 |
+
value: 22.334
|
2183 |
+
- type: precision_at_3
|
2184 |
+
value: 86.667
|
2185 |
+
- type: precision_at_5
|
2186 |
+
value: 83.6
|
2187 |
+
- type: recall_at_1
|
2188 |
+
value: 0.22300000000000003
|
2189 |
+
- type: recall_at_10
|
2190 |
+
value: 1.9879999999999998
|
2191 |
+
- type: recall_at_100
|
2192 |
+
value: 13.300999999999998
|
2193 |
+
- type: recall_at_1000
|
2194 |
+
value: 46.587
|
2195 |
+
- type: recall_at_3
|
2196 |
+
value: 0.6629999999999999
|
2197 |
+
- type: recall_at_5
|
2198 |
+
value: 1.079
|
2199 |
+
- task:
|
2200 |
+
type: Retrieval
|
2201 |
+
dataset:
|
2202 |
+
type: webis-touche2020
|
2203 |
+
name: MTEB Touche2020
|
2204 |
+
metrics:
|
2205 |
+
- type: map_at_1
|
2206 |
+
value: 3.047
|
2207 |
+
- type: map_at_10
|
2208 |
+
value: 8.792
|
2209 |
+
- type: map_at_100
|
2210 |
+
value: 14.631
|
2211 |
+
- type: map_at_1000
|
2212 |
+
value: 16.127
|
2213 |
+
- type: map_at_3
|
2214 |
+
value: 4.673
|
2215 |
+
- type: map_at_5
|
2216 |
+
value: 5.897
|
2217 |
+
- type: mrr_at_1
|
2218 |
+
value: 38.775999999999996
|
2219 |
+
- type: mrr_at_10
|
2220 |
+
value: 49.271
|
2221 |
+
- type: mrr_at_100
|
2222 |
+
value: 50.181
|
2223 |
+
- type: mrr_at_1000
|
2224 |
+
value: 50.2
|
2225 |
+
- type: mrr_at_3
|
2226 |
+
value: 44.558
|
2227 |
+
- type: mrr_at_5
|
2228 |
+
value: 47.925000000000004
|
2229 |
+
- type: ndcg_at_1
|
2230 |
+
value: 35.714
|
2231 |
+
- type: ndcg_at_10
|
2232 |
+
value: 23.44
|
2233 |
+
- type: ndcg_at_100
|
2234 |
+
value: 35.345
|
2235 |
+
- type: ndcg_at_1000
|
2236 |
+
value: 46.495
|
2237 |
+
- type: ndcg_at_3
|
2238 |
+
value: 26.146
|
2239 |
+
- type: ndcg_at_5
|
2240 |
+
value: 24.878
|
2241 |
+
- type: precision_at_1
|
2242 |
+
value: 38.775999999999996
|
2243 |
+
- type: precision_at_10
|
2244 |
+
value: 20.816000000000003
|
2245 |
+
- type: precision_at_100
|
2246 |
+
value: 7.428999999999999
|
2247 |
+
- type: precision_at_1000
|
2248 |
+
value: 1.494
|
2249 |
+
- type: precision_at_3
|
2250 |
+
value: 25.85
|
2251 |
+
- type: precision_at_5
|
2252 |
+
value: 24.082
|
2253 |
+
- type: recall_at_1
|
2254 |
+
value: 3.047
|
2255 |
+
- type: recall_at_10
|
2256 |
+
value: 14.975
|
2257 |
+
- type: recall_at_100
|
2258 |
+
value: 45.943
|
2259 |
+
- type: recall_at_1000
|
2260 |
+
value: 80.31099999999999
|
2261 |
+
- type: recall_at_3
|
2262 |
+
value: 5.478000000000001
|
2263 |
+
- type: recall_at_5
|
2264 |
+
value: 8.294
|
2265 |
+
- task:
|
2266 |
+
type: Classification
|
2267 |
+
dataset:
|
2268 |
+
type: mteb/toxic_conversations_50k
|
2269 |
+
name: MTEB ToxicConversationsClassification
|
2270 |
+
metrics:
|
2271 |
+
- type: accuracy
|
2272 |
+
value: 68.84080000000002
|
2273 |
+
- type: ap
|
2274 |
+
value: 13.135219251019848
|
2275 |
+
- type: f1
|
2276 |
+
value: 52.849999421995506
|
2277 |
+
- task:
|
2278 |
+
type: Classification
|
2279 |
+
dataset:
|
2280 |
+
type: mteb/tweet_sentiment_extraction
|
2281 |
+
name: MTEB TweetSentimentExtractionClassification
|
2282 |
+
metrics:
|
2283 |
+
- type: accuracy
|
2284 |
+
value: 56.68647425014149
|
2285 |
+
- type: f1
|
2286 |
+
value: 56.97981427365949
|
2287 |
+
- task:
|
2288 |
+
type: Clustering
|
2289 |
+
dataset:
|
2290 |
+
type: mteb/twentynewsgroups-clustering
|
2291 |
+
name: MTEB TwentyNewsgroupsClustering
|
2292 |
+
metrics:
|
2293 |
+
- type: v_measure
|
2294 |
+
value: 40.8911707239219
|
2295 |
+
- task:
|
2296 |
+
type: PairClassification
|
2297 |
+
dataset:
|
2298 |
+
type: mteb/twittersemeval2015-pairclassification
|
2299 |
+
name: MTEB TwitterSemEval2015
|
2300 |
+
metrics:
|
2301 |
+
- type: cos_sim_accuracy
|
2302 |
+
value: 83.04226023722954
|
2303 |
+
- type: cos_sim_ap
|
2304 |
+
value: 63.681339908301325
|
2305 |
+
- type: cos_sim_f1
|
2306 |
+
value: 60.349184470480125
|
2307 |
+
- type: cos_sim_precision
|
2308 |
+
value: 53.437754271765655
|
2309 |
+
- type: cos_sim_recall
|
2310 |
+
value: 69.31398416886545
|
2311 |
+
- type: dot_accuracy
|
2312 |
+
value: 81.46271681468677
|
2313 |
+
- type: dot_ap
|
2314 |
+
value: 57.78072296265885
|
2315 |
+
- type: dot_f1
|
2316 |
+
value: 56.28769265132901
|
2317 |
+
- type: dot_precision
|
2318 |
+
value: 48.7993803253292
|
2319 |
+
- type: dot_recall
|
2320 |
+
value: 66.49076517150397
|
2321 |
+
- type: euclidean_accuracy
|
2322 |
+
value: 82.16606067830959
|
2323 |
+
- type: euclidean_ap
|
2324 |
+
value: 59.974530371203514
|
2325 |
+
- type: euclidean_f1
|
2326 |
+
value: 56.856023506366306
|
2327 |
+
- type: euclidean_precision
|
2328 |
+
value: 53.037916857012334
|
2329 |
+
- type: euclidean_recall
|
2330 |
+
value: 61.2664907651715
|
2331 |
+
- type: manhattan_accuracy
|
2332 |
+
value: 82.16606067830959
|
2333 |
+
- type: manhattan_ap
|
2334 |
+
value: 59.98962379571767
|
2335 |
+
- type: manhattan_f1
|
2336 |
+
value: 56.98153158451947
|
2337 |
+
- type: manhattan_precision
|
2338 |
+
value: 51.41158989598811
|
2339 |
+
- type: manhattan_recall
|
2340 |
+
value: 63.90501319261214
|
2341 |
+
- type: max_accuracy
|
2342 |
+
value: 83.04226023722954
|
2343 |
+
- type: max_ap
|
2344 |
+
value: 63.681339908301325
|
2345 |
+
- type: max_f1
|
2346 |
+
value: 60.349184470480125
|
2347 |
+
- task:
|
2348 |
+
type: PairClassification
|
2349 |
+
dataset:
|
2350 |
+
type: mteb/twitterurlcorpus-pairclassification
|
2351 |
+
name: MTEB TwitterURLCorpus
|
2352 |
+
metrics:
|
2353 |
+
- type: cos_sim_accuracy
|
2354 |
+
value: 88.56871191834517
|
2355 |
+
- type: cos_sim_ap
|
2356 |
+
value: 84.80240716354544
|
2357 |
+
- type: cos_sim_f1
|
2358 |
+
value: 77.07765285922385
|
2359 |
+
- type: cos_sim_precision
|
2360 |
+
value: 74.84947406601378
|
2361 |
+
- type: cos_sim_recall
|
2362 |
+
value: 79.44256236526024
|
2363 |
+
- type: dot_accuracy
|
2364 |
+
value: 86.00923662048356
|
2365 |
+
- type: dot_ap
|
2366 |
+
value: 78.6556459012073
|
2367 |
+
- type: dot_f1
|
2368 |
+
value: 72.7583749109052
|
2369 |
+
- type: dot_precision
|
2370 |
+
value: 67.72823779193206
|
2371 |
+
- type: dot_recall
|
2372 |
+
value: 78.59562673236834
|
2373 |
+
- type: euclidean_accuracy
|
2374 |
+
value: 87.84103698529127
|
2375 |
+
- type: euclidean_ap
|
2376 |
+
value: 83.50424424952834
|
2377 |
+
- type: euclidean_f1
|
2378 |
+
value: 75.74496544549307
|
2379 |
+
- type: euclidean_precision
|
2380 |
+
value: 73.19402556369381
|
2381 |
+
- type: euclidean_recall
|
2382 |
+
value: 78.48013550970127
|
2383 |
+
- type: manhattan_accuracy
|
2384 |
+
value: 87.9225365777933
|
2385 |
+
- type: manhattan_ap
|
2386 |
+
value: 83.49479248597825
|
2387 |
+
- type: manhattan_f1
|
2388 |
+
value: 75.67748162447101
|
2389 |
+
- type: manhattan_precision
|
2390 |
+
value: 73.06810035842294
|
2391 |
+
- type: manhattan_recall
|
2392 |
+
value: 78.48013550970127
|
2393 |
+
- type: max_accuracy
|
2394 |
+
value: 88.56871191834517
|
2395 |
+
- type: max_ap
|
2396 |
+
value: 84.80240716354544
|
2397 |
+
- type: max_f1
|
2398 |
+
value: 77.07765285922385
|
2399 |
---
|
2400 |
|
2401 |
# SGPT-2.7B-weightedmean-msmarco-specb-bitfit
|