metadata
language:
- multilingual
- af
- am
- ar
- as
- az
- be
- bg
- bn
- br
- bs
- ca
- cs
- cy
- da
- de
- el
- en
- eo
- es
- et
- eu
- fa
- fi
- fr
- fy
- ga
- gd
- gl
- gu
- ha
- he
- hi
- hr
- hu
- hy
- id
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lo
- lt
- lv
- mg
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- 'no'
- om
- or
- pa
- pl
- ps
- pt
- ro
- ru
- sa
- sd
- si
- sk
- sl
- so
- sq
- sr
- su
- sv
- sw
- ta
- te
- th
- tl
- tr
- ug
- uk
- ur
- uz
- vi
- xh
- yi
- zh
license: mit
model-index:
- name: multilingual-e5-large
results:
- dataset:
config: en
name: MTEB AmazonCounterfactualClassification (en)
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
split: test
type: mteb/amazon_counterfactual
metrics:
- type: accuracy
value: 79.05970149253731
- type: ap
value: 43.486574390835635
- type: f1
value: 73.32700092140148
task:
type: Classification
- dataset:
config: de
name: MTEB AmazonCounterfactualClassification (de)
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
split: test
type: mteb/amazon_counterfactual
metrics:
- type: accuracy
value: 71.22055674518201
- type: ap
value: 81.55756710830498
- type: f1
value: 69.28271787752661
task:
type: Classification
- dataset:
config: en-ext
name: MTEB AmazonCounterfactualClassification (en-ext)
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
split: test
type: mteb/amazon_counterfactual
metrics:
- type: accuracy
value: 80.41979010494754
- type: ap
value: 29.34879922376344
- type: f1
value: 67.62475449011278
task:
type: Classification
- dataset:
config: ja
name: MTEB AmazonCounterfactualClassification (ja)
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
split: test
type: mteb/amazon_counterfactual
metrics:
- type: accuracy
value: 77.8372591006424
- type: ap
value: 26.557560591210738
- type: f1
value: 64.96619417368707
task:
type: Classification
- dataset:
config: default
name: MTEB AmazonPolarityClassification
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
split: test
type: mteb/amazon_polarity
metrics:
- type: accuracy
value: 93.489875
- type: ap
value: 90.98758636917603
- type: f1
value: 93.48554819717332
task:
type: Classification
- dataset:
config: en
name: MTEB AmazonReviewsClassification (en)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
- type: accuracy
value: 47.564
- type: f1
value: 46.75122173518047
task:
type: Classification
- dataset:
config: de
name: MTEB AmazonReviewsClassification (de)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
- type: accuracy
value: 45.400000000000006
- type: f1
value: 44.17195682400632
task:
type: Classification
- dataset:
config: es
name: MTEB AmazonReviewsClassification (es)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
- type: accuracy
value: 43.068
- type: f1
value: 42.38155696855596
task:
type: Classification
- dataset:
config: fr
name: MTEB AmazonReviewsClassification (fr)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
- type: accuracy
value: 41.89
- type: f1
value: 40.84407321682663
task:
type: Classification
- dataset:
config: ja
name: MTEB AmazonReviewsClassification (ja)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
- type: accuracy
value: 40.120000000000005
- type: f1
value: 39.522976223819114
task:
type: Classification
- dataset:
config: zh
name: MTEB AmazonReviewsClassification (zh)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
- type: accuracy
value: 38.832
- type: f1
value: 38.0392533394713
task:
type: Classification
- dataset:
config: default
name: MTEB ArguAna
revision: None
split: test
type: arguana
metrics:
- type: map_at_1
value: 30.725
- type: map_at_10
value: 46.055
- type: map_at_100
value: 46.900999999999996
- type: map_at_1000
value: 46.911
- type: map_at_3
value: 41.548
- type: map_at_5
value: 44.297
- type: mrr_at_1
value: 31.152
- type: mrr_at_10
value: 46.231
- type: mrr_at_100
value: 47.07
- type: mrr_at_1000
value: 47.08
- type: mrr_at_3
value: 41.738
- type: mrr_at_5
value: 44.468999999999994
- type: ndcg_at_1
value: 30.725
- type: ndcg_at_10
value: 54.379999999999995
- type: ndcg_at_100
value: 58.138
- type: ndcg_at_1000
value: 58.389
- type: ndcg_at_3
value: 45.156
- type: ndcg_at_5
value: 50.123
- type: precision_at_1
value: 30.725
- type: precision_at_10
value: 8.087
- type: precision_at_100
value: 0.9769999999999999
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 18.54
- type: precision_at_5
value: 13.542000000000002
- type: recall_at_1
value: 30.725
- type: recall_at_10
value: 80.868
- type: recall_at_100
value: 97.653
- type: recall_at_1000
value: 99.57300000000001
- type: recall_at_3
value: 55.619
- type: recall_at_5
value: 67.71000000000001
task:
type: Retrieval
- dataset:
config: default
name: MTEB ArxivClusteringP2P
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
split: test
type: mteb/arxiv-clustering-p2p
metrics:
- type: v_measure
value: 44.30960650674069
task:
type: Clustering
- dataset:
config: default
name: MTEB ArxivClusteringS2S
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
split: test
type: mteb/arxiv-clustering-s2s
metrics:
- type: v_measure
value: 38.427074197498996
task:
type: Clustering
- dataset:
config: default
name: MTEB AskUbuntuDupQuestions
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
split: test
type: mteb/askubuntudupquestions-reranking
metrics:
- type: map
value: 60.28270056031872
- type: mrr
value: 74.38332673789738
task:
type: Reranking
- dataset:
config: default
name: MTEB BIOSSES
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
split: test
type: mteb/biosses-sts
metrics:
- type: cos_sim_pearson
value: 84.05942144105269
- type: cos_sim_spearman
value: 82.51212105850809
- type: euclidean_pearson
value: 81.95639829909122
- type: euclidean_spearman
value: 82.3717564144213
- type: manhattan_pearson
value: 81.79273425468256
- type: manhattan_spearman
value: 82.20066817871039
task:
type: STS
- dataset:
config: de-en
name: MTEB BUCC (de-en)
revision: d51519689f32196a32af33b075a01d0e7c51e252
split: test
type: mteb/bucc-bitext-mining
metrics:
- type: accuracy
value: 99.46764091858039
- type: f1
value: 99.37717466945023
- type: precision
value: 99.33194154488518
- type: recall
value: 99.46764091858039
task:
type: BitextMining
- dataset:
config: fr-en
name: MTEB BUCC (fr-en)
revision: d51519689f32196a32af33b075a01d0e7c51e252
split: test
type: mteb/bucc-bitext-mining
metrics:
- type: accuracy
value: 98.29407880255337
- type: f1
value: 98.11248073959938
- type: precision
value: 98.02443319392472
- type: recall
value: 98.29407880255337
task:
type: BitextMining
- dataset:
config: ru-en
name: MTEB BUCC (ru-en)
revision: d51519689f32196a32af33b075a01d0e7c51e252
split: test
type: mteb/bucc-bitext-mining
metrics:
- type: accuracy
value: 97.79009352268791
- type: f1
value: 97.5176076665512
- type: precision
value: 97.38136473848286
- type: recall
value: 97.79009352268791
task:
type: BitextMining
- dataset:
config: zh-en
name: MTEB BUCC (zh-en)
revision: d51519689f32196a32af33b075a01d0e7c51e252
split: test
type: mteb/bucc-bitext-mining
metrics:
- type: accuracy
value: 99.26276987888363
- type: f1
value: 99.20133403545726
- type: precision
value: 99.17500438827453
- type: recall
value: 99.26276987888363
task:
type: BitextMining
- dataset:
config: default
name: MTEB Banking77Classification
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
split: test
type: mteb/banking77
metrics:
- type: accuracy
value: 84.72727272727273
- type: f1
value: 84.67672206031433
task:
type: Classification
- dataset:
config: default
name: MTEB BiorxivClusteringP2P
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
split: test
type: mteb/biorxiv-clustering-p2p
metrics:
- type: v_measure
value: 35.34220182511161
task:
type: Clustering
- dataset:
config: default
name: MTEB BiorxivClusteringS2S
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
split: test
type: mteb/biorxiv-clustering-s2s
metrics:
- type: v_measure
value: 33.4987096128766
task:
type: Clustering
- dataset:
config: default
name: MTEB CQADupstackRetrieval
revision: None
split: test
type: BeIR/cqadupstack
metrics:
- type: map_at_1
value: 25.558249999999997
- type: map_at_10
value: 34.44425000000001
- type: map_at_100
value: 35.59833333333333
- type: map_at_1000
value: 35.706916666666665
- type: map_at_3
value: 31.691749999999995
- type: map_at_5
value: 33.252916666666664
- type: mrr_at_1
value: 30.252666666666666
- type: mrr_at_10
value: 38.60675
- type: mrr_at_100
value: 39.42666666666666
- type: mrr_at_1000
value: 39.48408333333334
- type: mrr_at_3
value: 36.17441666666665
- type: mrr_at_5
value: 37.56275
- type: ndcg_at_1
value: 30.252666666666666
- type: ndcg_at_10
value: 39.683
- type: ndcg_at_100
value: 44.68541666666667
- type: ndcg_at_1000
value: 46.94316666666668
- type: ndcg_at_3
value: 34.961749999999995
- type: ndcg_at_5
value: 37.215666666666664
- type: precision_at_1
value: 30.252666666666666
- type: precision_at_10
value: 6.904166666666667
- type: precision_at_100
value: 1.0989999999999995
- type: precision_at_1000
value: 0.14733333333333334
- type: precision_at_3
value: 16.037666666666667
- type: precision_at_5
value: 11.413583333333333
- type: recall_at_1
value: 25.558249999999997
- type: recall_at_10
value: 51.13341666666666
- type: recall_at_100
value: 73.08366666666667
- type: recall_at_1000
value: 88.79483333333334
- type: recall_at_3
value: 37.989083333333326
- type: recall_at_5
value: 43.787833333333325
task:
type: Retrieval
- dataset:
config: default
name: MTEB ClimateFEVER
revision: None
split: test
type: climate-fever
metrics:
- type: map_at_1
value: 10.338
- type: map_at_10
value: 18.360000000000003
- type: map_at_100
value: 19.942
- type: map_at_1000
value: 20.134
- type: map_at_3
value: 15.174000000000001
- type: map_at_5
value: 16.830000000000002
- type: mrr_at_1
value: 23.257
- type: mrr_at_10
value: 33.768
- type: mrr_at_100
value: 34.707
- type: mrr_at_1000
value: 34.766000000000005
- type: mrr_at_3
value: 30.977
- type: mrr_at_5
value: 32.528
- type: ndcg_at_1
value: 23.257
- type: ndcg_at_10
value: 25.733
- type: ndcg_at_100
value: 32.288
- type: ndcg_at_1000
value: 35.992000000000004
- type: ndcg_at_3
value: 20.866
- type: ndcg_at_5
value: 22.612
- type: precision_at_1
value: 23.257
- type: precision_at_10
value: 8.124
- type: precision_at_100
value: 1.518
- type: precision_at_1000
value: 0.219
- type: precision_at_3
value: 15.679000000000002
- type: precision_at_5
value: 12.117
- type: recall_at_1
value: 10.338
- type: recall_at_10
value: 31.154
- type: recall_at_100
value: 54.161
- type: recall_at_1000
value: 75.21900000000001
- type: recall_at_3
value: 19.427
- type: recall_at_5
value: 24.214
task:
type: Retrieval
- dataset:
config: default
name: MTEB DBPedia
revision: None
split: test
type: dbpedia-entity
metrics:
- type: map_at_1
value: 8.498
- type: map_at_10
value: 19.103
- type: map_at_100
value: 27.375
- type: map_at_1000
value: 28.981
- type: map_at_3
value: 13.764999999999999
- type: map_at_5
value: 15.950000000000001
- type: mrr_at_1
value: 65.5
- type: mrr_at_10
value: 74.53800000000001
- type: mrr_at_100
value: 74.71799999999999
- type: mrr_at_1000
value: 74.725
- type: mrr_at_3
value: 72.792
- type: mrr_at_5
value: 73.554
- type: ndcg_at_1
value: 53.37499999999999
- type: ndcg_at_10
value: 41.286
- type: ndcg_at_100
value: 45.972
- type: ndcg_at_1000
value: 53.123
- type: ndcg_at_3
value: 46.172999999999995
- type: ndcg_at_5
value: 43.033
- type: precision_at_1
value: 65.5
- type: precision_at_10
value: 32.725
- type: precision_at_100
value: 10.683
- type: precision_at_1000
value: 1.978
- type: precision_at_3
value: 50
- type: precision_at_5
value: 41.349999999999994
- type: recall_at_1
value: 8.498
- type: recall_at_10
value: 25.070999999999998
- type: recall_at_100
value: 52.383
- type: recall_at_1000
value: 74.91499999999999
- type: recall_at_3
value: 15.207999999999998
- type: recall_at_5
value: 18.563
task:
type: Retrieval
- dataset:
config: default
name: MTEB EmotionClassification
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
split: test
type: mteb/emotion
metrics:
- type: accuracy
value: 46.5
- type: f1
value: 41.93833713984145
task:
type: Classification
- dataset:
config: default
name: MTEB FEVER
revision: None
split: test
type: fever
metrics:
- type: map_at_1
value: 67.914
- type: map_at_10
value: 78.10000000000001
- type: map_at_100
value: 78.333
- type: map_at_1000
value: 78.346
- type: map_at_3
value: 76.626
- type: map_at_5
value: 77.627
- type: mrr_at_1
value: 72.74199999999999
- type: mrr_at_10
value: 82.414
- type: mrr_at_100
value: 82.511
- type: mrr_at_1000
value: 82.513
- type: mrr_at_3
value: 81.231
- type: mrr_at_5
value: 82.065
- type: ndcg_at_1
value: 72.74199999999999
- type: ndcg_at_10
value: 82.806
- type: ndcg_at_100
value: 83.677
- type: ndcg_at_1000
value: 83.917
- type: ndcg_at_3
value: 80.305
- type: ndcg_at_5
value: 81.843
- type: precision_at_1
value: 72.74199999999999
- type: precision_at_10
value: 10.24
- type: precision_at_100
value: 1.089
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 31.268
- type: precision_at_5
value: 19.706000000000003
- type: recall_at_1
value: 67.914
- type: recall_at_10
value: 92.889
- type: recall_at_100
value: 96.42699999999999
- type: recall_at_1000
value: 97.92
- type: recall_at_3
value: 86.21
- type: recall_at_5
value: 90.036
task:
type: Retrieval
- dataset:
config: default
name: MTEB FiQA2018
revision: None
split: test
type: fiqa
metrics:
- type: map_at_1
value: 22.166
- type: map_at_10
value: 35.57
- type: map_at_100
value: 37.405
- type: map_at_1000
value: 37.564
- type: map_at_3
value: 30.379
- type: map_at_5
value: 33.324
- type: mrr_at_1
value: 43.519000000000005
- type: mrr_at_10
value: 51.556000000000004
- type: mrr_at_100
value: 52.344
- type: mrr_at_1000
value: 52.373999999999995
- type: mrr_at_3
value: 48.868
- type: mrr_at_5
value: 50.319
- type: ndcg_at_1
value: 43.519000000000005
- type: ndcg_at_10
value: 43.803
- type: ndcg_at_100
value: 50.468999999999994
- type: ndcg_at_1000
value: 53.111
- type: ndcg_at_3
value: 38.893
- type: ndcg_at_5
value: 40.653
- type: precision_at_1
value: 43.519000000000005
- type: precision_at_10
value: 12.253
- type: precision_at_100
value: 1.931
- type: precision_at_1000
value: 0.242
- type: precision_at_3
value: 25.617
- type: precision_at_5
value: 19.383
- type: recall_at_1
value: 22.166
- type: recall_at_10
value: 51.6
- type: recall_at_100
value: 76.574
- type: recall_at_1000
value: 92.192
- type: recall_at_3
value: 34.477999999999994
- type: recall_at_5
value: 41.835
task:
type: Retrieval
- dataset:
config: default
name: MTEB HotpotQA
revision: None
split: test
type: hotpotqa
metrics:
- type: map_at_1
value: 39.041
- type: map_at_10
value: 62.961999999999996
- type: map_at_100
value: 63.79899999999999
- type: map_at_1000
value: 63.854
- type: map_at_3
value: 59.399
- type: map_at_5
value: 61.669
- type: mrr_at_1
value: 78.082
- type: mrr_at_10
value: 84.321
- type: mrr_at_100
value: 84.49600000000001
- type: mrr_at_1000
value: 84.502
- type: mrr_at_3
value: 83.421
- type: mrr_at_5
value: 83.977
- type: ndcg_at_1
value: 78.082
- type: ndcg_at_10
value: 71.229
- type: ndcg_at_100
value: 74.10900000000001
- type: ndcg_at_1000
value: 75.169
- type: ndcg_at_3
value: 66.28699999999999
- type: ndcg_at_5
value: 69.084
- type: precision_at_1
value: 78.082
- type: precision_at_10
value: 14.993
- type: precision_at_100
value: 1.7239999999999998
- type: precision_at_1000
value: 0.186
- type: precision_at_3
value: 42.737
- type: precision_at_5
value: 27.843
- type: recall_at_1
value: 39.041
- type: recall_at_10
value: 74.96300000000001
- type: recall_at_100
value: 86.199
- type: recall_at_1000
value: 93.228
- type: recall_at_3
value: 64.105
- type: recall_at_5
value: 69.608
task:
type: Retrieval
- dataset:
config: default
name: MTEB ImdbClassification
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
split: test
type: mteb/imdb
metrics:
- type: accuracy
value: 90.23160000000001
- type: ap
value: 85.5674856808308
- type: f1
value: 90.18033354786317
task:
type: Classification
- dataset:
config: default
name: MTEB MSMARCO
revision: None
split: dev
type: msmarco
metrics:
- type: map_at_1
value: 24.091
- type: map_at_10
value: 36.753
- type: map_at_100
value: 37.913000000000004
- type: map_at_1000
value: 37.958999999999996
- type: map_at_3
value: 32.818999999999996
- type: map_at_5
value: 35.171
- type: mrr_at_1
value: 24.742
- type: mrr_at_10
value: 37.285000000000004
- type: mrr_at_100
value: 38.391999999999996
- type: mrr_at_1000
value: 38.431
- type: mrr_at_3
value: 33.440999999999995
- type: mrr_at_5
value: 35.75
- type: ndcg_at_1
value: 24.742
- type: ndcg_at_10
value: 43.698
- type: ndcg_at_100
value: 49.145
- type: ndcg_at_1000
value: 50.23800000000001
- type: ndcg_at_3
value: 35.769
- type: ndcg_at_5
value: 39.961999999999996
- type: precision_at_1
value: 24.742
- type: precision_at_10
value: 6.7989999999999995
- type: precision_at_100
value: 0.95
- type: precision_at_1000
value: 0.104
- type: precision_at_3
value: 15.096000000000002
- type: precision_at_5
value: 11.183
- type: recall_at_1
value: 24.091
- type: recall_at_10
value: 65.068
- type: recall_at_100
value: 89.899
- type: recall_at_1000
value: 98.16
- type: recall_at_3
value: 43.68
- type: recall_at_5
value: 53.754999999999995
task:
type: Retrieval
- dataset:
config: en
name: MTEB MTOPDomainClassification (en)
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
split: test
type: mteb/mtop_domain
metrics:
- type: accuracy
value: 93.66621067031465
- type: f1
value: 93.49622853272142
task:
type: Classification
- dataset:
config: de
name: MTEB MTOPDomainClassification (de)
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
split: test
type: mteb/mtop_domain
metrics:
- type: accuracy
value: 91.94702733164272
- type: f1
value: 91.17043441745282
task:
type: Classification
- dataset:
config: es
name: MTEB MTOPDomainClassification (es)
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
split: test
type: mteb/mtop_domain
metrics:
- type: accuracy
value: 92.20146764509674
- type: f1
value: 91.98359080555608
task:
type: Classification
- dataset:
config: fr
name: MTEB MTOPDomainClassification (fr)
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
split: test
type: mteb/mtop_domain
metrics:
- type: accuracy
value: 88.99780770435328
- type: f1
value: 89.19746342724068
task:
type: Classification
- dataset:
config: hi
name: MTEB MTOPDomainClassification (hi)
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
split: test
type: mteb/mtop_domain
metrics:
- type: accuracy
value: 89.78486912871998
- type: f1
value: 89.24578823628642
task:
type: Classification
- dataset:
config: th
name: MTEB MTOPDomainClassification (th)
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
split: test
type: mteb/mtop_domain
metrics:
- type: accuracy
value: 88.74502712477394
- type: f1
value: 89.00297573881542
task:
type: Classification
- dataset:
config: en
name: MTEB MTOPIntentClassification (en)
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
split: test
type: mteb/mtop_intent
metrics:
- type: accuracy
value: 77.9046967624259
- type: f1
value: 59.36787125785957
task:
type: Classification
- dataset:
config: de
name: MTEB MTOPIntentClassification (de)
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
split: test
type: mteb/mtop_intent
metrics:
- type: accuracy
value: 74.5280360664976
- type: f1
value: 57.17723440888718
task:
type: Classification
- dataset:
config: es
name: MTEB MTOPIntentClassification (es)
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
split: test
type: mteb/mtop_intent
metrics:
- type: accuracy
value: 75.44029352901934
- type: f1
value: 54.052855531072964
task:
type: Classification
- dataset:
config: fr
name: MTEB MTOPIntentClassification (fr)
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
split: test
type: mteb/mtop_intent
metrics:
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value: 70.5606013153774
- type: f1
value: 52.62215934386531
task:
type: Classification
- dataset:
config: hi
name: MTEB MTOPIntentClassification (hi)
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
split: test
type: mteb/mtop_intent
metrics:
- type: accuracy
value: 73.11581211903908
- type: f1
value: 52.341291845645465
task:
type: Classification
- dataset:
config: th
name: MTEB MTOPIntentClassification (th)
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
split: test
type: mteb/mtop_intent
metrics:
- type: accuracy
value: 74.28933092224233
- type: f1
value: 57.07918745504911
task:
type: Classification
- dataset:
config: af
name: MTEB MassiveIntentClassification (af)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 62.38063214525892
- type: f1
value: 59.46463723443009
task:
type: Classification
- dataset:
config: am
name: MTEB MassiveIntentClassification (am)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
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value: 56.06926698049766
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value: 52.49084283283562
task:
type: Classification
- dataset:
config: ar
name: MTEB MassiveIntentClassification (ar)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 60.74983187626093
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value: 56.960640620165904
task:
type: Classification
- dataset:
config: az
name: MTEB MassiveIntentClassification (az)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 64.86550100874243
- type: f1
value: 62.47370548140688
task:
type: Classification
- dataset:
config: bn
name: MTEB MassiveIntentClassification (bn)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
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value: 63.971082716879636
- type: f1
value: 61.03812421957381
task:
type: Classification
- dataset:
config: cy
name: MTEB MassiveIntentClassification (cy)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
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value: 54.98318762609282
- type: f1
value: 51.51207916008392
task:
type: Classification
- dataset:
config: da
name: MTEB MassiveIntentClassification (da)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
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value: 69.45527908540686
- type: f1
value: 66.16631905400318
task:
type: Classification
- dataset:
config: de
name: MTEB MassiveIntentClassification (de)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
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value: 69.32750504371216
- type: f1
value: 66.16755288646591
task:
type: Classification
- dataset:
config: el
name: MTEB MassiveIntentClassification (el)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
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value: 69.09213180901143
- type: f1
value: 66.95654394661507
task:
type: Classification
- dataset:
config: en
name: MTEB MassiveIntentClassification (en)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
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value: 73.75588433086752
- type: f1
value: 71.79973779656923
task:
type: Classification
- dataset:
config: es
name: MTEB MassiveIntentClassification (es)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
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value: 70.49428379287154
- type: f1
value: 68.37494379215734
task:
type: Classification
- dataset:
config: fa
name: MTEB MassiveIntentClassification (fa)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
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value: 69.90921318090115
- type: f1
value: 66.79517376481645
task:
type: Classification
- dataset:
config: fi
name: MTEB MassiveIntentClassification (fi)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
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value: 70.12104909213181
- type: f1
value: 67.29448842879584
task:
type: Classification
- dataset:
config: fr
name: MTEB MassiveIntentClassification (fr)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
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value: 69.34095494283793
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value: 67.01134288992947
task:
type: Classification
- dataset:
config: he
name: MTEB MassiveIntentClassification (he)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
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value: 67.61264290517822
- type: f1
value: 64.68730512660757
task:
type: Classification
- dataset:
config: hi
name: MTEB MassiveIntentClassification (hi)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
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value: 67.79757901815738
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value: 65.24938539425598
task:
type: Classification
- dataset:
config: hu
name: MTEB MassiveIntentClassification (hu)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
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value: 69.68728984532616
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value: 67.0487169762553
task:
type: Classification
- dataset:
config: hy
name: MTEB MassiveIntentClassification (hy)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
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value: 62.07464694014795
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value: 59.183532276789286
task:
type: Classification
- dataset:
config: id
name: MTEB MassiveIntentClassification (id)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
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value: 70.04707464694015
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value: 67.66829629003848
task:
type: Classification
- dataset:
config: is
name: MTEB MassiveIntentClassification (is)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
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value: 62.42434431741762
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value: 59.01617226544757
task:
type: Classification
- dataset:
config: it
name: MTEB MassiveIntentClassification (it)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
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value: 70.53127101546738
- type: f1
value: 68.10033760906255
task:
type: Classification
- dataset:
config: ja
name: MTEB MassiveIntentClassification (ja)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
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value: 72.50504371217215
- type: f1
value: 69.74931103158923
task:
type: Classification
- dataset:
config: jv
name: MTEB MassiveIntentClassification (jv)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
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value: 57.91190316072628
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value: 54.05551136648796
task:
type: Classification
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config: ka
name: MTEB MassiveIntentClassification (ka)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 51.78211163416275
- type: f1
value: 49.874888544058535
task:
type: Classification
- dataset:
config: km
name: MTEB MassiveIntentClassification (km)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
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value: 47.017484868863484
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value: 44.53364263352014
task:
type: Classification
- dataset:
config: kn
name: MTEB MassiveIntentClassification (kn)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
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value: 62.16207128446537
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value: 59.01185692320829
task:
type: Classification
- dataset:
config: ko
name: MTEB MassiveIntentClassification (ko)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
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value: 69.42501681237391
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value: 67.13169450166086
task:
type: Classification
- dataset:
config: lv
name: MTEB MassiveIntentClassification (lv)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
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value: 67.0780094149294
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value: 64.41720167850707
task:
type: Classification
- dataset:
config: ml
name: MTEB MassiveIntentClassification (ml)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
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value: 65.57162071284466
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value: 62.414138683804424
task:
type: Classification
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config: mn
name: MTEB MassiveIntentClassification (mn)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 61.71149966375252
- type: f1
value: 58.594805125087234
task:
type: Classification
- dataset:
config: ms
name: MTEB MassiveIntentClassification (ms)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 66.03900470746471
- type: f1
value: 63.87937257883887
task:
type: Classification
- dataset:
config: my
name: MTEB MassiveIntentClassification (my)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 60.8776059179556
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value: 57.48587618059131
task:
type: Classification
- dataset:
config: nb
name: MTEB MassiveIntentClassification (nb)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
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value: 69.87895090786819
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value: 66.8141299430347
task:
type: Classification
- dataset:
config: nl
name: MTEB MassiveIntentClassification (nl)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
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value: 70.45057162071285
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value: 67.46444039673516
task:
type: Classification
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config: pl
name: MTEB MassiveIntentClassification (pl)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 71.546738399462
- type: f1
value: 68.63640876702655
task:
type: Classification
- dataset:
config: pt
name: MTEB MassiveIntentClassification (pt)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 70.72965702757229
- type: f1
value: 68.54119560379115
task:
type: Classification
- dataset:
config: ro
name: MTEB MassiveIntentClassification (ro)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 68.35574983187625
- type: f1
value: 65.88844917691927
task:
type: Classification
- dataset:
config: ru
name: MTEB MassiveIntentClassification (ru)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
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value: 71.70477471418964
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value: 69.19665697061978
task:
type: Classification
- dataset:
config: sl
name: MTEB MassiveIntentClassification (sl)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
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value: 67.0880968392737
- type: f1
value: 64.76962317666086
task:
type: Classification
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config: sq
name: MTEB MassiveIntentClassification (sq)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
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value: 65.18493611297916
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value: 62.49984559035371
task:
type: Classification
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config: sv
name: MTEB MassiveIntentClassification (sv)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
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value: 71.75857431069265
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value: 69.20053687623418
task:
type: Classification
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config: sw
name: MTEB MassiveIntentClassification (sw)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
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value: 58.500336247478145
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value: 55.2972398687929
task:
type: Classification
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config: ta
name: MTEB MassiveIntentClassification (ta)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 62.68997982515132
- type: f1
value: 59.36848202755348
task:
type: Classification
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config: te
name: MTEB MassiveIntentClassification (te)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 63.01950235373235
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value: 60.09351954625423
task:
type: Classification
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config: th
name: MTEB MassiveIntentClassification (th)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 68.29186281102892
- type: f1
value: 67.57860496703447
task:
type: Classification
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config: tl
name: MTEB MassiveIntentClassification (tl)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 64.77471418964357
- type: f1
value: 61.913983147713836
task:
type: Classification
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config: tr
name: MTEB MassiveIntentClassification (tr)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 69.87222595830532
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value: 66.03679033708141
task:
type: Classification
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config: ur
name: MTEB MassiveIntentClassification (ur)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 64.04505716207127
- type: f1
value: 61.28569169817908
task:
type: Classification
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config: vi
name: MTEB MassiveIntentClassification (vi)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 69.38466711499663
- type: f1
value: 67.20532357036844
task:
type: Classification
- dataset:
config: zh-CN
name: MTEB MassiveIntentClassification (zh-CN)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 71.12306657700067
- type: f1
value: 68.91251226588182
task:
type: Classification
- dataset:
config: zh-TW
name: MTEB MassiveIntentClassification (zh-TW)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 66.20040349697378
- type: f1
value: 66.02657347714175
task:
type: Classification
- dataset:
config: af
name: MTEB MassiveScenarioClassification (af)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 68.73907195696032
- type: f1
value: 66.98484521791418
task:
type: Classification
- dataset:
config: am
name: MTEB MassiveScenarioClassification (am)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 60.58843308675185
- type: f1
value: 58.95591723092005
task:
type: Classification
- dataset:
config: ar
name: MTEB MassiveScenarioClassification (ar)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 66.22730329522528
- type: f1
value: 66.0894499712115
task:
type: Classification
- dataset:
config: az
name: MTEB MassiveScenarioClassification (az)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 66.48285137861465
- type: f1
value: 65.21963176785157
task:
type: Classification
- dataset:
config: bn
name: MTEB MassiveScenarioClassification (bn)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 67.74714189643578
- type: f1
value: 66.8212192745412
task:
type: Classification
- dataset:
config: cy
name: MTEB MassiveScenarioClassification (cy)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 59.09213180901143
- type: f1
value: 56.70735546356339
task:
type: Classification
- dataset:
config: da
name: MTEB MassiveScenarioClassification (da)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 75.05716207128448
- type: f1
value: 74.8413712365364
task:
type: Classification
- dataset:
config: de
name: MTEB MassiveScenarioClassification (de)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 74.69737726967047
- type: f1
value: 74.7664341963
task:
type: Classification
- dataset:
config: el
name: MTEB MassiveScenarioClassification (el)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 73.90383322125084
- type: f1
value: 73.59201554448323
task:
type: Classification
- dataset:
config: en
name: MTEB MassiveScenarioClassification (en)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 77.51176866173503
- type: f1
value: 77.46104434577758
task:
type: Classification
- dataset:
config: es
name: MTEB MassiveScenarioClassification (es)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 74.31069266980496
- type: f1
value: 74.61048660675635
task:
type: Classification
- dataset:
config: fa
name: MTEB MassiveScenarioClassification (fa)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 72.95225285810356
- type: f1
value: 72.33160006574627
task:
type: Classification
- dataset:
config: fi
name: MTEB MassiveScenarioClassification (fi)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 73.12373907195696
- type: f1
value: 73.20921012557481
task:
type: Classification
- dataset:
config: fr
name: MTEB MassiveScenarioClassification (fr)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 73.86684599865501
- type: f1
value: 73.82348774610831
task:
type: Classification
- dataset:
config: he
name: MTEB MassiveScenarioClassification (he)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 71.40215198386012
- type: f1
value: 71.11945183971858
task:
type: Classification
- dataset:
config: hi
name: MTEB MassiveScenarioClassification (hi)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 72.12844653665098
- type: f1
value: 71.34450495911766
task:
type: Classification
- dataset:
config: hu
name: MTEB MassiveScenarioClassification (hu)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 74.52252858103566
- type: f1
value: 73.98878711342999
task:
type: Classification
- dataset:
config: hy
name: MTEB MassiveScenarioClassification (hy)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 64.93611297915265
- type: f1
value: 63.723200467653385
task:
type: Classification
- dataset:
config: id
name: MTEB MassiveScenarioClassification (id)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 74.11903160726295
- type: f1
value: 73.82138439467096
task:
type: Classification
- dataset:
config: is
name: MTEB MassiveScenarioClassification (is)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 67.15198386012105
- type: f1
value: 66.02172193802167
task:
type: Classification
- dataset:
config: it
name: MTEB MassiveScenarioClassification (it)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 74.32414256893072
- type: f1
value: 74.30943421170574
task:
type: Classification
- dataset:
config: ja
name: MTEB MassiveScenarioClassification (ja)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 77.46805648957633
- type: f1
value: 77.62808409298209
task:
type: Classification
- dataset:
config: jv
name: MTEB MassiveScenarioClassification (jv)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 63.318762609280434
- type: f1
value: 62.094284066075076
task:
type: Classification
- dataset:
config: ka
name: MTEB MassiveScenarioClassification (ka)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 58.34902488231338
- type: f1
value: 57.12893860987984
task:
type: Classification
- dataset:
config: km
name: MTEB MassiveScenarioClassification (km)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 50.88433086751849
- type: f1
value: 48.2272350802058
task:
type: Classification
- dataset:
config: kn
name: MTEB MassiveScenarioClassification (kn)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 66.4425016812374
- type: f1
value: 64.61463095996173
task:
type: Classification
- dataset:
config: ko
name: MTEB MassiveScenarioClassification (ko)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 75.04707464694015
- type: f1
value: 75.05099199098998
task:
type: Classification
- dataset:
config: lv
name: MTEB MassiveScenarioClassification (lv)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 70.50437121721586
- type: f1
value: 69.83397721096314
task:
type: Classification
- dataset:
config: ml
name: MTEB MassiveScenarioClassification (ml)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 69.94283792871553
- type: f1
value: 68.8704663703913
task:
type: Classification
- dataset:
config: mn
name: MTEB MassiveScenarioClassification (mn)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 64.79488903833222
- type: f1
value: 63.615424063345436
task:
type: Classification
- dataset:
config: ms
name: MTEB MassiveScenarioClassification (ms)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 69.88231338264963
- type: f1
value: 68.57892302593237
task:
type: Classification
- dataset:
config: my
name: MTEB MassiveScenarioClassification (my)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 63.248150638870214
- type: f1
value: 61.06680605338809
task:
type: Classification
- dataset:
config: nb
name: MTEB MassiveScenarioClassification (nb)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 74.84196368527236
- type: f1
value: 74.52566464968763
task:
type: Classification
- dataset:
config: nl
name: MTEB MassiveScenarioClassification (nl)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 74.8285137861466
- type: f1
value: 74.8853197608802
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 74.13248150638869
- type: f1
value: 74.3982040999179
task:
type: Classification
- dataset:
config: pt
name: MTEB MassiveScenarioClassification (pt)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 73.49024882313383
- type: f1
value: 73.82153848368573
task:
type: Classification
- dataset:
config: ro
name: MTEB MassiveScenarioClassification (ro)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 71.72158708809684
- type: f1
value: 71.85049433180541
task:
type: Classification
- dataset:
config: ru
name: MTEB MassiveScenarioClassification (ru)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 75.137861466039
- type: f1
value: 75.37628348188467
task:
type: Classification
- dataset:
config: sl
name: MTEB MassiveScenarioClassification (sl)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 71.86953597848016
- type: f1
value: 71.87537624521661
task:
type: Classification
- dataset:
config: sq
name: MTEB MassiveScenarioClassification (sq)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 70.27572293207801
- type: f1
value: 68.80017302344231
task:
type: Classification
- dataset:
config: sv
name: MTEB MassiveScenarioClassification (sv)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 76.09952925353059
- type: f1
value: 76.07992707688408
task:
type: Classification
- dataset:
config: sw
name: MTEB MassiveScenarioClassification (sw)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 63.140551445864155
- type: f1
value: 61.73855010331415
task:
type: Classification
- dataset:
config: ta
name: MTEB MassiveScenarioClassification (ta)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 66.27774041694687
- type: f1
value: 64.83664868894539
task:
type: Classification
- dataset:
config: te
name: MTEB MassiveScenarioClassification (te)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 66.69468728984533
- type: f1
value: 64.76239666920868
task:
type: Classification
- dataset:
config: th
name: MTEB MassiveScenarioClassification (th)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 73.44653665097512
- type: f1
value: 73.14646052013873
task:
type: Classification
- dataset:
config: tl
name: MTEB MassiveScenarioClassification (tl)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 67.71351714862139
- type: f1
value: 66.67212180163382
task:
type: Classification
- dataset:
config: tr
name: MTEB MassiveScenarioClassification (tr)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 73.9946200403497
- type: f1
value: 73.87348793725525
task:
type: Classification
- dataset:
config: ur
name: MTEB MassiveScenarioClassification (ur)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 68.15400134498992
- type: f1
value: 67.09433241421094
task:
type: Classification
- dataset:
config: vi
name: MTEB MassiveScenarioClassification (vi)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 73.11365164761264
- type: f1
value: 73.59502539433753
task:
type: Classification
- dataset:
config: zh-CN
name: MTEB MassiveScenarioClassification (zh-CN)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 76.82582380632145
- type: f1
value: 76.89992945316313
task:
type: Classification
- dataset:
config: zh-TW
name: MTEB MassiveScenarioClassification (zh-TW)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 71.81237390719569
- type: f1
value: 72.36499770986265
task:
type: Classification
- dataset:
config: default
name: MTEB MedrxivClusteringP2P
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
split: test
type: mteb/medrxiv-clustering-p2p
metrics:
- type: v_measure
value: 31.480506569594695
task:
type: Clustering
- dataset:
config: default
name: MTEB MedrxivClusteringS2S
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
split: test
type: mteb/medrxiv-clustering-s2s
metrics:
- type: v_measure
value: 29.71252128004552
task:
type: Clustering
- dataset:
config: default
name: MTEB MindSmallReranking
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
split: test
type: mteb/mind_small
metrics:
- type: map
value: 31.421396787056548
- type: mrr
value: 32.48155274872267
task:
type: Reranking
- dataset:
config: default
name: MTEB NFCorpus
revision: None
split: test
type: nfcorpus
metrics:
- type: map_at_1
value: 5.595
- type: map_at_10
value: 12.642000000000001
- type: map_at_100
value: 15.726
- type: map_at_1000
value: 17.061999999999998
- type: map_at_3
value: 9.125
- type: map_at_5
value: 10.866000000000001
- type: mrr_at_1
value: 43.344
- type: mrr_at_10
value: 52.227999999999994
- type: mrr_at_100
value: 52.898999999999994
- type: mrr_at_1000
value: 52.944
- type: mrr_at_3
value: 49.845
- type: mrr_at_5
value: 51.115
- type: ndcg_at_1
value: 41.949999999999996
- type: ndcg_at_10
value: 33.995
- type: ndcg_at_100
value: 30.869999999999997
- type: ndcg_at_1000
value: 39.487
- type: ndcg_at_3
value: 38.903999999999996
- type: ndcg_at_5
value: 37.236999999999995
- type: precision_at_1
value: 43.344
- type: precision_at_10
value: 25.480000000000004
- type: precision_at_100
value: 7.672
- type: precision_at_1000
value: 2.028
- type: precision_at_3
value: 36.636
- type: precision_at_5
value: 32.632
- type: recall_at_1
value: 5.595
- type: recall_at_10
value: 16.466
- type: recall_at_100
value: 31.226
- type: recall_at_1000
value: 62.778999999999996
- type: recall_at_3
value: 9.931
- type: recall_at_5
value: 12.884
task:
type: Retrieval
- dataset:
config: default
name: MTEB NQ
revision: None
split: test
type: nq
metrics:
- type: map_at_1
value: 40.414
- type: map_at_10
value: 56.754000000000005
- type: map_at_100
value: 57.457
- type: map_at_1000
value: 57.477999999999994
- type: map_at_3
value: 52.873999999999995
- type: map_at_5
value: 55.175
- type: mrr_at_1
value: 45.278
- type: mrr_at_10
value: 59.192
- type: mrr_at_100
value: 59.650000000000006
- type: mrr_at_1000
value: 59.665
- type: mrr_at_3
value: 56.141
- type: mrr_at_5
value: 57.998000000000005
- type: ndcg_at_1
value: 45.278
- type: ndcg_at_10
value: 64.056
- type: ndcg_at_100
value: 66.89
- type: ndcg_at_1000
value: 67.364
- type: ndcg_at_3
value: 56.97
- type: ndcg_at_5
value: 60.719
- type: precision_at_1
value: 45.278
- type: precision_at_10
value: 9.994
- type: precision_at_100
value: 1.165
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 25.512
- type: precision_at_5
value: 17.509
- type: recall_at_1
value: 40.414
- type: recall_at_10
value: 83.596
- type: recall_at_100
value: 95.72
- type: recall_at_1000
value: 99.24
- type: recall_at_3
value: 65.472
- type: recall_at_5
value: 74.039
task:
type: Retrieval
- dataset:
config: default
name: MTEB QuoraRetrieval
revision: None
split: test
type: quora
metrics:
- type: map_at_1
value: 70.352
- type: map_at_10
value: 84.369
- type: map_at_100
value: 85.02499999999999
- type: map_at_1000
value: 85.04
- type: map_at_3
value: 81.42399999999999
- type: map_at_5
value: 83.279
- type: mrr_at_1
value: 81.05
- type: mrr_at_10
value: 87.401
- type: mrr_at_100
value: 87.504
- type: mrr_at_1000
value: 87.505
- type: mrr_at_3
value: 86.443
- type: mrr_at_5
value: 87.10799999999999
- type: ndcg_at_1
value: 81.04
- type: ndcg_at_10
value: 88.181
- type: ndcg_at_100
value: 89.411
- type: ndcg_at_1000
value: 89.507
- type: ndcg_at_3
value: 85.28099999999999
- type: ndcg_at_5
value: 86.888
- type: precision_at_1
value: 81.04
- type: precision_at_10
value: 13.406
- type: precision_at_100
value: 1.5350000000000001
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 37.31
- type: precision_at_5
value: 24.54
- type: recall_at_1
value: 70.352
- type: recall_at_10
value: 95.358
- type: recall_at_100
value: 99.541
- type: recall_at_1000
value: 99.984
- type: recall_at_3
value: 87.111
- type: recall_at_5
value: 91.643
task:
type: Retrieval
- dataset:
config: default
name: MTEB RedditClustering
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
split: test
type: mteb/reddit-clustering
metrics:
- type: v_measure
value: 46.54068723291946
task:
type: Clustering
- dataset:
config: default
name: MTEB RedditClusteringP2P
revision: 282350215ef01743dc01b456c7f5241fa8937f16
split: test
type: mteb/reddit-clustering-p2p
metrics:
- type: v_measure
value: 63.216287629895994
task:
type: Clustering
- dataset:
config: default
name: MTEB SCIDOCS
revision: None
split: test
type: scidocs
metrics:
- type: map_at_1
value: 4.023000000000001
- type: map_at_10
value: 10.071
- type: map_at_100
value: 11.892
- type: map_at_1000
value: 12.196
- type: map_at_3
value: 7.234
- type: map_at_5
value: 8.613999999999999
- type: mrr_at_1
value: 19.900000000000002
- type: mrr_at_10
value: 30.516
- type: mrr_at_100
value: 31.656000000000002
- type: mrr_at_1000
value: 31.723000000000003
- type: mrr_at_3
value: 27.400000000000002
- type: mrr_at_5
value: 29.270000000000003
- type: ndcg_at_1
value: 19.900000000000002
- type: ndcg_at_10
value: 17.474
- type: ndcg_at_100
value: 25.020999999999997
- type: ndcg_at_1000
value: 30.728
- type: ndcg_at_3
value: 16.588
- type: ndcg_at_5
value: 14.498
- type: precision_at_1
value: 19.900000000000002
- type: precision_at_10
value: 9.139999999999999
- type: precision_at_100
value: 2.011
- type: precision_at_1000
value: 0.33899999999999997
- type: precision_at_3
value: 15.667
- type: precision_at_5
value: 12.839999999999998
- type: recall_at_1
value: 4.023000000000001
- type: recall_at_10
value: 18.497
- type: recall_at_100
value: 40.8
- type: recall_at_1000
value: 68.812
- type: recall_at_3
value: 9.508
- type: recall_at_5
value: 12.983
task:
type: Retrieval
- dataset:
config: default
name: MTEB SICK-R
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
split: test
type: mteb/sickr-sts
metrics:
- type: cos_sim_pearson
value: 83.967008785134
- type: cos_sim_spearman
value: 80.23142141101837
- type: euclidean_pearson
value: 81.20166064704539
- type: euclidean_spearman
value: 80.18961335654585
- type: manhattan_pearson
value: 81.13925443187625
- type: manhattan_spearman
value: 80.07948723044424
task:
type: STS
- dataset:
config: default
name: MTEB STS12
revision: a0d554a64d88156834ff5ae9920b964011b16384
split: test
type: mteb/sts12-sts
metrics:
- type: cos_sim_pearson
value: 86.94262461316023
- type: cos_sim_spearman
value: 80.01596278563865
- type: euclidean_pearson
value: 83.80799622922581
- type: euclidean_spearman
value: 79.94984954947103
- type: manhattan_pearson
value: 83.68473841756281
- type: manhattan_spearman
value: 79.84990707951822
task:
type: STS
- dataset:
config: default
name: MTEB STS13
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
split: test
type: mteb/sts13-sts
metrics:
- type: cos_sim_pearson
value: 80.57346443146068
- type: cos_sim_spearman
value: 81.54689837570866
- type: euclidean_pearson
value: 81.10909881516007
- type: euclidean_spearman
value: 81.56746243261762
- type: manhattan_pearson
value: 80.87076036186582
- type: manhattan_spearman
value: 81.33074987964402
task:
type: STS
- dataset:
config: default
name: MTEB STS14
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
split: test
type: mteb/sts14-sts
metrics:
- type: cos_sim_pearson
value: 79.54733787179849
- type: cos_sim_spearman
value: 77.72202105610411
- type: euclidean_pearson
value: 78.9043595478849
- type: euclidean_spearman
value: 77.93422804309435
- type: manhattan_pearson
value: 78.58115121621368
- type: manhattan_spearman
value: 77.62508135122033
task:
type: STS
- dataset:
config: default
name: MTEB STS15
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
split: test
type: mteb/sts15-sts
metrics:
- type: cos_sim_pearson
value: 88.59880017237558
- type: cos_sim_spearman
value: 89.31088630824758
- type: euclidean_pearson
value: 88.47069261564656
- type: euclidean_spearman
value: 89.33581971465233
- type: manhattan_pearson
value: 88.40774264100956
- type: manhattan_spearman
value: 89.28657485627835
task:
type: STS
- dataset:
config: default
name: MTEB STS16
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
split: test
type: mteb/sts16-sts
metrics:
- type: cos_sim_pearson
value: 84.08055117917084
- type: cos_sim_spearman
value: 85.78491813080304
- type: euclidean_pearson
value: 84.99329155500392
- type: euclidean_spearman
value: 85.76728064677287
- type: manhattan_pearson
value: 84.87947428989587
- type: manhattan_spearman
value: 85.62429454917464
task:
type: STS
- dataset:
config: ko-ko
name: MTEB STS17 (ko-ko)
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 82.14190939287384
- type: cos_sim_spearman
value: 82.27331573306041
- type: euclidean_pearson
value: 81.891896953716
- type: euclidean_spearman
value: 82.37695542955998
- type: manhattan_pearson
value: 81.73123869460504
- type: manhattan_spearman
value: 82.19989168441421
task:
type: STS
- dataset:
config: ar-ar
name: MTEB STS17 (ar-ar)
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 76.84695301843362
- type: cos_sim_spearman
value: 77.87790986014461
- type: euclidean_pearson
value: 76.91981583106315
- type: euclidean_spearman
value: 77.88154772749589
- type: manhattan_pearson
value: 76.94953277451093
- type: manhattan_spearman
value: 77.80499230728604
task:
type: STS
- dataset:
config: en-ar
name: MTEB STS17 (en-ar)
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 75.44657840482016
- type: cos_sim_spearman
value: 75.05531095119674
- type: euclidean_pearson
value: 75.88161755829299
- type: euclidean_spearman
value: 74.73176238219332
- type: manhattan_pearson
value: 75.63984765635362
- type: manhattan_spearman
value: 74.86476440770737
task:
type: STS
- dataset:
config: en-de
name: MTEB STS17 (en-de)
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 85.64700140524133
- type: cos_sim_spearman
value: 86.16014210425672
- type: euclidean_pearson
value: 86.49086860843221
- type: euclidean_spearman
value: 86.09729326815614
- type: manhattan_pearson
value: 86.43406265125513
- type: manhattan_spearman
value: 86.17740150939994
task:
type: STS
- dataset:
config: en-en
name: MTEB STS17 (en-en)
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 87.91170098764921
- type: cos_sim_spearman
value: 88.12437004058931
- type: euclidean_pearson
value: 88.81828254494437
- type: euclidean_spearman
value: 88.14831794572122
- type: manhattan_pearson
value: 88.93442183448961
- type: manhattan_spearman
value: 88.15254630778304
task:
type: STS
- dataset:
config: en-tr
name: MTEB STS17 (en-tr)
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 72.91390577997292
- type: cos_sim_spearman
value: 71.22979457536074
- type: euclidean_pearson
value: 74.40314008106749
- type: euclidean_spearman
value: 72.54972136083246
- type: manhattan_pearson
value: 73.85687539530218
- type: manhattan_spearman
value: 72.09500771742637
task:
type: STS
- dataset:
config: es-en
name: MTEB STS17 (es-en)
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 80.9301067983089
- type: cos_sim_spearman
value: 80.74989828346473
- type: euclidean_pearson
value: 81.36781301814257
- type: euclidean_spearman
value: 80.9448819964426
- type: manhattan_pearson
value: 81.0351322685609
- type: manhattan_spearman
value: 80.70192121844177
task:
type: STS
- dataset:
config: es-es
name: MTEB STS17 (es-es)
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 87.13820465980005
- type: cos_sim_spearman
value: 86.73532498758757
- type: euclidean_pearson
value: 87.21329451846637
- type: euclidean_spearman
value: 86.57863198601002
- type: manhattan_pearson
value: 87.06973713818554
- type: manhattan_spearman
value: 86.47534918791499
task:
type: STS
- dataset:
config: fr-en
name: MTEB STS17 (fr-en)
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 85.48720108904415
- type: cos_sim_spearman
value: 85.62221757068387
- type: euclidean_pearson
value: 86.1010129512749
- type: euclidean_spearman
value: 85.86580966509942
- type: manhattan_pearson
value: 86.26800938808971
- type: manhattan_spearman
value: 85.88902721678429
task:
type: STS
- dataset:
config: it-en
name: MTEB STS17 (it-en)
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 83.98021347333516
- type: cos_sim_spearman
value: 84.53806553803501
- type: euclidean_pearson
value: 84.61483347248364
- type: euclidean_spearman
value: 85.14191408011702
- type: manhattan_pearson
value: 84.75297588825967
- type: manhattan_spearman
value: 85.33176753669242
task:
type: STS
- dataset:
config: nl-en
name: MTEB STS17 (nl-en)
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 84.51856644893233
- type: cos_sim_spearman
value: 85.27510748506413
- type: euclidean_pearson
value: 85.09886861540977
- type: euclidean_spearman
value: 85.62579245860887
- type: manhattan_pearson
value: 84.93017860464607
- type: manhattan_spearman
value: 85.5063988898453
task:
type: STS
- dataset:
config: en
name: MTEB STS22 (en)
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 62.581573200584195
- type: cos_sim_spearman
value: 63.05503590247928
- type: euclidean_pearson
value: 63.652564812602094
- type: euclidean_spearman
value: 62.64811520876156
- type: manhattan_pearson
value: 63.506842893061076
- type: manhattan_spearman
value: 62.51289573046917
task:
type: STS
- dataset:
config: de
name: MTEB STS22 (de)
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 48.2248801729127
- type: cos_sim_spearman
value: 56.5936604678561
- type: euclidean_pearson
value: 43.98149464089
- type: euclidean_spearman
value: 56.108561882423615
- type: manhattan_pearson
value: 43.86880305903564
- type: manhattan_spearman
value: 56.04671150510166
task:
type: STS
- dataset:
config: es
name: MTEB STS22 (es)
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 55.17564527009831
- type: cos_sim_spearman
value: 64.57978560979488
- type: euclidean_pearson
value: 58.8818330154583
- type: euclidean_spearman
value: 64.99214839071281
- type: manhattan_pearson
value: 58.72671436121381
- type: manhattan_spearman
value: 65.10713416616109
task:
type: STS
- dataset:
config: pl
name: MTEB STS22 (pl)
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 26.772131864023297
- type: cos_sim_spearman
value: 34.68200792408681
- type: euclidean_pearson
value: 16.68082419005441
- type: euclidean_spearman
value: 34.83099932652166
- type: manhattan_pearson
value: 16.52605949659529
- type: manhattan_spearman
value: 34.82075801399475
task:
type: STS
- dataset:
config: tr
name: MTEB STS22 (tr)
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 54.42415189043831
- type: cos_sim_spearman
value: 63.54594264576758
- type: euclidean_pearson
value: 57.36577498297745
- type: euclidean_spearman
value: 63.111466379158074
- type: manhattan_pearson
value: 57.584543715873885
- type: manhattan_spearman
value: 63.22361054139183
task:
type: STS
- dataset:
config: ar
name: MTEB STS22 (ar)
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 47.55216762405518
- type: cos_sim_spearman
value: 56.98670142896412
- type: euclidean_pearson
value: 50.15318757562699
- type: euclidean_spearman
value: 56.524941926541906
- type: manhattan_pearson
value: 49.955618528674904
- type: manhattan_spearman
value: 56.37102209240117
task:
type: STS
- dataset:
config: ru
name: MTEB STS22 (ru)
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 49.20540980338571
- type: cos_sim_spearman
value: 59.9009453504406
- type: euclidean_pearson
value: 49.557749853620535
- type: euclidean_spearman
value: 59.76631621172456
- type: manhattan_pearson
value: 49.62340591181147
- type: manhattan_spearman
value: 59.94224880322436
task:
type: STS
- dataset:
config: zh
name: MTEB STS22 (zh)
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 51.508169956576985
- type: cos_sim_spearman
value: 66.82461565306046
- type: euclidean_pearson
value: 56.2274426480083
- type: euclidean_spearman
value: 66.6775323848333
- type: manhattan_pearson
value: 55.98277796300661
- type: manhattan_spearman
value: 66.63669848497175
task:
type: STS
- dataset:
config: fr
name: MTEB STS22 (fr)
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 72.86478788045507
- type: cos_sim_spearman
value: 76.7946552053193
- type: euclidean_pearson
value: 75.01598530490269
- type: euclidean_spearman
value: 76.83618917858281
- type: manhattan_pearson
value: 74.68337628304332
- type: manhattan_spearman
value: 76.57480204017773
task:
type: STS
- dataset:
config: de-en
name: MTEB STS22 (de-en)
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 55.922619099401984
- type: cos_sim_spearman
value: 56.599362477240774
- type: euclidean_pearson
value: 56.68307052369783
- type: euclidean_spearman
value: 54.28760436777401
- type: manhattan_pearson
value: 56.67763566500681
- type: manhattan_spearman
value: 53.94619541711359
task:
type: STS
- dataset:
config: es-en
name: MTEB STS22 (es-en)
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 66.74357206710913
- type: cos_sim_spearman
value: 72.5208244925311
- type: euclidean_pearson
value: 67.49254562186032
- type: euclidean_spearman
value: 72.02469076238683
- type: manhattan_pearson
value: 67.45251772238085
- type: manhattan_spearman
value: 72.05538819984538
task:
type: STS
- dataset:
config: it
name: MTEB STS22 (it)
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 71.25734330033191
- type: cos_sim_spearman
value: 76.98349083946823
- type: euclidean_pearson
value: 73.71642838667736
- type: euclidean_spearman
value: 77.01715504651384
- type: manhattan_pearson
value: 73.61712711868105
- type: manhattan_spearman
value: 77.01392571153896
task:
type: STS
- dataset:
config: pl-en
name: MTEB STS22 (pl-en)
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 63.18215462781212
- type: cos_sim_spearman
value: 65.54373266117607
- type: euclidean_pearson
value: 64.54126095439005
- type: euclidean_spearman
value: 65.30410369102711
- type: manhattan_pearson
value: 63.50332221148234
- type: manhattan_spearman
value: 64.3455878104313
task:
type: STS
- dataset:
config: zh-en
name: MTEB STS22 (zh-en)
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 62.30509221440029
- type: cos_sim_spearman
value: 65.99582704642478
- type: euclidean_pearson
value: 63.43818859884195
- type: euclidean_spearman
value: 66.83172582815764
- type: manhattan_pearson
value: 63.055779168508764
- type: manhattan_spearman
value: 65.49585020501449
task:
type: STS
- dataset:
config: es-it
name: MTEB STS22 (es-it)
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 59.587830825340404
- type: cos_sim_spearman
value: 68.93467614588089
- type: euclidean_pearson
value: 62.3073527367404
- type: euclidean_spearman
value: 69.69758171553175
- type: manhattan_pearson
value: 61.9074580815789
- type: manhattan_spearman
value: 69.57696375597865
task:
type: STS
- dataset:
config: de-fr
name: MTEB STS22 (de-fr)
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 57.143220125577066
- type: cos_sim_spearman
value: 67.78857859159226
- type: euclidean_pearson
value: 55.58225107923733
- type: euclidean_spearman
value: 67.80662907184563
- type: manhattan_pearson
value: 56.24953502726514
- type: manhattan_spearman
value: 67.98262125431616
task:
type: STS
- dataset:
config: de-pl
name: MTEB STS22 (de-pl)
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 21.826928900322066
- type: cos_sim_spearman
value: 49.578506634400405
- type: euclidean_pearson
value: 27.939890138843214
- type: euclidean_spearman
value: 52.71950519136242
- type: manhattan_pearson
value: 26.39878683847546
- type: manhattan_spearman
value: 47.54609580342499
task:
type: STS
- dataset:
config: fr-pl
name: MTEB STS22 (fr-pl)
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_pearson
value: 57.27603854632001
- type: cos_sim_spearman
value: 50.709255283710995
- type: euclidean_pearson
value: 59.5419024445929
- type: euclidean_spearman
value: 50.709255283710995
- type: manhattan_pearson
value: 59.03256832438492
- type: manhattan_spearman
value: 61.97797868009122
task:
type: STS
- dataset:
config: default
name: MTEB STSBenchmark
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
split: test
type: mteb/stsbenchmark-sts
metrics:
- type: cos_sim_pearson
value: 85.00757054859712
- type: cos_sim_spearman
value: 87.29283629622222
- type: euclidean_pearson
value: 86.54824171775536
- type: euclidean_spearman
value: 87.24364730491402
- type: manhattan_pearson
value: 86.5062156915074
- type: manhattan_spearman
value: 87.15052170378574
task:
type: STS
- dataset:
config: default
name: MTEB SciDocsRR
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
split: test
type: mteb/scidocs-reranking
metrics:
- type: map
value: 82.03549357197389
- type: mrr
value: 95.05437645143527
task:
type: Reranking
- dataset:
config: default
name: MTEB SciFact
revision: None
split: test
type: scifact
metrics:
- type: map_at_1
value: 57.260999999999996
- type: map_at_10
value: 66.259
- type: map_at_100
value: 66.884
- type: map_at_1000
value: 66.912
- type: map_at_3
value: 63.685
- type: map_at_5
value: 65.35499999999999
- type: mrr_at_1
value: 60.333000000000006
- type: mrr_at_10
value: 67.5
- type: mrr_at_100
value: 68.013
- type: mrr_at_1000
value: 68.038
- type: mrr_at_3
value: 65.61099999999999
- type: mrr_at_5
value: 66.861
- type: ndcg_at_1
value: 60.333000000000006
- type: ndcg_at_10
value: 70.41
- type: ndcg_at_100
value: 73.10600000000001
- type: ndcg_at_1000
value: 73.846
- type: ndcg_at_3
value: 66.133
- type: ndcg_at_5
value: 68.499
- type: precision_at_1
value: 60.333000000000006
- type: precision_at_10
value: 9.232999999999999
- type: precision_at_100
value: 1.0630000000000002
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 25.667
- type: precision_at_5
value: 17.067
- type: recall_at_1
value: 57.260999999999996
- type: recall_at_10
value: 81.94399999999999
- type: recall_at_100
value: 93.867
- type: recall_at_1000
value: 99.667
- type: recall_at_3
value: 70.339
- type: recall_at_5
value: 76.25
task:
type: Retrieval
- dataset:
config: default
name: MTEB SprintDuplicateQuestions
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
split: test
type: mteb/sprintduplicatequestions-pairclassification
metrics:
- type: cos_sim_accuracy
value: 99.74356435643564
- type: cos_sim_ap
value: 93.13411948212683
- type: cos_sim_f1
value: 86.80521991300147
- type: cos_sim_precision
value: 84.00374181478017
- type: cos_sim_recall
value: 89.8
- type: dot_accuracy
value: 99.67920792079208
- type: dot_ap
value: 89.27277565444479
- type: dot_f1
value: 83.9276990718124
- type: dot_precision
value: 82.04393505253104
- type: dot_recall
value: 85.9
- type: euclidean_accuracy
value: 99.74257425742574
- type: euclidean_ap
value: 93.17993008259062
- type: euclidean_f1
value: 86.69396110542476
- type: euclidean_precision
value: 88.78406708595388
- type: euclidean_recall
value: 84.7
- type: manhattan_accuracy
value: 99.74257425742574
- type: manhattan_ap
value: 93.14413755550099
- type: manhattan_f1
value: 86.82483594144371
- type: manhattan_precision
value: 87.66564729867483
- type: manhattan_recall
value: 86
- type: max_accuracy
value: 99.74356435643564
- type: max_ap
value: 93.17993008259062
- type: max_f1
value: 86.82483594144371
task:
type: PairClassification
- dataset:
config: default
name: MTEB StackExchangeClustering
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
split: test
type: mteb/stackexchange-clustering
metrics:
- type: v_measure
value: 57.525863806168566
task:
type: Clustering
- dataset:
config: default
name: MTEB StackExchangeClusteringP2P
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
split: test
type: mteb/stackexchange-clustering-p2p
metrics:
- type: v_measure
value: 32.68850574423839
task:
type: Clustering
- dataset:
config: default
name: MTEB StackOverflowDupQuestions
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
split: test
type: mteb/stackoverflowdupquestions-reranking
metrics:
- type: map
value: 49.71580650644033
- type: mrr
value: 50.50971903913081
task:
type: Reranking
- dataset:
config: default
name: MTEB SummEval
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
split: test
type: mteb/summeval
metrics:
- type: cos_sim_pearson
value: 29.152190498799484
- type: cos_sim_spearman
value: 29.686180371952727
- type: dot_pearson
value: 27.248664793816342
- type: dot_spearman
value: 28.37748983721745
task:
type: Summarization
- dataset:
config: default
name: MTEB TRECCOVID
revision: None
split: test
type: trec-covid
metrics:
- type: map_at_1
value: 0.20400000000000001
- type: map_at_10
value: 1.6209999999999998
- type: map_at_100
value: 9.690999999999999
- type: map_at_1000
value: 23.733
- type: map_at_3
value: 0.575
- type: map_at_5
value: 0.885
- type: mrr_at_1
value: 78
- type: mrr_at_10
value: 86.56700000000001
- type: mrr_at_100
value: 86.56700000000001
- type: mrr_at_1000
value: 86.56700000000001
- type: mrr_at_3
value: 85.667
- type: mrr_at_5
value: 86.56700000000001
- type: ndcg_at_1
value: 76
- type: ndcg_at_10
value: 71.326
- type: ndcg_at_100
value: 54.208999999999996
- type: ndcg_at_1000
value: 49.252
- type: ndcg_at_3
value: 74.235
- type: ndcg_at_5
value: 73.833
- type: precision_at_1
value: 78
- type: precision_at_10
value: 74.8
- type: precision_at_100
value: 55.50000000000001
- type: precision_at_1000
value: 21.836
- type: precision_at_3
value: 78
- type: precision_at_5
value: 78
- type: recall_at_1
value: 0.20400000000000001
- type: recall_at_10
value: 1.894
- type: recall_at_100
value: 13.245999999999999
- type: recall_at_1000
value: 46.373
- type: recall_at_3
value: 0.613
- type: recall_at_5
value: 0.991
task:
type: Retrieval
- dataset:
config: sqi-eng
name: MTEB Tatoeba (sqi-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 95.89999999999999
- type: f1
value: 94.69999999999999
- type: precision
value: 94.11666666666667
- type: recall
value: 95.89999999999999
task:
type: BitextMining
- dataset:
config: fry-eng
name: MTEB Tatoeba (fry-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 68.20809248554913
- type: f1
value: 63.431048720066066
- type: precision
value: 61.69143958161298
- type: recall
value: 68.20809248554913
task:
type: BitextMining
- dataset:
config: kur-eng
name: MTEB Tatoeba (kur-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 71.21951219512195
- type: f1
value: 66.82926829268293
- type: precision
value: 65.1260162601626
- type: recall
value: 71.21951219512195
task:
type: BitextMining
- dataset:
config: tur-eng
name: MTEB Tatoeba (tur-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 97.2
- type: f1
value: 96.26666666666667
- type: precision
value: 95.8
- type: recall
value: 97.2
task:
type: BitextMining
- dataset:
config: deu-eng
name: MTEB Tatoeba (deu-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 99.3
- type: f1
value: 99.06666666666666
- type: precision
value: 98.95
- type: recall
value: 99.3
task:
type: BitextMining
- dataset:
config: nld-eng
name: MTEB Tatoeba (nld-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 97.39999999999999
- type: f1
value: 96.63333333333333
- type: precision
value: 96.26666666666668
- type: recall
value: 97.39999999999999
task:
type: BitextMining
- dataset:
config: ron-eng
name: MTEB Tatoeba (ron-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 96
- type: f1
value: 94.86666666666666
- type: precision
value: 94.31666666666668
- type: recall
value: 96
task:
type: BitextMining
- dataset:
config: ang-eng
name: MTEB Tatoeba (ang-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 47.01492537313433
- type: f1
value: 40.178867566927266
- type: precision
value: 38.179295828549556
- type: recall
value: 47.01492537313433
task:
type: BitextMining
- dataset:
config: ido-eng
name: MTEB Tatoeba (ido-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 86.5
- type: f1
value: 83.62537480063796
- type: precision
value: 82.44555555555554
- type: recall
value: 86.5
task:
type: BitextMining
- dataset:
config: jav-eng
name: MTEB Tatoeba (jav-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 80.48780487804879
- type: f1
value: 75.45644599303138
- type: precision
value: 73.37398373983739
- type: recall
value: 80.48780487804879
task:
type: BitextMining
- dataset:
config: isl-eng
name: MTEB Tatoeba (isl-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 93.7
- type: f1
value: 91.95666666666666
- type: precision
value: 91.125
- type: recall
value: 93.7
task:
type: BitextMining
- dataset:
config: slv-eng
name: MTEB Tatoeba (slv-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 91.73754556500607
- type: f1
value: 89.65168084244632
- type: precision
value: 88.73025516403402
- type: recall
value: 91.73754556500607
task:
type: BitextMining
- dataset:
config: cym-eng
name: MTEB Tatoeba (cym-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 81.04347826086956
- type: f1
value: 76.2128364389234
- type: precision
value: 74.2
- type: recall
value: 81.04347826086956
task:
type: BitextMining
- dataset:
config: kaz-eng
name: MTEB Tatoeba (kaz-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 83.65217391304348
- type: f1
value: 79.4376811594203
- type: precision
value: 77.65797101449274
- type: recall
value: 83.65217391304348
task:
type: BitextMining
- dataset:
config: est-eng
name: MTEB Tatoeba (est-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 87.5
- type: f1
value: 85.02690476190476
- type: precision
value: 83.96261904761904
- type: recall
value: 87.5
task:
type: BitextMining
- dataset:
config: heb-eng
name: MTEB Tatoeba (heb-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 89.3
- type: f1
value: 86.52333333333333
- type: precision
value: 85.22833333333332
- type: recall
value: 89.3
task:
type: BitextMining
- dataset:
config: gla-eng
name: MTEB Tatoeba (gla-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 65.01809408926418
- type: f1
value: 59.00594446432805
- type: precision
value: 56.827215807915444
- type: recall
value: 65.01809408926418
task:
type: BitextMining
- dataset:
config: mar-eng
name: MTEB Tatoeba (mar-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 91.2
- type: f1
value: 88.58
- type: precision
value: 87.33333333333334
- type: recall
value: 91.2
task:
type: BitextMining
- dataset:
config: lat-eng
name: MTEB Tatoeba (lat-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 59.199999999999996
- type: f1
value: 53.299166276284915
- type: precision
value: 51.3383908045977
- type: recall
value: 59.199999999999996
task:
type: BitextMining
- dataset:
config: bel-eng
name: MTEB Tatoeba (bel-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 93.2
- type: f1
value: 91.2
- type: precision
value: 90.25
- type: recall
value: 93.2
task:
type: BitextMining
- dataset:
config: pms-eng
name: MTEB Tatoeba (pms-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 64.76190476190476
- type: f1
value: 59.867110667110666
- type: precision
value: 58.07390192653351
- type: recall
value: 64.76190476190476
task:
type: BitextMining
- dataset:
config: gle-eng
name: MTEB Tatoeba (gle-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 76.2
- type: f1
value: 71.48147546897547
- type: precision
value: 69.65409090909091
- type: recall
value: 76.2
task:
type: BitextMining
- dataset:
config: pes-eng
name: MTEB Tatoeba (pes-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 93.8
- type: f1
value: 92.14
- type: precision
value: 91.35833333333333
- type: recall
value: 93.8
task:
type: BitextMining
- dataset:
config: nob-eng
name: MTEB Tatoeba (nob-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 97.89999999999999
- type: f1
value: 97.2
- type: precision
value: 96.85000000000001
- type: recall
value: 97.89999999999999
task:
type: BitextMining
- dataset:
config: bul-eng
name: MTEB Tatoeba (bul-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 94.6
- type: f1
value: 92.93333333333334
- type: precision
value: 92.13333333333333
- type: recall
value: 94.6
task:
type: BitextMining
- dataset:
config: cbk-eng
name: MTEB Tatoeba (cbk-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 74.1
- type: f1
value: 69.14817460317461
- type: precision
value: 67.2515873015873
- type: recall
value: 74.1
task:
type: BitextMining
- dataset:
config: hun-eng
name: MTEB Tatoeba (hun-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 95.19999999999999
- type: f1
value: 94.01333333333335
- type: precision
value: 93.46666666666667
- type: recall
value: 95.19999999999999
task:
type: BitextMining
- dataset:
config: uig-eng
name: MTEB Tatoeba (uig-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 76.9
- type: f1
value: 72.07523809523809
- type: precision
value: 70.19777777777779
- type: recall
value: 76.9
task:
type: BitextMining
- dataset:
config: rus-eng
name: MTEB Tatoeba (rus-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 94.1
- type: f1
value: 92.31666666666666
- type: precision
value: 91.43333333333332
- type: recall
value: 94.1
task:
type: BitextMining
- dataset:
config: spa-eng
name: MTEB Tatoeba (spa-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 97.8
- type: f1
value: 97.1
- type: precision
value: 96.76666666666668
- type: recall
value: 97.8
task:
type: BitextMining
- dataset:
config: hye-eng
name: MTEB Tatoeba (hye-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 92.85714285714286
- type: f1
value: 90.92093441150045
- type: precision
value: 90.00449236298293
- type: recall
value: 92.85714285714286
task:
type: BitextMining
- dataset:
config: tel-eng
name: MTEB Tatoeba (tel-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 93.16239316239316
- type: f1
value: 91.33903133903132
- type: precision
value: 90.56267806267806
- type: recall
value: 93.16239316239316
task:
type: BitextMining
- dataset:
config: afr-eng
name: MTEB Tatoeba (afr-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 92.4
- type: f1
value: 90.25666666666666
- type: precision
value: 89.25833333333334
- type: recall
value: 92.4
task:
type: BitextMining
- dataset:
config: mon-eng
name: MTEB Tatoeba (mon-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 90.22727272727272
- type: f1
value: 87.53030303030303
- type: precision
value: 86.37121212121211
- type: recall
value: 90.22727272727272
task:
type: BitextMining
- dataset:
config: arz-eng
name: MTEB Tatoeba (arz-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 79.03563941299791
- type: f1
value: 74.7349505840072
- type: precision
value: 72.9035639412998
- type: recall
value: 79.03563941299791
task:
type: BitextMining
- dataset:
config: hrv-eng
name: MTEB Tatoeba (hrv-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 97
- type: f1
value: 96.15
- type: precision
value: 95.76666666666668
- type: recall
value: 97
task:
type: BitextMining
- dataset:
config: nov-eng
name: MTEB Tatoeba (nov-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 76.26459143968872
- type: f1
value: 71.55642023346303
- type: precision
value: 69.7544932369835
- type: recall
value: 76.26459143968872
task:
type: BitextMining
- dataset:
config: gsw-eng
name: MTEB Tatoeba (gsw-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 58.119658119658126
- type: f1
value: 51.65242165242165
- type: precision
value: 49.41768108434775
- type: recall
value: 58.119658119658126
task:
type: BitextMining
- dataset:
config: nds-eng
name: MTEB Tatoeba (nds-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 74.3
- type: f1
value: 69.52055555555555
- type: precision
value: 67.7574938949939
- type: recall
value: 74.3
task:
type: BitextMining
- dataset:
config: ukr-eng
name: MTEB Tatoeba (ukr-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 94.8
- type: f1
value: 93.31666666666666
- type: precision
value: 92.60000000000001
- type: recall
value: 94.8
task:
type: BitextMining
- dataset:
config: uzb-eng
name: MTEB Tatoeba (uzb-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 76.63551401869158
- type: f1
value: 72.35202492211837
- type: precision
value: 70.60358255451713
- type: recall
value: 76.63551401869158
task:
type: BitextMining
- dataset:
config: lit-eng
name: MTEB Tatoeba (lit-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 90.4
- type: f1
value: 88.4811111111111
- type: precision
value: 87.7452380952381
- type: recall
value: 90.4
task:
type: BitextMining
- dataset:
config: ina-eng
name: MTEB Tatoeba (ina-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 95
- type: f1
value: 93.60666666666667
- type: precision
value: 92.975
- type: recall
value: 95
task:
type: BitextMining
- dataset:
config: lfn-eng
name: MTEB Tatoeba (lfn-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 67.2
- type: f1
value: 63.01595782872099
- type: precision
value: 61.596587301587306
- type: recall
value: 67.2
task:
type: BitextMining
- dataset:
config: zsm-eng
name: MTEB Tatoeba (zsm-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 95.7
- type: f1
value: 94.52999999999999
- type: precision
value: 94
- type: recall
value: 95.7
task:
type: BitextMining
- dataset:
config: ita-eng
name: MTEB Tatoeba (ita-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 94.6
- type: f1
value: 93.28999999999999
- type: precision
value: 92.675
- type: recall
value: 94.6
task:
type: BitextMining
- dataset:
config: cmn-eng
name: MTEB Tatoeba (cmn-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 96.39999999999999
- type: f1
value: 95.28333333333333
- type: precision
value: 94.75
- type: recall
value: 96.39999999999999
task:
type: BitextMining
- dataset:
config: lvs-eng
name: MTEB Tatoeba (lvs-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 91.9
- type: f1
value: 89.83
- type: precision
value: 88.92
- type: recall
value: 91.9
task:
type: BitextMining
- dataset:
config: glg-eng
name: MTEB Tatoeba (glg-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 94.69999999999999
- type: f1
value: 93.34222222222223
- type: precision
value: 92.75416666666668
- type: recall
value: 94.69999999999999
task:
type: BitextMining
- dataset:
config: ceb-eng
name: MTEB Tatoeba (ceb-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 60.333333333333336
- type: f1
value: 55.31203703703703
- type: precision
value: 53.39971108326371
- type: recall
value: 60.333333333333336
task:
type: BitextMining
- dataset:
config: bre-eng
name: MTEB Tatoeba (bre-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 12.9
- type: f1
value: 11.099861903031458
- type: precision
value: 10.589187932631877
- type: recall
value: 12.9
task:
type: BitextMining
- dataset:
config: ben-eng
name: MTEB Tatoeba (ben-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 86.7
- type: f1
value: 83.0152380952381
- type: precision
value: 81.37833333333333
- type: recall
value: 86.7
task:
type: BitextMining
- dataset:
config: swg-eng
name: MTEB Tatoeba (swg-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 63.39285714285714
- type: f1
value: 56.832482993197274
- type: precision
value: 54.56845238095237
- type: recall
value: 63.39285714285714
task:
type: BitextMining
- dataset:
config: arq-eng
name: MTEB Tatoeba (arq-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 48.73765093304062
- type: f1
value: 41.555736920720456
- type: precision
value: 39.06874531737319
- type: recall
value: 48.73765093304062
task:
type: BitextMining
- dataset:
config: kab-eng
name: MTEB Tatoeba (kab-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 41.099999999999994
- type: f1
value: 36.540165945165946
- type: precision
value: 35.05175685425686
- type: recall
value: 41.099999999999994
task:
type: BitextMining
- dataset:
config: fra-eng
name: MTEB Tatoeba (fra-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 94.89999999999999
- type: f1
value: 93.42333333333333
- type: precision
value: 92.75833333333333
- type: recall
value: 94.89999999999999
task:
type: BitextMining
- dataset:
config: por-eng
name: MTEB Tatoeba (por-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 94.89999999999999
- type: f1
value: 93.63333333333334
- type: precision
value: 93.01666666666665
- type: recall
value: 94.89999999999999
task:
type: BitextMining
- dataset:
config: tat-eng
name: MTEB Tatoeba (tat-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 77.9
- type: f1
value: 73.64833333333334
- type: precision
value: 71.90282106782105
- type: recall
value: 77.9
task:
type: BitextMining
- dataset:
config: oci-eng
name: MTEB Tatoeba (oci-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 59.4
- type: f1
value: 54.90521367521367
- type: precision
value: 53.432840025471606
- type: recall
value: 59.4
task:
type: BitextMining
- dataset:
config: pol-eng
name: MTEB Tatoeba (pol-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 97.39999999999999
- type: f1
value: 96.6
- type: precision
value: 96.2
- type: recall
value: 97.39999999999999
task:
type: BitextMining
- dataset:
config: war-eng
name: MTEB Tatoeba (war-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 67.2
- type: f1
value: 62.25926129426129
- type: precision
value: 60.408376623376626
- type: recall
value: 67.2
task:
type: BitextMining
- dataset:
config: aze-eng
name: MTEB Tatoeba (aze-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 90.2
- type: f1
value: 87.60666666666667
- type: precision
value: 86.45277777777778
- type: recall
value: 90.2
task:
type: BitextMining
- dataset:
config: vie-eng
name: MTEB Tatoeba (vie-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 97.7
- type: f1
value: 97
- type: precision
value: 96.65
- type: recall
value: 97.7
task:
type: BitextMining
- dataset:
config: nno-eng
name: MTEB Tatoeba (nno-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 93.2
- type: f1
value: 91.39746031746031
- type: precision
value: 90.6125
- type: recall
value: 93.2
task:
type: BitextMining
- dataset:
config: cha-eng
name: MTEB Tatoeba (cha-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 32.11678832116788
- type: f1
value: 27.210415386260234
- type: precision
value: 26.20408990846947
- type: recall
value: 32.11678832116788
task:
type: BitextMining
- dataset:
config: mhr-eng
name: MTEB Tatoeba (mhr-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 8.5
- type: f1
value: 6.787319277832475
- type: precision
value: 6.3452094433344435
- type: recall
value: 8.5
task:
type: BitextMining
- dataset:
config: dan-eng
name: MTEB Tatoeba (dan-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 96.1
- type: f1
value: 95.08
- type: precision
value: 94.61666666666667
- type: recall
value: 96.1
task:
type: BitextMining
- dataset:
config: ell-eng
name: MTEB Tatoeba (ell-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 95.3
- type: f1
value: 93.88333333333333
- type: precision
value: 93.18333333333332
- type: recall
value: 95.3
task:
type: BitextMining
- dataset:
config: amh-eng
name: MTEB Tatoeba (amh-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 85.11904761904762
- type: f1
value: 80.69444444444444
- type: precision
value: 78.72023809523809
- type: recall
value: 85.11904761904762
task:
type: BitextMining
- dataset:
config: pam-eng
name: MTEB Tatoeba (pam-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 11.1
- type: f1
value: 9.276381801735853
- type: precision
value: 8.798174603174601
- type: recall
value: 11.1
task:
type: BitextMining
- dataset:
config: hsb-eng
name: MTEB Tatoeba (hsb-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 63.56107660455487
- type: f1
value: 58.70433569191332
- type: precision
value: 56.896926581464015
- type: recall
value: 63.56107660455487
task:
type: BitextMining
- dataset:
config: srp-eng
name: MTEB Tatoeba (srp-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 94.69999999999999
- type: f1
value: 93.10000000000001
- type: precision
value: 92.35
- type: recall
value: 94.69999999999999
task:
type: BitextMining
- dataset:
config: epo-eng
name: MTEB Tatoeba (epo-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 96.8
- type: f1
value: 96.01222222222222
- type: precision
value: 95.67083333333332
- type: recall
value: 96.8
task:
type: BitextMining
- dataset:
config: kzj-eng
name: MTEB Tatoeba (kzj-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 9.2
- type: f1
value: 7.911555250305249
- type: precision
value: 7.631246556216846
- type: recall
value: 9.2
task:
type: BitextMining
- dataset:
config: awa-eng
name: MTEB Tatoeba (awa-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 77.48917748917748
- type: f1
value: 72.27375798804371
- type: precision
value: 70.14430014430013
- type: recall
value: 77.48917748917748
task:
type: BitextMining
- dataset:
config: fao-eng
name: MTEB Tatoeba (fao-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 77.09923664122137
- type: f1
value: 72.61541257724463
- type: precision
value: 70.8998380754106
- type: recall
value: 77.09923664122137
task:
type: BitextMining
- dataset:
config: mal-eng
name: MTEB Tatoeba (mal-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 98.2532751091703
- type: f1
value: 97.69529354682193
- type: precision
value: 97.42843279961184
- type: recall
value: 98.2532751091703
task:
type: BitextMining
- dataset:
config: ile-eng
name: MTEB Tatoeba (ile-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 82.8
- type: f1
value: 79.14672619047619
- type: precision
value: 77.59489247311828
- type: recall
value: 82.8
task:
type: BitextMining
- dataset:
config: bos-eng
name: MTEB Tatoeba (bos-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 94.35028248587571
- type: f1
value: 92.86252354048965
- type: precision
value: 92.2080979284369
- type: recall
value: 94.35028248587571
task:
type: BitextMining
- dataset:
config: cor-eng
name: MTEB Tatoeba (cor-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 8.5
- type: f1
value: 6.282429263935621
- type: precision
value: 5.783274240739785
- type: recall
value: 8.5
task:
type: BitextMining
- dataset:
config: cat-eng
name: MTEB Tatoeba (cat-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 92.7
- type: f1
value: 91.025
- type: precision
value: 90.30428571428571
- type: recall
value: 92.7
task:
type: BitextMining
- dataset:
config: eus-eng
name: MTEB Tatoeba (eus-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 81
- type: f1
value: 77.8232380952381
- type: precision
value: 76.60194444444444
- type: recall
value: 81
task:
type: BitextMining
- dataset:
config: yue-eng
name: MTEB Tatoeba (yue-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 91
- type: f1
value: 88.70857142857142
- type: precision
value: 87.7
- type: recall
value: 91
task:
type: BitextMining
- dataset:
config: swe-eng
name: MTEB Tatoeba (swe-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 96.39999999999999
- type: f1
value: 95.3
- type: precision
value: 94.76666666666667
- type: recall
value: 96.39999999999999
task:
type: BitextMining
- dataset:
config: dtp-eng
name: MTEB Tatoeba (dtp-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 8.1
- type: f1
value: 7.001008218834307
- type: precision
value: 6.708329562594269
- type: recall
value: 8.1
task:
type: BitextMining
- dataset:
config: kat-eng
name: MTEB Tatoeba (kat-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 87.1313672922252
- type: f1
value: 84.09070598748882
- type: precision
value: 82.79171454104429
- type: recall
value: 87.1313672922252
task:
type: BitextMining
- dataset:
config: jpn-eng
name: MTEB Tatoeba (jpn-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 96.39999999999999
- type: f1
value: 95.28333333333333
- type: precision
value: 94.73333333333332
- type: recall
value: 96.39999999999999
task:
type: BitextMining
- dataset:
config: csb-eng
name: MTEB Tatoeba (csb-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 42.29249011857708
- type: f1
value: 36.981018542283365
- type: precision
value: 35.415877813576024
- type: recall
value: 42.29249011857708
task:
type: BitextMining
- dataset:
config: xho-eng
name: MTEB Tatoeba (xho-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 83.80281690140845
- type: f1
value: 80.86854460093896
- type: precision
value: 79.60093896713614
- type: recall
value: 83.80281690140845
task:
type: BitextMining
- dataset:
config: orv-eng
name: MTEB Tatoeba (orv-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 45.26946107784431
- type: f1
value: 39.80235464678088
- type: precision
value: 38.14342660001342
- type: recall
value: 45.26946107784431
task:
type: BitextMining
- dataset:
config: ind-eng
name: MTEB Tatoeba (ind-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 94.3
- type: f1
value: 92.9
- type: precision
value: 92.26666666666668
- type: recall
value: 94.3
task:
type: BitextMining
- dataset:
config: tuk-eng
name: MTEB Tatoeba (tuk-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 37.93103448275862
- type: f1
value: 33.15192743764172
- type: precision
value: 31.57456528146183
- type: recall
value: 37.93103448275862
task:
type: BitextMining
- dataset:
config: max-eng
name: MTEB Tatoeba (max-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 69.01408450704226
- type: f1
value: 63.41549295774648
- type: precision
value: 61.342778895595806
- type: recall
value: 69.01408450704226
task:
type: BitextMining
- dataset:
config: swh-eng
name: MTEB Tatoeba (swh-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 76.66666666666667
- type: f1
value: 71.60705960705961
- type: precision
value: 69.60683760683762
- type: recall
value: 76.66666666666667
task:
type: BitextMining
- dataset:
config: hin-eng
name: MTEB Tatoeba (hin-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 95.8
- type: f1
value: 94.48333333333333
- type: precision
value: 93.83333333333333
- type: recall
value: 95.8
task:
type: BitextMining
- dataset:
config: dsb-eng
name: MTEB Tatoeba (dsb-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 52.81837160751566
- type: f1
value: 48.435977731384824
- type: precision
value: 47.11291973845539
- type: recall
value: 52.81837160751566
task:
type: BitextMining
- dataset:
config: ber-eng
name: MTEB Tatoeba (ber-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 44.9
- type: f1
value: 38.88962621607783
- type: precision
value: 36.95936507936508
- type: recall
value: 44.9
task:
type: BitextMining
- dataset:
config: tam-eng
name: MTEB Tatoeba (tam-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 90.55374592833876
- type: f1
value: 88.22553125484721
- type: precision
value: 87.26927252985884
- type: recall
value: 90.55374592833876
task:
type: BitextMining
- dataset:
config: slk-eng
name: MTEB Tatoeba (slk-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 94.6
- type: f1
value: 93.13333333333333
- type: precision
value: 92.45333333333333
- type: recall
value: 94.6
task:
type: BitextMining
- dataset:
config: tgl-eng
name: MTEB Tatoeba (tgl-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 93.7
- type: f1
value: 91.99666666666667
- type: precision
value: 91.26666666666668
- type: recall
value: 93.7
task:
type: BitextMining
- dataset:
config: ast-eng
name: MTEB Tatoeba (ast-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 85.03937007874016
- type: f1
value: 81.75853018372703
- type: precision
value: 80.34120734908137
- type: recall
value: 85.03937007874016
task:
type: BitextMining
- dataset:
config: mkd-eng
name: MTEB Tatoeba (mkd-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 88.3
- type: f1
value: 85.5
- type: precision
value: 84.25833333333334
- type: recall
value: 88.3
task:
type: BitextMining
- dataset:
config: khm-eng
name: MTEB Tatoeba (khm-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 65.51246537396122
- type: f1
value: 60.02297410192148
- type: precision
value: 58.133467727289236
- type: recall
value: 65.51246537396122
task:
type: BitextMining
- dataset:
config: ces-eng
name: MTEB Tatoeba (ces-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 96
- type: f1
value: 94.89
- type: precision
value: 94.39166666666667
- type: recall
value: 96
task:
type: BitextMining
- dataset:
config: tzl-eng
name: MTEB Tatoeba (tzl-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 57.692307692307686
- type: f1
value: 53.162393162393165
- type: precision
value: 51.70673076923077
- type: recall
value: 57.692307692307686
task:
type: BitextMining
- dataset:
config: urd-eng
name: MTEB Tatoeba (urd-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 91.60000000000001
- type: f1
value: 89.21190476190475
- type: precision
value: 88.08666666666667
- type: recall
value: 91.60000000000001
task:
type: BitextMining
- dataset:
config: ara-eng
name: MTEB Tatoeba (ara-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 88
- type: f1
value: 85.47
- type: precision
value: 84.43266233766234
- type: recall
value: 88
task:
type: BitextMining
- dataset:
config: kor-eng
name: MTEB Tatoeba (kor-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 92.7
- type: f1
value: 90.64999999999999
- type: precision
value: 89.68333333333332
- type: recall
value: 92.7
task:
type: BitextMining
- dataset:
config: yid-eng
name: MTEB Tatoeba (yid-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 80.30660377358491
- type: f1
value: 76.33044137466307
- type: precision
value: 74.78970125786164
- type: recall
value: 80.30660377358491
task:
type: BitextMining
- dataset:
config: fin-eng
name: MTEB Tatoeba (fin-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 96.39999999999999
- type: f1
value: 95.44
- type: precision
value: 94.99166666666666
- type: recall
value: 96.39999999999999
task:
type: BitextMining
- dataset:
config: tha-eng
name: MTEB Tatoeba (tha-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 96.53284671532847
- type: f1
value: 95.37712895377129
- type: precision
value: 94.7992700729927
- type: recall
value: 96.53284671532847
task:
type: BitextMining
- dataset:
config: wuu-eng
name: MTEB Tatoeba (wuu-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: accuracy
value: 89
- type: f1
value: 86.23190476190476
- type: precision
value: 85.035
- type: recall
value: 89
task:
type: BitextMining
- dataset:
config: default
name: MTEB Touche2020
revision: None
split: test
type: webis-touche2020
metrics:
- type: map_at_1
value: 2.585
- type: map_at_10
value: 9.012
- type: map_at_100
value: 14.027000000000001
- type: map_at_1000
value: 15.565000000000001
- type: map_at_3
value: 5.032
- type: map_at_5
value: 6.657
- type: mrr_at_1
value: 28.571
- type: mrr_at_10
value: 45.377
- type: mrr_at_100
value: 46.119
- type: mrr_at_1000
value: 46.127
- type: mrr_at_3
value: 41.156
- type: mrr_at_5
value: 42.585
- type: ndcg_at_1
value: 27.551
- type: ndcg_at_10
value: 23.395
- type: ndcg_at_100
value: 33.342
- type: ndcg_at_1000
value: 45.523
- type: ndcg_at_3
value: 25.158
- type: ndcg_at_5
value: 23.427
- type: precision_at_1
value: 28.571
- type: precision_at_10
value: 21.429000000000002
- type: precision_at_100
value: 6.714
- type: precision_at_1000
value: 1.473
- type: precision_at_3
value: 27.211000000000002
- type: precision_at_5
value: 24.490000000000002
- type: recall_at_1
value: 2.585
- type: recall_at_10
value: 15.418999999999999
- type: recall_at_100
value: 42.485
- type: recall_at_1000
value: 79.536
- type: recall_at_3
value: 6.239999999999999
- type: recall_at_5
value: 8.996
task:
type: Retrieval
- dataset:
config: default
name: MTEB ToxicConversationsClassification
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
split: test
type: mteb/toxic_conversations_50k
metrics:
- type: accuracy
value: 71.3234
- type: ap
value: 14.361688653847423
- type: f1
value: 54.819068624319044
task:
type: Classification
- dataset:
config: default
name: MTEB TweetSentimentExtractionClassification
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
split: test
type: mteb/tweet_sentiment_extraction
metrics:
- type: accuracy
value: 61.97792869269949
- type: f1
value: 62.28965628513728
task:
type: Classification
- dataset:
config: default
name: MTEB TwentyNewsgroupsClustering
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
split: test
type: mteb/twentynewsgroups-clustering
metrics:
- type: v_measure
value: 38.90540145385218
task:
type: Clustering
- dataset:
config: default
name: MTEB TwitterSemEval2015
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
split: test
type: mteb/twittersemeval2015-pairclassification
metrics:
- type: cos_sim_accuracy
value: 86.53513739047506
- type: cos_sim_ap
value: 75.27741586677557
- type: cos_sim_f1
value: 69.18792902473774
- type: cos_sim_precision
value: 67.94708725515136
- type: cos_sim_recall
value: 70.47493403693932
- type: dot_accuracy
value: 84.7052512368123
- type: dot_ap
value: 69.36075482849378
- type: dot_f1
value: 64.44688376631296
- type: dot_precision
value: 59.92288500793831
- type: dot_recall
value: 69.70976253298153
- type: euclidean_accuracy
value: 86.60666388508076
- type: euclidean_ap
value: 75.47512772621097
- type: euclidean_f1
value: 69.413872536473
- type: euclidean_precision
value: 67.39562624254472
- type: euclidean_recall
value: 71.55672823218997
- type: manhattan_accuracy
value: 86.52917684925792
- type: manhattan_ap
value: 75.34000110496703
- type: manhattan_f1
value: 69.28489190226429
- type: manhattan_precision
value: 67.24608889992551
- type: manhattan_recall
value: 71.45118733509234
- type: max_accuracy
value: 86.60666388508076
- type: max_ap
value: 75.47512772621097
- type: max_f1
value: 69.413872536473
task:
type: PairClassification
- dataset:
config: default
name: MTEB TwitterURLCorpus
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
split: test
type: mteb/twitterurlcorpus-pairclassification
metrics:
- type: cos_sim_accuracy
value: 89.01695967710637
- type: cos_sim_ap
value: 85.8298270742901
- type: cos_sim_f1
value: 78.46988128389272
- type: cos_sim_precision
value: 74.86017897091722
- type: cos_sim_recall
value: 82.44533415460425
- type: dot_accuracy
value: 88.19420188613343
- type: dot_ap
value: 83.82679165901324
- type: dot_f1
value: 76.55833777304208
- type: dot_precision
value: 75.6884875846501
- type: dot_recall
value: 77.44841392054204
- type: euclidean_accuracy
value: 89.03054294252338
- type: euclidean_ap
value: 85.89089555185325
- type: euclidean_f1
value: 78.62997658079624
- type: euclidean_precision
value: 74.92329149232914
- type: euclidean_recall
value: 82.72251308900523
- type: manhattan_accuracy
value: 89.0266620095471
- type: manhattan_ap
value: 85.86458997929147
- type: manhattan_f1
value: 78.50685331000291
- type: manhattan_precision
value: 74.5499861534201
- type: manhattan_recall
value: 82.90729904527257
- type: max_accuracy
value: 89.03054294252338
- type: max_ap
value: 85.89089555185325
- type: max_f1
value: 78.62997658079624
task:
type: PairClassification
tags:
- mteb
- sentence-similarity
- feature-extraction
- onnx
- teradata
See Disclaimer below
A Teradata Vantage compatible Embeddings Model
intfloat/multilingual-e5-large
Overview of this Model
An Embedding Model which maps text (sentence/ paragraphs) into a vector. The intfloat/multilingual-e5-large model well known for its effectiveness in capturing semantic meanings in text data. It's a state-of-the-art model trained on a large corpus, capable of generating high-quality text embeddings.
- 559.89M params (Sizes in ONNX format - "int8": 535.01MB, "uint8": 535.01MB)
- 514 maximum input tokens
- 1024 dimensions of output vector
- Licence: mit. The released models can be used for commercial purposes free of charge.
- Reference to Original Model: https://huggingface.co./intfloat/multilingual-e5-large
Quickstart: Deploying this Model in Teradata Vantage
We have pre-converted the model into the ONNX format compatible with BYOM 6.0, eliminating the need for manual conversion.
Note: Ensure you have access to a Teradata Database with BYOM 6.0 installed.
To get started, clone the pre-converted model directly from the Teradata HuggingFace repository.
import teradataml as tdml
import getpass
from huggingface_hub import hf_hub_download
model_name = "multilingual-e5-large"
number_dimensions_output = 1024
model_file_name = "model_int8.onnx"
# Step 1: Download Model from Teradata HuggingFace Page
hf_hub_download(repo_id=f"Teradata/{model_name}", filename=f"onnx/{model_file_name}", local_dir="./")
hf_hub_download(repo_id=f"Teradata/{model_name}", filename=f"tokenizer.json", local_dir="./")
# Step 2: Create Connection to Vantage
tdml.create_context(host = input('enter your hostname'),
username=input('enter your username'),
password = getpass.getpass("enter your password"))
# Step 3: Load Models into Vantage
# a) Embedding model
tdml.save_byom(model_id = model_name, # must be unique in the models table
model_file = f"onnx/{model_file_name}",
table_name = 'embeddings_models' )
# b) Tokenizer
tdml.save_byom(model_id = model_name, # must be unique in the models table
model_file = 'tokenizer.json',
table_name = 'embeddings_tokenizers')
# Step 4: Test ONNXEmbeddings Function
# Note that ONNXEmbeddings expects the 'payload' column to be 'txt'.
# If it has got a different name, just rename it in a subquery/CTE.
input_table = "emails.emails"
embeddings_query = f"""
SELECT
*
from mldb.ONNXEmbeddings(
on {input_table} as InputTable
on (select * from embeddings_models where model_id = '{model_name}') as ModelTable DIMENSION
on (select model as tokenizer from embeddings_tokenizers where model_id = '{model_name}') as TokenizerTable DIMENSION
using
Accumulate('id', 'txt')
ModelOutputTensor('sentence_embedding')
EnableMemoryCheck('false')
OutputFormat('FLOAT32({number_dimensions_output})')
OverwriteCachedModel('true')
) a
"""
DF_embeddings = tdml.DataFrame.from_query(embeddings_query)
DF_embeddings
What Can I Do with the Embeddings?
Teradata Vantage includes pre-built in-database functions to process embeddings further. Explore the following examples:
- Semantic Clustering with TD_KMeans: Semantic Clustering Python Notebook
- Semantic Distance with TD_VectorDistance: Semantic Similarity Python Notebook
- RAG-Based Application with TD_VectorDistance: RAG and Bedrock Query PDF Notebook
Deep Dive into Model Conversion to ONNX
The steps below outline how we converted the open-source Hugging Face model into an ONNX file compatible with the in-database ONNXEmbeddings function.
You do not need to perform these steps—they are provided solely for documentation and transparency. However, they may be helpful if you wish to convert another model to the required format.
Part 1. Importing and Converting Model using optimum
We start by importing the pre-trained intfloat/multilingual-e5-large model from Hugging Face.
To enhance performance and ensure compatibility with various execution environments, we'll use the Optimum utility to convert the model into the ONNX (Open Neural Network Exchange) format.
After conversion to ONNX, we are fixing the opset in the ONNX file for compatibility with ONNX runtime used in Teradata Vantage
We are generating ONNX files for multiple different precisions: int8, uint8
You can find the detailed conversion steps in the file convert.py
Part 2. Running the model in Python with onnxruntime & compare results
Once the fixes are applied, we proceed to test the correctness of the ONNX model by calculating cosine similarity between two texts using native SentenceTransformers and ONNX runtime, comparing the results.
If the results are identical, it confirms that the ONNX model gives the same result as the native models, validating its correctness and suitability for further use in the database.
import onnxruntime as rt
from sentence_transformers.util import cos_sim
from sentence_transformers import SentenceTransformer
import transformers
sentences_1 = 'How is the weather today?'
sentences_2 = 'What is the current weather like today?'
# Calculate ONNX result
tokenizer = transformers.AutoTokenizer.from_pretrained("intfloat/multilingual-e5-large")
predef_sess = rt.InferenceSession("onnx/model_int8.onnx")
enc1 = tokenizer(sentences_1)
embeddings_1_onnx = predef_sess.run(None, {"input_ids": [enc1.input_ids],
"attention_mask": [enc1.attention_mask]})
enc2 = tokenizer(sentences_2)
embeddings_2_onnx = predef_sess.run(None, {"input_ids": [enc2.input_ids],
"attention_mask": [enc2.attention_mask]})
# Calculate embeddings with SentenceTransformer
model = SentenceTransformer(model_id, trust_remote_code=True)
embeddings_1_sentence_transformer = model.encode(sentences_1, normalize_embeddings=True, trust_remote_code=True)
embeddings_2_sentence_transformer = model.encode(sentences_2, normalize_embeddings=True, trust_remote_code=True)
# Compare results
print("Cosine similiarity for embeddings calculated with ONNX:" + str(cos_sim(embeddings_1_onnx[1][0], embeddings_2_onnx[1][0])))
print("Cosine similiarity for embeddings calculated with SentenceTransformer:" + str(cos_sim(embeddings_1_sentence_transformer, embeddings_2_sentence_transformer)))
You can find the detailed ONNX vs. SentenceTransformer result comparison steps in the file test_local.py
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