library_name
pipeline_tag
tags
model-index
license
language
new_version
sentence-transformers
sentence-similarity
feature-extraction
sentence-similarity
mteb
transformers
transformers.js
name
results
epoch_0_model
task
dataset
metrics
type
name
config
split
revision
mteb/amazon_counterfactual
MTEB AmazonCounterfactualClassification (en)
en
test
e8379541af4e31359cca9fbcf4b00f2671dba205
type
value
accuracy
76.8507462686567
type
value
ap
40.592189159090495
type
value
f1
71.01634655512476
task
dataset
metrics
type
name
config
split
revision
mteb/amazon_polarity
MTEB AmazonPolarityClassification
default
test
e2d317d38cd51312af73b3d32a06d1a08b442046
type
value
accuracy
91.51892500000001
type
value
ap
88.50346762975335
type
value
f1
91.50342077459624
task
dataset
metrics
type
name
config
split
revision
mteb/amazon_reviews_multi
MTEB AmazonReviewsClassification (en)
en
test
1399c76144fd37290681b995c656ef9b2e06e26d
type
value
accuracy
47.364
type
value
f1
46.72708080922794
task
dataset
metrics
type
name
config
split
revision
arguana
MTEB ArguAna
default
test
None
type
value
map_at_1
25.178
type
value
map_at_10
40.244
type
value
map_at_100
41.321999999999996
type
value
map_at_1000
41.331
type
value
map_at_3
35.016999999999996
type
value
map_at_5
37.99
type
value
mrr_at_1
25.605
type
value
mrr_at_10
40.422000000000004
type
value
mrr_at_100
41.507
type
value
mrr_at_1000
41.516
type
value
mrr_at_3
35.23
type
value
mrr_at_5
38.15
type
value
ndcg_at_1
25.178
type
value
ndcg_at_10
49.258
type
value
ndcg_at_100
53.776
type
value
ndcg_at_1000
53.995000000000005
type
value
ndcg_at_3
38.429
type
value
ndcg_at_5
43.803
type
value
precision_at_1
25.178
type
value
precision_at_10
7.831
type
value
precision_at_100
0.979
type
value
precision_at_1000
0.1
type
value
precision_at_3
16.121
type
value
precision_at_5
12.29
type
value
recall_at_1
25.178
type
value
recall_at_10
78.307
type
value
recall_at_100
97.866
type
value
recall_at_1000
99.57300000000001
type
value
recall_at_3
48.364000000000004
type
value
recall_at_5
61.451
task
dataset
metrics
type
name
config
split
revision
mteb/arxiv-clustering-p2p
MTEB ArxivClusteringP2P
default
test
a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
type
value
v_measure
45.93034494751465
task
dataset
metrics
type
name
config
split
revision
mteb/arxiv-clustering-s2s
MTEB ArxivClusteringS2S
default
test
f910caf1a6075f7329cdf8c1a6135696f37dbd53
type
value
v_measure
36.64579480054327
task
dataset
metrics
type
name
config
split
revision
mteb/askubuntudupquestions-reranking
MTEB AskUbuntuDupQuestions
default
test
2000358ca161889fa9c082cb41daa8dcfb161a54
type
value
map
60.601310529222054
type
value
mrr
75.04484896451656
task
dataset
metrics
type
name
config
split
revision
mteb/biosses-sts
MTEB BIOSSES
default
test
d3fb88f8f02e40887cd149695127462bbcf29b4a
type
value
cos_sim_pearson
88.57797718095814
type
value
cos_sim_spearman
86.47064499110101
type
value
euclidean_pearson
87.4559602783142
type
value
euclidean_spearman
86.47064499110101
type
value
manhattan_pearson
87.7232764230245
type
value
manhattan_spearman
86.91222131777742
task
dataset
metrics
type
name
config
split
revision
mteb/banking77
MTEB Banking77Classification
default
test
0fd18e25b25c072e09e0d92ab615fda904d66300
type
value
accuracy
84.5422077922078
type
value
f1
84.47657456950589
task
dataset
metrics
type
name
config
split
revision
mteb/biorxiv-clustering-p2p
MTEB BiorxivClusteringP2P
default
test
65b79d1d13f80053f67aca9498d9402c2d9f1f40
type
value
v_measure
38.48953561974464
task
dataset
metrics
type
name
config
split
revision
mteb/biorxiv-clustering-s2s
MTEB BiorxivClusteringS2S
default
test
258694dd0231531bc1fd9de6ceb52a0853c6d908
type
value
v_measure
32.75995857510105
task
dataset
metrics
type
name
config
split
revision
BeIR/cqadupstack
MTEB CQADupstackAndroidRetrieval
default
test
None
type
value
map_at_1
30.008000000000003
type
value
map_at_10
39.51
type
value
map_at_100
40.841
type
value
map_at_1000
40.973
type
value
map_at_3
36.248999999999995
type
value
map_at_5
38.096999999999994
type
value
mrr_at_1
36.481
type
value
mrr_at_10
44.818000000000005
type
value
mrr_at_100
45.64
type
value
mrr_at_1000
45.687
type
value
mrr_at_3
42.036
type
value
mrr_at_5
43.782
type
value
ndcg_at_1
36.481
type
value
ndcg_at_10
45.152
type
value
ndcg_at_100
50.449
type
value
ndcg_at_1000
52.76499999999999
type
value
ndcg_at_3
40.161
type
value
ndcg_at_5
42.577999999999996
type
value
precision_at_1
36.481
type
value
precision_at_10
8.369
type
value
precision_at_100
1.373
type
value
precision_at_1000
0.186
type
value
precision_at_3
18.693
type
value
precision_at_5
13.533999999999999
type
value
recall_at_1
30.008000000000003
type
value
recall_at_10
56.108999999999995
type
value
recall_at_100
78.55499999999999
type
value
recall_at_1000
93.659
type
value
recall_at_3
41.754999999999995
type
value
recall_at_5
48.296
task
dataset
metrics
type
name
config
split
revision
BeIR/cqadupstack
MTEB CQADupstackEnglishRetrieval
default
test
None
type
value
map_at_1
30.262
type
value
map_at_10
40.139
type
value
map_at_100
41.394
type
value
map_at_1000
41.526
type
value
map_at_3
37.155
type
value
map_at_5
38.785
type
value
mrr_at_1
38.153
type
value
mrr_at_10
46.369
type
value
mrr_at_100
47.072
type
value
mrr_at_1000
47.111999999999995
type
value
mrr_at_3
44.268
type
value
mrr_at_5
45.389
type
value
ndcg_at_1
38.153
type
value
ndcg_at_10
45.925
type
value
ndcg_at_100
50.394000000000005
type
value
ndcg_at_1000
52.37500000000001
type
value
ndcg_at_3
41.754000000000005
type
value
ndcg_at_5
43.574
type
value
precision_at_1
38.153
type
value
precision_at_10
8.796
type
value
precision_at_100
1.432
type
value
precision_at_1000
0.189
type
value
precision_at_3
20.318
type
value
precision_at_5
14.395
type
value
recall_at_1
30.262
type
value
recall_at_10
55.72200000000001
type
value
recall_at_100
74.97500000000001
type
value
recall_at_1000
87.342
type
value
recall_at_3
43.129
type
value
recall_at_5
48.336
task
dataset
metrics
type
name
config
split
revision
BeIR/cqadupstack
MTEB CQADupstackGamingRetrieval
default
test
None
type
value
map_at_1
39.951
type
value
map_at_10
51.248000000000005
type
value
map_at_100
52.188
type
value
map_at_1000
52.247
type
value
map_at_3
48.211
type
value
map_at_5
49.797000000000004
type
value
mrr_at_1
45.329
type
value
mrr_at_10
54.749
type
value
mrr_at_100
55.367999999999995
type
value
mrr_at_1000
55.400000000000006
type
value
mrr_at_3
52.382
type
value
mrr_at_5
53.649
type
value
ndcg_at_1
45.329
type
value
ndcg_at_10
56.847
type
value
ndcg_at_100
60.738
type
value
ndcg_at_1000
61.976
type
value
ndcg_at_3
51.59
type
value
ndcg_at_5
53.915
type
value
precision_at_1
45.329
type
value
precision_at_10
8.959
type
value
precision_at_100
1.187
type
value
precision_at_1000
0.134
type
value
precision_at_3
22.612
type
value
precision_at_5
15.273
type
value
recall_at_1
39.951
type
value
recall_at_10
70.053
type
value
recall_at_100
86.996
type
value
recall_at_1000
95.707
type
value
recall_at_3
56.032000000000004
type
value
recall_at_5
61.629999999999995
task
dataset
metrics
type
name
config
split
revision
BeIR/cqadupstack
MTEB CQADupstackGisRetrieval
default
test
None
type
value
map_at_1
25.566
type
value
map_at_10
33.207
type
value
map_at_100
34.166000000000004
type
value
map_at_1000
34.245
type
value
map_at_3
30.94
type
value
map_at_5
32.01
type
value
mrr_at_1
27.345000000000002
type
value
mrr_at_10
35.193000000000005
type
value
mrr_at_100
35.965
type
value
mrr_at_1000
36.028999999999996
type
value
mrr_at_3
32.806000000000004
type
value
mrr_at_5
34.021
type
value
ndcg_at_1
27.345000000000002
type
value
ndcg_at_10
37.891999999999996
type
value
ndcg_at_100
42.664
type
value
ndcg_at_1000
44.757000000000005
type
value
ndcg_at_3
33.123000000000005
type
value
ndcg_at_5
35.035
type
value
precision_at_1
27.345000000000002
type
value
precision_at_10
5.763
type
value
precision_at_100
0.859
type
value
precision_at_1000
0.108
type
value
precision_at_3
13.71
type
value
precision_at_5
9.401
type
value
recall_at_1
25.566
type
value
recall_at_10
50.563
type
value
recall_at_100
72.86399999999999
type
value
recall_at_1000
88.68599999999999
type
value
recall_at_3
37.43
type
value
recall_at_5
41.894999999999996
task
dataset
metrics
type
name
config
split
revision
BeIR/cqadupstack
MTEB CQADupstackMathematicaRetrieval
default
test
None
type
value
map_at_1
16.663
type
value
map_at_10
23.552
type
value
map_at_100
24.538
type
value
map_at_1000
24.661
type
value
map_at_3
21.085
type
value
map_at_5
22.391
type
value
mrr_at_1
20.025000000000002
type
value
mrr_at_10
27.643
type
value
mrr_at_100
28.499999999999996
type
value
mrr_at_1000
28.582
type
value
mrr_at_3
25.083
type
value
mrr_at_5
26.544
type
value
ndcg_at_1
20.025000000000002
type
value
ndcg_at_10
28.272000000000002
type
value
ndcg_at_100
33.353
type
value
ndcg_at_1000
36.454
type
value
ndcg_at_3
23.579
type
value
ndcg_at_5
25.685000000000002
type
value
precision_at_1
20.025000000000002
type
value
precision_at_10
5.187
type
value
precision_at_100
0.897
type
value
precision_at_1000
0.13
type
value
precision_at_3
10.987
type
value
precision_at_5
8.06
type
value
recall_at_1
16.663
type
value
recall_at_10
38.808
type
value
recall_at_100
61.305
type
value
recall_at_1000
83.571
type
value
recall_at_3
25.907999999999998
type
value
recall_at_5
31.214
task
dataset
metrics
type
name
config
split
revision
BeIR/cqadupstack
MTEB CQADupstackPhysicsRetrieval
default
test
None
type
value
map_at_1
27.695999999999998
type
value
map_at_10
37.018
type
value
map_at_100
38.263000000000005
type
value
map_at_1000
38.371
type
value
map_at_3
34.226
type
value
map_at_5
35.809999999999995
type
value
mrr_at_1
32.916000000000004
type
value
mrr_at_10
42.067
type
value
mrr_at_100
42.925000000000004
type
value
mrr_at_1000
42.978
type
value
mrr_at_3
39.637
type
value
mrr_at_5
41.134
type
value
ndcg_at_1
32.916000000000004
type
value
ndcg_at_10
42.539
type
value
ndcg_at_100
47.873
type
value
ndcg_at_1000
50.08200000000001
type
value
ndcg_at_3
37.852999999999994
type
value
ndcg_at_5
40.201
type
value
precision_at_1
32.916000000000004
type
value
precision_at_10
7.5840000000000005
type
value
precision_at_100
1.199
type
value
precision_at_1000
0.155
type
value
precision_at_3
17.485
type
value
precision_at_5
12.512
type
value
recall_at_1
27.695999999999998
type
value
recall_at_10
53.638
type
value
recall_at_100
76.116
type
value
recall_at_1000
91.069
type
value
recall_at_3
41.13
type
value
recall_at_5
46.872
task
dataset
metrics
type
name
config
split
revision
BeIR/cqadupstack
MTEB CQADupstackProgrammersRetrieval
default
test
None
type
value
map_at_1
24.108
type
value
map_at_10
33.372
type
value
map_at_100
34.656
type
value
map_at_1000
34.768
type
value
map_at_3
30.830999999999996
type
value
map_at_5
32.204
type
value
mrr_at_1
29.110000000000003
type
value
mrr_at_10
37.979
type
value
mrr_at_100
38.933
type
value
mrr_at_1000
38.988
type
value
mrr_at_3
35.731
type
value
mrr_at_5
36.963
type
value
ndcg_at_1
29.110000000000003
type
value
ndcg_at_10
38.635000000000005
type
value
ndcg_at_100
44.324999999999996
type
value
ndcg_at_1000
46.747
type
value
ndcg_at_3
34.37
type
value
ndcg_at_5
36.228
type
value
precision_at_1
29.110000000000003
type
value
precision_at_10
6.963
type
value
precision_at_100
1.146
type
value
precision_at_1000
0.152
type
value
precision_at_3
16.400000000000002
type
value
precision_at_5
11.552999999999999
type
value
recall_at_1
24.108
type
value
recall_at_10
49.597
type
value
recall_at_100
73.88900000000001
type
value
recall_at_1000
90.62400000000001
type
value
recall_at_3
37.662
type
value
recall_at_5
42.565
task
dataset
metrics
type
name
config
split
revision
BeIR/cqadupstack
MTEB CQADupstackRetrieval
default
test
None
type
value
map_at_1
25.00791666666667
type
value
map_at_10
33.287749999999996
type
value
map_at_100
34.41141666666667
type
value
map_at_1000
34.52583333333333
type
value
map_at_3
30.734416666666668
type
value
map_at_5
32.137166666666666
type
value
mrr_at_1
29.305666666666664
type
value
mrr_at_10
37.22966666666666
type
value
mrr_at_100
38.066583333333334
type
value
mrr_at_1000
38.12616666666667
type
value
mrr_at_3
34.92275
type
value
mrr_at_5
36.23333333333334
type
value
ndcg_at_1
29.305666666666664
type
value
ndcg_at_10
38.25533333333333
type
value
ndcg_at_100
43.25266666666666
type
value
ndcg_at_1000
45.63583333333334
type
value
ndcg_at_3
33.777166666666666
type
value
ndcg_at_5
35.85
type
value
precision_at_1
29.305666666666664
type
value
precision_at_10
6.596416666666667
type
value
precision_at_100
1.0784166666666668
type
value
precision_at_1000
0.14666666666666664
type
value
precision_at_3
15.31075
type
value
precision_at_5
10.830916666666667
type
value
recall_at_1
25.00791666666667
type
value
recall_at_10
49.10933333333333
type
value
recall_at_100
71.09216666666667
type
value
recall_at_1000
87.77725000000001
type
value
recall_at_3
36.660916666666665
type
value
recall_at_5
41.94149999999999
task
dataset
metrics
type
name
config
split
revision
BeIR/cqadupstack
MTEB CQADupstackStatsRetrieval
default
test
None
type
value
map_at_1
23.521
type
value
map_at_10
30.043
type
value
map_at_100
30.936000000000003
type
value
map_at_1000
31.022
type
value
map_at_3
27.926000000000002
type
value
map_at_5
29.076999999999998
type
value
mrr_at_1
26.227
type
value
mrr_at_10
32.822
type
value
mrr_at_100
33.61
type
value
mrr_at_1000
33.672000000000004
type
value
mrr_at_3
30.776999999999997
type
value
mrr_at_5
31.866
type
value
ndcg_at_1
26.227
type
value
ndcg_at_10
34.041
type
value
ndcg_at_100
38.394
type
value
ndcg_at_1000
40.732
type
value
ndcg_at_3
30.037999999999997
type
value
ndcg_at_5
31.845000000000002
type
value
precision_at_1
26.227
type
value
precision_at_10
5.244999999999999
type
value
precision_at_100
0.808
type
value
precision_at_1000
0.107
type
value
precision_at_3
12.679000000000002
type
value
precision_at_5
8.773
type
value
recall_at_1
23.521
type
value
recall_at_10
43.633
type
value
recall_at_100
63.126000000000005
type
value
recall_at_1000
80.765
type
value
recall_at_3
32.614
type
value
recall_at_5
37.15
task
dataset
metrics
type
name
config
split
revision
BeIR/cqadupstack
MTEB CQADupstackTexRetrieval
default
test
None
type
value
map_at_1
16.236
type
value
map_at_10
22.898
type
value
map_at_100
23.878
type
value
map_at_1000
24.009
type
value
map_at_3
20.87
type
value
map_at_5
22.025
type
value
mrr_at_1
19.339000000000002
type
value
mrr_at_10
26.382
type
value
mrr_at_100
27.245
type
value
mrr_at_1000
27.33
type
value
mrr_at_3
24.386
type
value
mrr_at_5
25.496000000000002
type
value
ndcg_at_1
19.339000000000002
type
value
ndcg_at_10
27.139999999999997
type
value
ndcg_at_100
31.944
type
value
ndcg_at_1000
35.077999999999996
type
value
ndcg_at_3
23.424
type
value
ndcg_at_5
25.188
type
value
precision_at_1
19.339000000000002
type
value
precision_at_10
4.8309999999999995
type
value
precision_at_100
0.845
type
value
precision_at_1000
0.128
type
value
precision_at_3
10.874
type
value
precision_at_5
7.825
type
value
recall_at_1
16.236
type
value
recall_at_10
36.513
type
value
recall_at_100
57.999
type
value
recall_at_1000
80.512
type
value
recall_at_3
26.179999999999996
type
value
recall_at_5
30.712
task
dataset
metrics
type
name
config
split
revision
BeIR/cqadupstack
MTEB CQADupstackUnixRetrieval
default
test
None
type
value
map_at_1
24.11
type
value
map_at_10
31.566
type
value
map_at_100
32.647
type
value
map_at_1000
32.753
type
value
map_at_3
29.24
type
value
map_at_5
30.564999999999998
type
value
mrr_at_1
28.265
type
value
mrr_at_10
35.504000000000005
type
value
mrr_at_100
36.436
type
value
mrr_at_1000
36.503
type
value
mrr_at_3
33.349000000000004
type
value
mrr_at_5
34.622
type
value
ndcg_at_1
28.265
type
value
ndcg_at_10
36.192
type
value
ndcg_at_100
41.388000000000005
type
value
ndcg_at_1000
43.948
type
value
ndcg_at_3
31.959
type
value
ndcg_at_5
33.998
type
value
precision_at_1
28.265
type
value
precision_at_10
5.989
type
value
precision_at_100
0.9650000000000001
type
value
precision_at_1000
0.13
type
value
precision_at_3
14.335
type
value
precision_at_5
10.112
type
value
recall_at_1
24.11
type
value
recall_at_10
46.418
type
value
recall_at_100
69.314
type
value
recall_at_1000
87.397
type
value
recall_at_3
34.724
type
value
recall_at_5
39.925
task
dataset
metrics
type
name
config
split
revision
BeIR/cqadupstack
MTEB CQADupstackWebmastersRetrieval
default
test
None
type
value
map_at_1
22.091
type
value
map_at_10
29.948999999999998
type
value
map_at_100
31.502000000000002
type
value
map_at_1000
31.713
type
value
map_at_3
27.464
type
value
map_at_5
28.968
type
value
mrr_at_1
26.482
type
value
mrr_at_10
34.009
type
value
mrr_at_100
35.081
type
value
mrr_at_1000
35.138000000000005
type
value
mrr_at_3
31.785000000000004
type
value
mrr_at_5
33.178999999999995
type
value
ndcg_at_1
26.482
type
value
ndcg_at_10
35.008
type
value
ndcg_at_100
41.272999999999996
type
value
ndcg_at_1000
43.972
type
value
ndcg_at_3
30.804
type
value
ndcg_at_5
33.046
type
value
precision_at_1
26.482
type
value
precision_at_10
6.462
type
value
precision_at_100
1.431
type
value
precision_at_1000
0.22899999999999998
type
value
precision_at_3
14.360999999999999
type
value
precision_at_5
10.474
type
value
recall_at_1
22.091
type
value
recall_at_10
45.125
type
value
recall_at_100
72.313
type
value
recall_at_1000
89.503
type
value
recall_at_3
33.158
type
value
recall_at_5
39.086999999999996
task
dataset
metrics
type
name
config
split
revision
BeIR/cqadupstack
MTEB CQADupstackWordpressRetrieval
default
test
None
type
value
map_at_1
19.883
type
value
map_at_10
26.951000000000004
type
value
map_at_100
27.927999999999997
type
value
map_at_1000
28.022000000000002
type
value
map_at_3
24.616
type
value
map_at_5
25.917
type
value
mrr_at_1
21.996
type
value
mrr_at_10
29.221000000000004
type
value
mrr_at_100
30.024
type
value
mrr_at_1000
30.095
type
value
mrr_at_3
26.833000000000002
type
value
mrr_at_5
28.155
type
value
ndcg_at_1
21.996
type
value
ndcg_at_10
31.421
type
value
ndcg_at_100
36.237
type
value
ndcg_at_1000
38.744
type
value
ndcg_at_3
26.671
type
value
ndcg_at_5
28.907
type
value
precision_at_1
21.996
type
value
precision_at_10
5.009
type
value
precision_at_100
0.799
type
value
precision_at_1000
0.11199999999999999
type
value
precision_at_3
11.275
type
value
precision_at_5
8.059
type
value
recall_at_1
19.883
type
value
recall_at_10
43.132999999999996
type
value
recall_at_100
65.654
type
value
recall_at_1000
84.492
type
value
recall_at_3
30.209000000000003
type
value
recall_at_5
35.616
task
dataset
metrics
type
name
config
split
revision
climate-fever
MTEB ClimateFEVER
default
test
None
type
value
map_at_1
17.756
type
value
map_at_10
30.378
type
value
map_at_100
32.537
type
value
map_at_1000
32.717
type
value
map_at_3
25.599
type
value
map_at_5
28.372999999999998
type
value
mrr_at_1
41.303
type
value
mrr_at_10
53.483999999999995
type
value
mrr_at_100
54.106
type
value
mrr_at_1000
54.127
type
value
mrr_at_3
50.315
type
value
mrr_at_5
52.396
type
value
ndcg_at_1
41.303
type
value
ndcg_at_10
40.503
type
value
ndcg_at_100
47.821000000000005
type
value
ndcg_at_1000
50.788
type
value
ndcg_at_3
34.364
type
value
ndcg_at_5
36.818
type
value
precision_at_1
41.303
type
value
precision_at_10
12.463000000000001
type
value
precision_at_100
2.037
type
value
precision_at_1000
0.26
type
value
precision_at_3
25.798
type
value
precision_at_5
19.896
type
value
recall_at_1
17.756
type
value
recall_at_10
46.102
type
value
recall_at_100
70.819
type
value
recall_at_1000
87.21799999999999
type
value
recall_at_3
30.646
type
value
recall_at_5
38.022
task
dataset
metrics
type
name
config
split
revision
dbpedia-entity
MTEB DBPedia
default
test
None
type
value
map_at_1
9.033
type
value
map_at_10
20.584
type
value
map_at_100
29.518
type
value
map_at_1000
31.186000000000003
type
value
map_at_3
14.468
type
value
map_at_5
17.177
type
value
mrr_at_1
69.75
type
value
mrr_at_10
77.025
type
value
mrr_at_100
77.36699999999999
type
value
mrr_at_1000
77.373
type
value
mrr_at_3
75.583
type
value
mrr_at_5
76.396
type
value
ndcg_at_1
58.5
type
value
ndcg_at_10
45.033
type
value
ndcg_at_100
49.071
type
value
ndcg_at_1000
56.056
type
value
ndcg_at_3
49.936
type
value
ndcg_at_5
47.471999999999994
type
value
precision_at_1
69.75
type
value
precision_at_10
35.775
type
value
precision_at_100
11.594999999999999
type
value
precision_at_1000
2.062
type
value
precision_at_3
52.5
type
value
precision_at_5
45.300000000000004
type
value
recall_at_1
9.033
type
value
recall_at_10
26.596999999999998
type
value
recall_at_100
54.607000000000006
type
value
recall_at_1000
76.961
type
value
recall_at_3
15.754999999999999
type
value
recall_at_5
20.033
task
dataset
metrics
type
name
config
split
revision
mteb/emotion
MTEB EmotionClassification
default
test
4f58c6b202a23cf9a4da393831edf4f9183cad37
type
value
accuracy
48.345000000000006
type
value
f1
43.4514918068706
task
dataset
metrics
type
name
config
split
revision
fever
MTEB FEVER
default
test
None
type
value
map_at_1
71.29100000000001
type
value
map_at_10
81.059
type
value
map_at_100
81.341
type
value
map_at_1000
81.355
type
value
map_at_3
79.74799999999999
type
value
map_at_5
80.612
type
value
mrr_at_1
76.40299999999999
type
value
mrr_at_10
84.615
type
value
mrr_at_100
84.745
type
value
mrr_at_1000
84.748
type
value
mrr_at_3
83.776
type
value
mrr_at_5
84.343
type
value
ndcg_at_1
76.40299999999999
type
value
ndcg_at_10
84.981
type
value
ndcg_at_100
86.00999999999999
type
value
ndcg_at_1000
86.252
type
value
ndcg_at_3
82.97
type
value
ndcg_at_5
84.152
type
value
precision_at_1
76.40299999999999
type
value
precision_at_10
10.446
type
value
precision_at_100
1.1199999999999999
type
value
precision_at_1000
0.116
type
value
precision_at_3
32.147999999999996
type
value
precision_at_5
20.135
type
value
recall_at_1
71.29100000000001
type
value
recall_at_10
93.232
type
value
recall_at_100
97.363
type
value
recall_at_1000
98.905
type
value
recall_at_3
87.893
type
value
recall_at_5
90.804
task
dataset
metrics
type
name
config
split
revision
fiqa
MTEB FiQA2018
default
test
None
type
value
map_at_1
18.667
type
value
map_at_10
30.853
type
value
map_at_100
32.494
type
value
map_at_1000
32.677
type
value
map_at_3
26.91
type
value
map_at_5
29.099000000000004
type
value
mrr_at_1
37.191
type
value
mrr_at_10
46.171
type
value
mrr_at_100
47.056
type
value
mrr_at_1000
47.099000000000004
type
value
mrr_at_3
44.059
type
value
mrr_at_5
45.147
type
value
ndcg_at_1
37.191
type
value
ndcg_at_10
38.437
type
value
ndcg_at_100
44.62
type
value
ndcg_at_1000
47.795
type
value
ndcg_at_3
35.003
type
value
ndcg_at_5
36.006
type
value
precision_at_1
37.191
type
value
precision_at_10
10.586
type
value
precision_at_100
1.688
type
value
precision_at_1000
0.22699999999999998
type
value
precision_at_3
23.302
type
value
precision_at_5
17.006
type
value
recall_at_1
18.667
type
value
recall_at_10
45.367000000000004
type
value
recall_at_100
68.207
type
value
recall_at_1000
87.072
type
value
recall_at_3
32.129000000000005
type
value
recall_at_5
37.719
task
dataset
metrics
type
name
config
split
revision
hotpotqa
MTEB HotpotQA
default
test
None
type
value
map_at_1
39.494
type
value
map_at_10
66.223
type
value
map_at_100
67.062
type
value
map_at_1000
67.11500000000001
type
value
map_at_3
62.867
type
value
map_at_5
64.994
type
value
mrr_at_1
78.987
type
value
mrr_at_10
84.585
type
value
mrr_at_100
84.773
type
value
mrr_at_1000
84.77900000000001
type
value
mrr_at_3
83.592
type
value
mrr_at_5
84.235
type
value
ndcg_at_1
78.987
type
value
ndcg_at_10
73.64
type
value
ndcg_at_100
76.519
type
value
ndcg_at_1000
77.51
type
value
ndcg_at_3
68.893
type
value
ndcg_at_5
71.585
type
value
precision_at_1
78.987
type
value
precision_at_10
15.529000000000002
type
value
precision_at_100
1.7770000000000001
type
value
precision_at_1000
0.191
type
value
precision_at_3
44.808
type
value
precision_at_5
29.006999999999998
type
value
recall_at_1
39.494
type
value
recall_at_10
77.643
type
value
recall_at_100
88.825
type
value
recall_at_1000
95.321
type
value
recall_at_3
67.211
type
value
recall_at_5
72.519
task
dataset
metrics
type
name
config
split
revision
mteb/imdb
MTEB ImdbClassification
default
test
3d86128a09e091d6018b6d26cad27f2739fc2db7
type
value
accuracy
85.55959999999999
type
value
ap
80.7246500384617
type
value
f1
85.52336485065454
task
dataset
metrics
type
name
config
split
revision
msmarco
MTEB MSMARCO
default
dev
None
type
value
map_at_1
23.631
type
value
map_at_10
36.264
type
value
map_at_100
37.428
type
value
map_at_1000
37.472
type
value
map_at_3
32.537
type
value
map_at_5
34.746
type
value
mrr_at_1
24.312
type
value
mrr_at_10
36.858000000000004
type
value
mrr_at_100
37.966
type
value
mrr_at_1000
38.004
type
value
mrr_at_3
33.188
type
value
mrr_at_5
35.367
type
value
ndcg_at_1
24.312
type
value
ndcg_at_10
43.126999999999995
type
value
ndcg_at_100
48.642
type
value
ndcg_at_1000
49.741
type
value
ndcg_at_3
35.589
type
value
ndcg_at_5
39.515
type
value
precision_at_1
24.312
type
value
precision_at_10
6.699
type
value
precision_at_100
0.9450000000000001
type
value
precision_at_1000
0.104
type
value
precision_at_3
15.153
type
value
precision_at_5
11.065999999999999
type
value
recall_at_1
23.631
type
value
recall_at_10
64.145
type
value
recall_at_100
89.41
type
value
recall_at_1000
97.83500000000001
type
value
recall_at_3
43.769000000000005
type
value
recall_at_5
53.169
task
dataset
metrics
type
name
config
split
revision
mteb/mtop_domain
MTEB MTOPDomainClassification (en)
en
test
d80d48c1eb48d3562165c59d59d0034df9fff0bf
type
value
accuracy
93.4108527131783
type
value
f1
93.1415880261038
task
dataset
metrics
type
name
config
split
revision
mteb/mtop_intent
MTEB MTOPIntentClassification (en)
en
test
ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
type
value
accuracy
77.24806201550388
type
value
f1
60.531916308197175
task
dataset
metrics
type
name
config
split
revision
mteb/amazon_massive_intent
MTEB MassiveIntentClassification (en)
en
test
31efe3c427b0bae9c22cbb560b8f15491cc6bed7
type
value
accuracy
73.71553463349024
type
value
f1
71.70753174900791
task
dataset
metrics
type
name
config
split
revision
mteb/amazon_massive_scenario
MTEB MassiveScenarioClassification (en)
en
test
7d571f92784cd94a019292a1f45445077d0ef634
type
value
accuracy
77.79757901815736
type
value
f1
77.83719850433258
task
dataset
metrics
type
name
config
split
revision
mteb/medrxiv-clustering-p2p
MTEB MedrxivClusteringP2P
default
test
e7a26af6f3ae46b30dde8737f02c07b1505bcc73
type
value
v_measure
33.74193296622113
task
dataset
metrics
type
name
config
split
revision
mteb/medrxiv-clustering-s2s
MTEB MedrxivClusteringS2S
default
test
35191c8c0dca72d8ff3efcd72aa802307d469663
type
value
v_measure
30.64257594108566
task
dataset
metrics
type
name
config
split
revision
mteb/mind_small
MTEB MindSmallReranking
default
test
3bdac13927fdc888b903db93b2ffdbd90b295a69
type
value
map
30.811018518883625
type
value
mrr
31.910376577445003
task
dataset
metrics
type
name
config
split
revision
nfcorpus
MTEB NFCorpus
default
test
None
type
value
map_at_1
5.409
type
value
map_at_10
13.093
type
value
map_at_100
16.256999999999998
type
value
map_at_1000
17.617
type
value
map_at_3
9.555
type
value
map_at_5
11.428
type
value
mrr_at_1
45.201
type
value
mrr_at_10
54.179
type
value
mrr_at_100
54.812000000000005
type
value
mrr_at_1000
54.840999999999994
type
value
mrr_at_3
51.909000000000006
type
value
mrr_at_5
53.519000000000005
type
value
ndcg_at_1
43.189
type
value
ndcg_at_10
35.028
type
value
ndcg_at_100
31.226
type
value
ndcg_at_1000
39.678000000000004
type
value
ndcg_at_3
40.596
type
value
ndcg_at_5
38.75
type
value
precision_at_1
44.582
type
value
precision_at_10
25.974999999999998
type
value
precision_at_100
7.793
type
value
precision_at_1000
2.036
type
value
precision_at_3
38.493
type
value
precision_at_5
33.994
type
value
recall_at_1
5.409
type
value
recall_at_10
16.875999999999998
type
value
recall_at_100
30.316
type
value
recall_at_1000
60.891
type
value
recall_at_3
10.688
type
value
recall_at_5
13.832
task
dataset
metrics
type
name
config
split
revision
nq
MTEB NQ
default
test
None
type
value
map_at_1
36.375
type
value
map_at_10
51.991
type
value
map_at_100
52.91400000000001
type
value
map_at_1000
52.93600000000001
type
value
map_at_3
48.014
type
value
map_at_5
50.381
type
value
mrr_at_1
40.759
type
value
mrr_at_10
54.617000000000004
type
value
mrr_at_100
55.301
type
value
mrr_at_1000
55.315000000000005
type
value
mrr_at_3
51.516
type
value
mrr_at_5
53.435
type
value
ndcg_at_1
40.759
type
value
ndcg_at_10
59.384
type
value
ndcg_at_100
63.157
type
value
ndcg_at_1000
63.654999999999994
type
value
ndcg_at_3
52.114000000000004
type
value
ndcg_at_5
55.986000000000004
type
value
precision_at_1
40.759
type
value
precision_at_10
9.411999999999999
type
value
precision_at_100
1.153
type
value
precision_at_1000
0.12
type
value
precision_at_3
23.329
type
value
precision_at_5
16.256999999999998
type
value
recall_at_1
36.375
type
value
recall_at_10
79.053
type
value
recall_at_100
95.167
type
value
recall_at_1000
98.82
type
value
recall_at_3
60.475
type
value
recall_at_5
69.327
task
dataset
metrics
type
name
config
split
revision
quora
MTEB QuoraRetrieval
default
test
None
type
value
map_at_1
70.256
type
value
map_at_10
83.8
type
value
map_at_100
84.425
type
value
map_at_1000
84.444
type
value
map_at_3
80.906
type
value
map_at_5
82.717
type
value
mrr_at_1
80.97999999999999
type
value
mrr_at_10
87.161
type
value
mrr_at_100
87.262
type
value
mrr_at_1000
87.263
type
value
mrr_at_3
86.175
type
value
mrr_at_5
86.848
type
value
ndcg_at_1
80.97999999999999
type
value
ndcg_at_10
87.697
type
value
ndcg_at_100
88.959
type
value
ndcg_at_1000
89.09899999999999
type
value
ndcg_at_3
84.83800000000001
type
value
ndcg_at_5
86.401
type
value
precision_at_1
80.97999999999999
type
value
precision_at_10
13.261000000000001
type
value
precision_at_100
1.5150000000000001
type
value
precision_at_1000
0.156
type
value
precision_at_3
37.01
type
value
precision_at_5
24.298000000000002
type
value
recall_at_1
70.256
type
value
recall_at_10
94.935
type
value
recall_at_100
99.274
type
value
recall_at_1000
99.928
type
value
recall_at_3
86.602
type
value
recall_at_5
91.133
task
dataset
metrics
type
name
config
split
revision
mteb/reddit-clustering
MTEB RedditClustering
default
test
24640382cdbf8abc73003fb0fa6d111a705499eb
type
value
v_measure
56.322692497613104
task
dataset
metrics
type
name
config
split
revision
mteb/reddit-clustering-p2p
MTEB RedditClusteringP2P
default
test
282350215ef01743dc01b456c7f5241fa8937f16
type
value
v_measure
61.895813503775074
task
dataset
metrics
type
name
config
split
revision
scidocs
MTEB SCIDOCS
default
test
None
type
value
map_at_1
4.338
type
value
map_at_10
10.767
type
value
map_at_100
12.537999999999998
type
value
map_at_1000
12.803999999999998
type
value
map_at_3
7.788
type
value
map_at_5
9.302000000000001
type
value
mrr_at_10
31.637999999999998
type
value
mrr_at_100
32.688
type
value
mrr_at_1000
32.756
type
value
mrr_at_3
28.433000000000003
type
value
mrr_at_5
30.178
type
value
ndcg_at_1
21.4
type
value
ndcg_at_10
18.293
type
value
ndcg_at_100
25.274
type
value
ndcg_at_1000
30.284
type
value
ndcg_at_3
17.391000000000002
type
value
ndcg_at_5
15.146999999999998
type
value
precision_at_1
21.4
type
value
precision_at_10
9.48
type
value
precision_at_100
1.949
type
value
precision_at_1000
0.316
type
value
precision_at_3
16.167
type
value
precision_at_5
13.22
type
value
recall_at_1
4.338
type
value
recall_at_10
19.213
type
value
recall_at_100
39.562999999999995
type
value
recall_at_1000
64.08
type
value
recall_at_3
9.828000000000001
type
value
recall_at_5
13.383000000000001
task
dataset
metrics
type
name
config
split
revision
mteb/sickr-sts
MTEB SICK-R
default
test
a6ea5a8cab320b040a23452cc28066d9beae2cee
type
value
cos_sim_pearson
82.42568163642142
type
value
cos_sim_spearman
78.5797159641342
type
value
euclidean_pearson
80.22151260811604
type
value
euclidean_spearman
78.5797151953878
type
value
manhattan_pearson
80.21224215864788
type
value
manhattan_spearman
78.55641478381344
task
dataset
metrics
type
name
config
split
revision
mteb/sts12-sts
MTEB STS12
default
test
a0d554a64d88156834ff5ae9920b964011b16384
type
value
cos_sim_pearson
85.44020710812569
type
value
cos_sim_spearman
78.91631735081286
type
value
euclidean_pearson
81.64188964182102
type
value
euclidean_spearman
78.91633286881678
type
value
manhattan_pearson
81.69294748512496
type
value
manhattan_spearman
78.93438558002656
task
dataset
metrics
type
name
config
split
revision
mteb/sts13-sts
MTEB STS13
default
test
7e90230a92c190f1bf69ae9002b8cea547a64cca
type
value
cos_sim_pearson
84.27165426412311
type
value
cos_sim_spearman
85.40429140249618
type
value
euclidean_pearson
84.7509580724893
type
value
euclidean_spearman
85.40429140249618
type
value
manhattan_pearson
84.76488289321308
type
value
manhattan_spearman
85.4256793698708
task
dataset
metrics
type
name
config
split
revision
mteb/sts14-sts
MTEB STS14
default
test
6031580fec1f6af667f0bd2da0a551cf4f0b2375
type
value
cos_sim_pearson
83.138851760732
type
value
cos_sim_spearman
81.64101363896586
type
value
euclidean_pearson
82.55165038934942
type
value
euclidean_spearman
81.64105257080502
type
value
manhattan_pearson
82.52802949883335
type
value
manhattan_spearman
81.61255430718158
task
dataset
metrics
type
name
config
split
revision
mteb/sts15-sts
MTEB STS15
default
test
ae752c7c21bf194d8b67fd573edf7ae58183cbe3
type
value
cos_sim_pearson
86.0654695484029
type
value
cos_sim_spearman
87.20408521902229
type
value
euclidean_pearson
86.8110651362115
type
value
euclidean_spearman
87.20408521902229
type
value
manhattan_pearson
86.77984656478691
type
value
manhattan_spearman
87.1719947099227
task
dataset
metrics
type
name
config
split
revision
mteb/sts16-sts
MTEB STS16
default
test
4d8694f8f0e0100860b497b999b3dbed754a0513
type
value
cos_sim_pearson
83.77823915496512
type
value
cos_sim_spearman
85.43566325729779
type
value
euclidean_pearson
84.5396956658821
type
value
euclidean_spearman
85.43566325729779
type
value
manhattan_pearson
84.5665398848169
type
value
manhattan_spearman
85.44375870303232
task
dataset
metrics
type
name
config
split
revision
mteb/sts17-crosslingual-sts
MTEB STS17 (en-en)
en-en
test
af5e6fb845001ecf41f4c1e033ce921939a2a68d
type
value
cos_sim_pearson
87.20030208471798
type
value
cos_sim_spearman
87.20485505076539
type
value
euclidean_pearson
88.10588324368722
type
value
euclidean_spearman
87.20485505076539
type
value
manhattan_pearson
87.92324770415183
type
value
manhattan_spearman
87.0571314561877
task
dataset
metrics
type
name
config
split
revision
mteb/sts22-crosslingual-sts
MTEB STS22 (en)
en
test
6d1ba47164174a496b7fa5d3569dae26a6813b80
type
value
cos_sim_pearson
63.06093161604453
type
value
cos_sim_spearman
64.2163140357722
type
value
euclidean_pearson
65.27589680994006
type
value
euclidean_spearman
64.2163140357722
type
value
manhattan_pearson
65.45904383711101
type
value
manhattan_spearman
64.55404716679305
task
dataset
metrics
type
name
config
split
revision
mteb/stsbenchmark-sts
MTEB STSBenchmark
default
test
b0fddb56ed78048fa8b90373c8a3cfc37b684831
type
value
cos_sim_pearson
84.32976164578706
type
value
cos_sim_spearman
85.54302197678368
type
value
euclidean_pearson
85.26307149193056
type
value
euclidean_spearman
85.54302197678368
type
value
manhattan_pearson
85.26647282029371
type
value
manhattan_spearman
85.5316135265568
task
dataset
metrics
type
name
config
split
revision
mteb/scidocs-reranking
MTEB SciDocsRR
default
test
d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
type
value
map
81.44675968318754
type
value
mrr
94.92741826075158
task
dataset
metrics
type
name
config
split
revision
scifact
MTEB SciFact
default
test
None
type
value
map_at_1
56.34400000000001
type
value
map_at_10
65.927
type
value
map_at_100
66.431
type
value
map_at_1000
66.461
type
value
map_at_3
63.529
type
value
map_at_5
64.818
type
value
mrr_at_1
59.333000000000006
type
value
mrr_at_10
67.54599999999999
type
value
mrr_at_100
67.892
type
value
mrr_at_1000
67.917
type
value
mrr_at_3
65.778
type
value
mrr_at_5
66.794
type
value
ndcg_at_1
59.333000000000006
type
value
ndcg_at_10
70.5
type
value
ndcg_at_100
72.688
type
value
ndcg_at_1000
73.483
type
value
ndcg_at_3
66.338
type
value
ndcg_at_5
68.265
type
value
precision_at_1
59.333000000000006
type
value
precision_at_10
9.3
type
value
precision_at_100
1.053
type
value
precision_at_1000
0.11199999999999999
type
value
precision_at_3
25.889
type
value
precision_at_5
16.866999999999997
type
value
recall_at_1
56.34400000000001
type
value
recall_at_10
82.789
type
value
recall_at_100
92.767
type
value
recall_at_1000
99
type
value
recall_at_3
71.64399999999999
type
value
recall_at_5
76.322
task
dataset
metrics
type
name
config
split
revision
mteb/sprintduplicatequestions-pairclassification
MTEB SprintDuplicateQuestions
default
test
d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
type
value
cos_sim_accuracy
99.75742574257426
type
value
cos_sim_ap
93.52081548447406
type
value
cos_sim_f1
87.33850129198966
type
value
cos_sim_precision
90.37433155080214
type
value
cos_sim_recall
84.5
type
value
dot_accuracy
99.75742574257426
type
value
dot_ap
93.52081548447406
type
value
dot_f1
87.33850129198966
type
value
dot_precision
90.37433155080214
type
value
dot_recall
84.5
type
value
euclidean_accuracy
99.75742574257426
type
value
euclidean_ap
93.52081548447406
type
value
euclidean_f1
87.33850129198966
type
value
euclidean_precision
90.37433155080214
type
value
euclidean_recall
84.5
type
value
manhattan_accuracy
99.75841584158415
type
value
manhattan_ap
93.4975678585854
type
value
manhattan_f1
87.26708074534162
type
value
manhattan_precision
90.45064377682404
type
value
manhattan_recall
84.3
type
value
max_accuracy
99.75841584158415
type
value
max_ap
93.52081548447406
type
value
max_f1
87.33850129198966
task
dataset
metrics
type
name
config
split
revision
mteb/stackexchange-clustering
MTEB StackExchangeClustering
default
test
6cbc1f7b2bc0622f2e39d2c77fa502909748c259
type
value
v_measure
64.31437036686651
task
dataset
metrics
type
name
config
split
revision
mteb/stackexchange-clustering-p2p
MTEB StackExchangeClusteringP2P
default
test
815ca46b2622cec33ccafc3735d572c266efdb44
type
value
v_measure
33.25569319007206
task
dataset
metrics
type
name
config
split
revision
mteb/stackoverflowdupquestions-reranking
MTEB StackOverflowDupQuestions
default
test
e185fbe320c72810689fc5848eb6114e1ef5ec69
type
value
map
49.90474939720706
type
value
mrr
50.568115503777264
task
dataset
metrics
type
name
config
split
revision
mteb/summeval
MTEB SummEval
default
test
cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
type
value
cos_sim_pearson
29.866828641244712
type
value
cos_sim_spearman
30.077555055873866
type
value
dot_pearson
29.866832988572266
type
value
dot_spearman
30.077555055873866
task
dataset
metrics
type
name
config
split
revision
trec-covid
MTEB TRECCOVID
default
test
None
type
value
map_at_1
0.232
type
value
map_at_10
2.094
type
value
map_at_100
11.971
type
value
map_at_1000
28.158
type
value
map_at_3
0.688
type
value
map_at_5
1.114
type
value
mrr_at_10
93.4
type
value
mrr_at_100
93.4
type
value
mrr_at_1000
93.4
type
value
ndcg_at_10
79.923
type
value
ndcg_at_100
61.17
type
value
ndcg_at_1000
53.03
type
value
ndcg_at_3
84.592
type
value
ndcg_at_5
82.821
type
value
precision_at_1
88
type
value
precision_at_10
85
type
value
precision_at_100
63.019999999999996
type
value
precision_at_1000
23.554
type
value
precision_at_3
89.333
type
value
precision_at_5
87.2
type
value
recall_at_1
0.232
type
value
recall_at_10
2.255
type
value
recall_at_100
14.823
type
value
recall_at_1000
49.456
type
value
recall_at_3
0.718
type
value
recall_at_5
1.175
task
dataset
metrics
type
name
config
split
revision
webis-touche2020
MTEB Touche2020
default
test
None
type
value
map_at_1
2.547
type
value
map_at_10
11.375
type
value
map_at_100
18.194
type
value
map_at_1000
19.749
type
value
map_at_3
5.825
type
value
map_at_5
8.581
type
value
mrr_at_1
32.653
type
value
mrr_at_10
51.32
type
value
mrr_at_100
51.747
type
value
mrr_at_1000
51.747
type
value
mrr_at_3
47.278999999999996
type
value
mrr_at_5
48.605
type
value
ndcg_at_1
29.592000000000002
type
value
ndcg_at_10
28.151
type
value
ndcg_at_100
39.438
type
value
ndcg_at_1000
50.769
type
value
ndcg_at_3
30.758999999999997
type
value
ndcg_at_5
30.366
type
value
precision_at_1
32.653
type
value
precision_at_10
25.714
type
value
precision_at_100
8.041
type
value
precision_at_1000
1.555
type
value
precision_at_3
33.333
type
value
precision_at_5
31.837
type
value
recall_at_1
2.547
type
value
recall_at_10
18.19
type
value
recall_at_100
49.538
type
value
recall_at_1000
83.86
type
value
recall_at_3
7.329
type
value
recall_at_5
11.532
task
dataset
metrics
type
name
config
split
revision
mteb/toxic_conversations_50k
MTEB ToxicConversationsClassification
default
test
d7c0de2777da35d6aae2200a62c6e0e5af397c4c
type
value
accuracy
71.4952
type
value
ap
14.793362635531409
type
value
f1
55.204635551516915
task
dataset
metrics
type
name
config
split
revision
mteb/tweet_sentiment_extraction
MTEB TweetSentimentExtractionClassification
default
test
d604517c81ca91fe16a244d1248fc021f9ecee7a
type
value
accuracy
61.5365025466893
type
value
f1
61.81742556334845
task
dataset
metrics
type
name
config
split
revision
mteb/twentynewsgroups-clustering
MTEB TwentyNewsgroupsClustering
default
test
6125ec4e24fa026cec8a478383ee943acfbd5449
type
value
v_measure
49.05531070301185
task
dataset
metrics
type
name
config
split
revision
mteb/twittersemeval2015-pairclassification
MTEB TwitterSemEval2015
default
test
70970daeab8776df92f5ea462b6173c0b46fd2d1
type
value
cos_sim_accuracy
86.51725576682364
type
value
cos_sim_ap
75.2292304265163
type
value
cos_sim_f1
69.54022988505749
type
value
cos_sim_precision
63.65629110039457
type
value
cos_sim_recall
76.62269129287598
type
value
dot_accuracy
86.51725576682364
type
value
dot_ap
75.22922386081054
type
value
dot_f1
69.54022988505749
type
value
dot_precision
63.65629110039457
type
value
dot_recall
76.62269129287598
type
value
euclidean_accuracy
86.51725576682364
type
value
euclidean_ap
75.22925730473472
type
value
euclidean_f1
69.54022988505749
type
value
euclidean_precision
63.65629110039457
type
value
euclidean_recall
76.62269129287598
type
value
manhattan_accuracy
86.52321630804077
type
value
manhattan_ap
75.20608115037336
type
value
manhattan_f1
69.60000000000001
type
value
manhattan_precision
64.37219730941705
type
value
manhattan_recall
75.75197889182058
type
value
max_accuracy
86.52321630804077
type
value
max_ap
75.22925730473472
type
value
max_f1
69.60000000000001
task
dataset
metrics
type
name
config
split
revision
mteb/twitterurlcorpus-pairclassification
MTEB TwitterURLCorpus
default
test
8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
type
value
cos_sim_accuracy
89.34877944657896
type
value
cos_sim_ap
86.71257569277373
type
value
cos_sim_f1
79.10386355986088
type
value
cos_sim_precision
76.91468470434214
type
value
cos_sim_recall
81.4213119802895
type
value
dot_accuracy
89.34877944657896
type
value
dot_ap
86.71257133133368
type
value
dot_f1
79.10386355986088
type
value
dot_precision
76.91468470434214
type
value
dot_recall
81.4213119802895
type
value
euclidean_accuracy
89.34877944657896
type
value
euclidean_ap
86.71257651501476
type
value
euclidean_f1
79.10386355986088
type
value
euclidean_precision
76.91468470434214
type
value
euclidean_recall
81.4213119802895
type
value
manhattan_accuracy
89.35848177901967
type
value
manhattan_ap
86.69330615469126
type
value
manhattan_f1
79.13867741453949
type
value
manhattan_precision
76.78881807647741
type
value
manhattan_recall
81.63689559593472
type
value
max_accuracy
89.35848177901967
type
value
max_ap
86.71257651501476
type
value
max_f1
79.13867741453949
apache-2.0
nomic-ai/nomic-embed-text-v1.5
nomic-embed-text-v1: A Reproducible Long Context (8192) Text Embedder
nomic-embed-text-v1
is 8192 context length text encoder that surpasses OpenAI text-embedding-ada-002 and text-embedding-3-small performance on short and long context tasks.
Performance Benchmarks
Name
SeqLen
MTEB
LoCo
Jina Long Context
Open Weights
Open Training Code
Open Data
nomic-embed-text-v1
8192
62.39
85.53
54.16
✅
✅
✅
jina-embeddings-v2-base-en
8192
60.39
85.45
51.90
✅
❌
❌
text-embedding-3-small
8191
62.26
82.40
58.20
❌
❌
❌
text-embedding-ada-002
8191
60.99
52.7
55.25
❌
❌
❌
Exciting Update! : nomic-embed-text-v1
is now multimodal! nomic-embed-vision-v1 is aligned to the embedding space of nomic-embed-text-v1
, meaning any text embedding is multimodal!
Usage
Important : the text prompt must include a task instruction prefix , instructing the model which task is being performed.
For example, if you are implementing a RAG application, you embed your documents as search_document: <text here>
and embed your user queries as search_query: <text here>
.
Task instruction prefixes
search_document
Purpose: embed texts as documents from a dataset
This prefix is used for embedding texts as documents, for example as documents for a RAG index.
from sentence_transformers import SentenceTransformer
model = SentenceTransformer ( "nomic-ai/nomic-embed-text-v1" , trust_remote_code = True )
sentences = [ 'search_document: TSNE is a dimensionality reduction algorithm created by Laurens van Der Maaten' ]
embeddings = model . encode ( sentences )
print ( embeddings )
search_query
Purpose: embed texts as questions to answer
This prefix is used for embedding texts as questions that documents from a dataset could resolve, for example as queries to be answered by a RAG application.
from sentence_transformers import SentenceTransformer
model = SentenceTransformer ( "nomic-ai/nomic-embed-text-v1" , trust_remote_code = True )
sentences = [ 'search_query: Who is Laurens van Der Maaten?' ]
embeddings = model . encode ( sentences )
print ( embeddings )
clustering
Purpose: embed texts to group them into clusters
This prefix is used for embedding texts in order to group them into clusters, discover common topics, or remove semantic duplicates.
from sentence_transformers import SentenceTransformer
model = SentenceTransformer ( "nomic-ai/nomic-embed-text-v1" , trust_remote_code = True )
sentences = [ 'clustering: the quick brown fox' ]
embeddings = model . encode ( sentences )
print ( embeddings )
classification
Purpose: embed texts to classify them
This prefix is used for embedding texts into vectors that will be used as features for a classification model
from sentence_transformers import SentenceTransformer
model = SentenceTransformer ( "nomic-ai/nomic-embed-text-v1" , trust_remote_code = True )
sentences = [ 'classification: the quick brown fox' ]
embeddings = model . encode ( sentences )
print ( embeddings )
Sentence Transformers
from sentence_transformers import SentenceTransformer
model = SentenceTransformer ( "nomic-ai/nomic-embed-text-v1" , trust_remote_code = True )
sentences = [ 'search_query: What is TSNE?' , 'search_query: Who is Laurens van der Maaten?' ]
embeddings = model . encode ( sentences )
print ( embeddings )
Transformers
import torch
import torch.nn.functional as F
from transformers import AutoTokenizer , AutoModel
def mean_pooling ( model_output , attention_mask ):
token_embeddings = model_output [ 0 ]
input_mask_expanded = attention_mask . unsqueeze ( - 1 ) . expand ( token_embeddings . size ()) . float ()
return torch . sum ( token_embeddings * input_mask_expanded , 1 ) / torch . clamp ( input_mask_expanded . sum ( 1 ), min = 1e-9 )
sentences = [ 'search_query: What is TSNE?' , 'search_query: Who is Laurens van der Maaten?' ]
tokenizer = AutoTokenizer . from_pretrained ( 'bert-base-uncased' )
model = AutoModel . from_pretrained ( 'nomic-ai/nomic-embed-text-v1' , trust_remote_code = True )
model . eval ()
encoded_input = tokenizer ( sentences , padding = True , truncation = True , return_tensors = 'pt' )
with torch . no_grad ():
model_output = model ( ** encoded_input )
embeddings = mean_pooling ( model_output , encoded_input [ 'attention_mask' ])
embeddings = F . normalize ( embeddings , p = 2 , dim = 1 )
print ( embeddings )
The model natively supports scaling of the sequence length past 2048 tokens. To do so,
- tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
+ tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', model_max_length=8192)
- model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1', trust_remote_code=True)
+ model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1', trust_remote_code=True, rotary_scaling_factor=2)
Transformers.js
import { pipeline } from '@xenova/transformers' ;
// Create a feature extraction pipeline
const extractor = await pipeline ( 'feature-extraction' , 'nomic-ai/nomic-embed-text-v1' , {
quantized : false , // Comment out this line to use the quantized version
});
// Compute sentence embeddings
const texts = [ 'search_query: What is TSNE?' , 'search_query: Who is Laurens van der Maaten?' ];
const embeddings = await extractor ( texts , { pooling : 'mean' , normalize : true });
console . log ( embeddings );
Nomic API
The easiest way to get started with Nomic Embed is through the Nomic Embedding API.
Generating embeddings with the nomic
Python client is as easy as
from nomic import embed
output = embed . text (
texts = [ 'Nomic Embedding API' , '#keepAIOpen' ],
model = 'nomic-embed-text-v1' ,
task_type = 'search_document'
)
print ( output )
For more information, see the API reference
Training
Click the Nomic Atlas map below to visualize a 5M sample of our contrastive pretraining data!
We train our embedder using a multi-stage training pipeline. Starting from a long-context BERT model ,
the first unsupervised contrastive stage trains on a dataset generated from weakly related text pairs, such as question-answer pairs from forums like StackExchange and Quora, title-body pairs from Amazon reviews, and summarizations from news articles.
In the second finetuning stage, higher quality labeled datasets such as search queries and answers from web searches are leveraged. Data curation and hard-example mining is crucial in this stage.
For more details, see the Nomic Embed Technical Report and corresponding blog post .
Training data to train the models is released in its entirety. For more details, see the contrastors
repository
Citation
If you find the model, dataset, or training code useful, please cite our work
@misc { nussbaum2024nomic ,
title = {Nomic Embed: Training a Reproducible Long Context Text Embedder} ,
author = {Zach Nussbaum and John X. Morris and Brandon Duderstadt and Andriy Mulyar} ,
year = {2024} ,
eprint = {2402.01613} ,
archivePrefix = {arXiv} ,
primaryClass = {cs.CL}
}