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---
language:
- af
- ar
- az
- be
- bg
- bn
- ca
- ceb
- cs
- cy
- da
- de
- el
- en
- es
- et
- eu
- fa
- fi
- fr
- gl
- gu
- he
- hi
- hr
- ht
- hu
- hy
- id
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ky
- lo
- lt
- lv
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- 'no'
- pa
- pl
- pt
- qu
- ro
- ru
- si
- sk
- sl
- so
- sq
- sr
- sv
- sw
- ta
- te
- th
- tl
- tr
- uk
- ur
- vi
- yo
- zh
license: apache-2.0
model-index:
- name: gte-multilingual-base (dense)
results:
- dataset:
config: default
name: MTEB 8TagsClustering
revision: None
split: test
type: PL-MTEB/8tags-clustering
metrics:
- type: v_measure
value: 33.66681726329994
task:
type: Clustering
- dataset:
config: default
name: MTEB AFQMC
revision: b44c3b011063adb25877c13823db83bb193913c4
split: validation
type: C-MTEB/AFQMC
metrics:
- type: cos_sim_spearman
value: 43.54760696384009
task:
type: STS
- dataset:
config: default
name: MTEB ATEC
revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865
split: test
type: C-MTEB/ATEC
metrics:
- type: cos_sim_spearman
value: 48.91186363417501
task:
type: STS
- dataset:
config: default
name: MTEB AllegroReviews
revision: None
split: test
type: PL-MTEB/allegro-reviews
metrics:
- type: accuracy
value: 41.689860834990064
task:
type: Classification
- dataset:
config: default
name: MTEB AlloProfClusteringP2P
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
split: test
type: lyon-nlp/alloprof
metrics:
- type: v_measure
value: 54.20241337977897
- type: v_measure
value: 44.34083695608643
task:
type: Clustering
- dataset:
config: default
name: MTEB AlloprofReranking
revision: 666fdacebe0291776e86f29345663dfaf80a0db9
split: test
type: lyon-nlp/mteb-fr-reranking-alloprof-s2p
metrics:
- type: map
value: 64.91495250072002
task:
type: Reranking
- dataset:
config: default
name: MTEB AlloprofRetrieval
revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
split: test
type: lyon-nlp/alloprof
metrics:
- type: ndcg_at_10
value: 53.638
task:
type: Retrieval
- dataset:
config: en
name: MTEB AmazonCounterfactualClassification (en)
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
split: test
type: mteb/amazon_counterfactual
metrics:
- type: accuracy
value: 75.95522388059702
task:
type: Classification
- dataset:
config: default
name: MTEB AmazonPolarityClassification
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
split: test
type: mteb/amazon_polarity
metrics:
- type: accuracy
value: 80.717625
task:
type: Classification
- dataset:
config: en
name: MTEB AmazonReviewsClassification (en)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
- type: accuracy
value: 43.64199999999999
task:
type: Classification
- dataset:
config: de
name: MTEB AmazonReviewsClassification (de)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
- type: accuracy
value: 40.108
task:
type: Classification
- dataset:
config: es
name: MTEB AmazonReviewsClassification (es)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
- type: accuracy
value: 40.169999999999995
task:
type: Classification
- dataset:
config: fr
name: MTEB AmazonReviewsClassification (fr)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
- type: accuracy
value: 39.56799999999999
task:
type: Classification
- dataset:
config: ja
name: MTEB AmazonReviewsClassification (ja)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
- type: accuracy
value: 35.75000000000001
task:
type: Classification
- dataset:
config: zh
name: MTEB AmazonReviewsClassification (zh)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
- type: accuracy
value: 33.342000000000006
task:
type: Classification
- dataset:
config: default
name: MTEB ArguAna
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
split: test
type: mteb/arguana
metrics:
- type: ndcg_at_10
value: 58.231
task:
type: Retrieval
- dataset:
config: default
name: MTEB ArguAna-PL
revision: 63fc86750af76253e8c760fc9e534bbf24d260a2
split: test
type: clarin-knext/arguana-pl
metrics:
- type: ndcg_at_10
value: 53.166000000000004
task:
type: Retrieval
- dataset:
config: default
name: MTEB ArxivClusteringP2P
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
split: test
type: mteb/arxiv-clustering-p2p
metrics:
- type: v_measure
value: 46.01900557959478
task:
type: Clustering
- dataset:
config: default
name: MTEB ArxivClusteringS2S
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
split: test
type: mteb/arxiv-clustering-s2s
metrics:
- type: v_measure
value: 41.06626465345723
task:
type: Clustering
- dataset:
config: default
name: MTEB AskUbuntuDupQuestions
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
split: test
type: mteb/askubuntudupquestions-reranking
metrics:
- type: map
value: 61.87514497610431
task:
type: Reranking
- dataset:
config: default
name: MTEB BIOSSES
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
split: test
type: mteb/biosses-sts
metrics:
- type: cos_sim_spearman
value: 81.21450112991194
task:
type: STS
- dataset:
config: default
name: MTEB BQ
revision: e3dda5e115e487b39ec7e618c0c6a29137052a55
split: test
type: C-MTEB/BQ
metrics:
- type: cos_sim_spearman
value: 51.71589543397271
task:
type: STS
- dataset:
config: default
name: MTEB BSARDRetrieval
revision: 5effa1b9b5fa3b0f9e12523e6e43e5f86a6e6d59
split: test
type: maastrichtlawtech/bsard
metrics:
- type: ndcg_at_10
value: 26.115
task:
type: Retrieval
- dataset:
config: de-en
name: MTEB BUCC (de-en)
revision: d51519689f32196a32af33b075a01d0e7c51e252
split: test
type: mteb/bucc-bitext-mining
metrics:
- type: f1
value: 98.6169102296451
task:
type: BitextMining
- dataset:
config: fr-en
name: MTEB BUCC (fr-en)
revision: d51519689f32196a32af33b075a01d0e7c51e252
split: test
type: mteb/bucc-bitext-mining
metrics:
- type: f1
value: 97.89603052314916
task:
type: BitextMining
- dataset:
config: ru-en
name: MTEB BUCC (ru-en)
revision: d51519689f32196a32af33b075a01d0e7c51e252
split: test
type: mteb/bucc-bitext-mining
metrics:
- type: f1
value: 97.12388869645537
task:
type: BitextMining
- dataset:
config: zh-en
name: MTEB BUCC (zh-en)
revision: d51519689f32196a32af33b075a01d0e7c51e252
split: test
type: mteb/bucc-bitext-mining
metrics:
- type: f1
value: 98.15692469720906
task:
type: BitextMining
- dataset:
config: default
name: MTEB Banking77Classification
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
split: test
type: mteb/banking77
metrics:
- type: accuracy
value: 85.36038961038962
task:
type: Classification
- dataset:
config: default
name: MTEB BiorxivClusteringP2P
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
split: test
type: mteb/biorxiv-clustering-p2p
metrics:
- type: v_measure
value: 37.5903826674123
task:
type: Clustering
- dataset:
config: default
name: MTEB BiorxivClusteringS2S
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
split: test
type: mteb/biorxiv-clustering-s2s
metrics:
- type: v_measure
value: 34.21474277151329
task:
type: Clustering
- dataset:
config: default
name: MTEB CBD
revision: None
split: test
type: PL-MTEB/cbd
metrics:
- type: accuracy
value: 62.519999999999996
task:
type: Classification
- dataset:
config: default
name: MTEB CDSC-E
revision: None
split: test
type: PL-MTEB/cdsce-pairclassification
metrics:
- type: cos_sim_ap
value: 74.90132799162956
task:
type: PairClassification
- dataset:
config: default
name: MTEB CDSC-R
revision: None
split: test
type: PL-MTEB/cdscr-sts
metrics:
- type: cos_sim_spearman
value: 90.30727955142524
task:
type: STS
- dataset:
config: default
name: MTEB CLSClusteringP2P
revision: 4b6227591c6c1a73bc76b1055f3b7f3588e72476
split: test
type: C-MTEB/CLSClusteringP2P
metrics:
- type: v_measure
value: 37.94850105022274
task:
type: Clustering
- dataset:
config: default
name: MTEB CLSClusteringS2S
revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f
split: test
type: C-MTEB/CLSClusteringS2S
metrics:
- type: v_measure
value: 38.11958675421534
task:
type: Clustering
- dataset:
config: default
name: MTEB CMedQAv1
revision: 8d7f1e942507dac42dc58017c1a001c3717da7df
split: test
type: C-MTEB/CMedQAv1-reranking
metrics:
- type: map
value: 86.10950950485399
task:
type: Reranking
- dataset:
config: default
name: MTEB CMedQAv2
revision: 23d186750531a14a0357ca22cd92d712fd512ea0
split: test
type: C-MTEB/CMedQAv2-reranking
metrics:
- type: map
value: 87.28038294231966
task:
type: Reranking
- dataset:
config: default
name: MTEB CQADupstackAndroidRetrieval
revision: f46a197baaae43b4f621051089b82a364682dfeb
split: test
type: mteb/cqadupstack-android
metrics:
- type: ndcg_at_10
value: 47.099000000000004
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackEnglishRetrieval
revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
split: test
type: mteb/cqadupstack-english
metrics:
- type: ndcg_at_10
value: 45.973000000000006
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackGamingRetrieval
revision: 4885aa143210c98657558c04aaf3dc47cfb54340
split: test
type: mteb/cqadupstack-gaming
metrics:
- type: ndcg_at_10
value: 55.606
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackGisRetrieval
revision: 5003b3064772da1887988e05400cf3806fe491f2
split: test
type: mteb/cqadupstack-gis
metrics:
- type: ndcg_at_10
value: 36.638
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackMathematicaRetrieval
revision: 90fceea13679c63fe563ded68f3b6f06e50061de
split: test
type: mteb/cqadupstack-mathematica
metrics:
- type: ndcg_at_10
value: 30.711
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackPhysicsRetrieval
revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
split: test
type: mteb/cqadupstack-physics
metrics:
- type: ndcg_at_10
value: 44.523
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackProgrammersRetrieval
revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
split: test
type: mteb/cqadupstack-programmers
metrics:
- type: ndcg_at_10
value: 37.940000000000005
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackRetrieval
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
split: test
type: mteb/cqadupstack
metrics:
- type: ndcg_at_10
value: 38.12183333333333
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackStatsRetrieval
revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
split: test
type: mteb/cqadupstack-stats
metrics:
- type: ndcg_at_10
value: 32.684000000000005
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackTexRetrieval
revision: 46989137a86843e03a6195de44b09deda022eec7
split: test
type: mteb/cqadupstack-tex
metrics:
- type: ndcg_at_10
value: 26.735
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackUnixRetrieval
revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
split: test
type: mteb/cqadupstack-unix
metrics:
- type: ndcg_at_10
value: 36.933
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackWebmastersRetrieval
revision: 160c094312a0e1facb97e55eeddb698c0abe3571
split: test
type: mteb/cqadupstack-webmasters
metrics:
- type: ndcg_at_10
value: 33.747
task:
type: Retrieval
- dataset:
config: default
name: MTEB CQADupstackWordpressRetrieval
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
split: test
type: mteb/cqadupstack-wordpress
metrics:
- type: ndcg_at_10
value: 28.872999999999998
task:
type: Retrieval
- dataset:
config: default
name: MTEB ClimateFEVER
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
split: test
type: mteb/climate-fever
metrics:
- type: ndcg_at_10
value: 34.833
task:
type: Retrieval
- dataset:
config: default
name: MTEB CmedqaRetrieval
revision: cd540c506dae1cf9e9a59c3e06f42030d54e7301
split: dev
type: C-MTEB/CmedqaRetrieval
metrics:
- type: ndcg_at_10
value: 43.78
task:
type: Retrieval
- dataset:
config: default
name: MTEB Cmnli
revision: 41bc36f332156f7adc9e38f53777c959b2ae9766
split: validation
type: C-MTEB/CMNLI
metrics:
- type: cos_sim_ap
value: 84.00640599186677
task:
type: PairClassification
- dataset:
config: default
name: MTEB CovidRetrieval
revision: 1271c7809071a13532e05f25fb53511ffce77117
split: dev
type: C-MTEB/CovidRetrieval
metrics:
- type: ndcg_at_10
value: 80.60000000000001
task:
type: Retrieval
- dataset:
config: default
name: MTEB DBPedia
revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
split: test
type: mteb/dbpedia
metrics:
- type: ndcg_at_10
value: 40.116
task:
type: Retrieval
- dataset:
config: default
name: MTEB DBPedia-PL
revision: 76afe41d9af165cc40999fcaa92312b8b012064a
split: test
type: clarin-knext/dbpedia-pl
metrics:
- type: ndcg_at_10
value: 32.498
task:
type: Retrieval
- dataset:
config: default
name: MTEB DuRetrieval
revision: a1a333e290fe30b10f3f56498e3a0d911a693ced
split: dev
type: C-MTEB/DuRetrieval
metrics:
- type: ndcg_at_10
value: 87.547
task:
type: Retrieval
- dataset:
config: default
name: MTEB EcomRetrieval
revision: 687de13dc7294d6fd9be10c6945f9e8fec8166b9
split: dev
type: C-MTEB/EcomRetrieval
metrics:
- type: ndcg_at_10
value: 64.85
task:
type: Retrieval
- dataset:
config: default
name: MTEB EmotionClassification
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
split: test
type: mteb/emotion
metrics:
- type: accuracy
value: 47.949999999999996
task:
type: Classification
- dataset:
config: default
name: MTEB FEVER
revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
split: test
type: mteb/fever
metrics:
- type: ndcg_at_10
value: 92.111
task:
type: Retrieval
- dataset:
config: default
name: MTEB FiQA-PL
revision: 2e535829717f8bf9dc829b7f911cc5bbd4e6608e
split: test
type: clarin-knext/fiqa-pl
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config: zh-TW
name: MTEB MassiveIntentClassification (zh-TW)
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
split: test
type: mteb/amazon_massive_intent
metrics:
- type: accuracy
value: 64.17283120376598
task:
type: Classification
- dataset:
config: af
name: MTEB MassiveScenarioClassification (af)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 64.89912575655683
task:
type: Classification
- dataset:
config: am
name: MTEB MassiveScenarioClassification (am)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 57.27975790181573
task:
type: Classification
- dataset:
config: ar
name: MTEB MassiveScenarioClassification (ar)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 62.269670477471415
task:
type: Classification
- dataset:
config: az
name: MTEB MassiveScenarioClassification (az)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 65.10423671822461
task:
type: Classification
- dataset:
config: bn
name: MTEB MassiveScenarioClassification (bn)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 62.40753194351043
task:
type: Classification
- dataset:
config: cy
name: MTEB MassiveScenarioClassification (cy)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 55.369872225958304
task:
type: Classification
- dataset:
config: da
name: MTEB MassiveScenarioClassification (da)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 71.60726294552792
task:
type: Classification
- dataset:
config: de
name: MTEB MassiveScenarioClassification (de)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 70.30262273032952
task:
type: Classification
- dataset:
config: el
name: MTEB MassiveScenarioClassification (el)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 69.52925353059851
task:
type: Classification
- dataset:
config: en
name: MTEB MassiveScenarioClassification (en)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 76.28446536650976
task:
type: Classification
- dataset:
config: es
name: MTEB MassiveScenarioClassification (es)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 72.45460659045058
task:
type: Classification
- dataset:
config: fa
name: MTEB MassiveScenarioClassification (fa)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 70.26563550773368
task:
type: Classification
- dataset:
config: fi
name: MTEB MassiveScenarioClassification (fi)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 67.20578345662408
task:
type: Classification
- dataset:
config: fr
name: MTEB MassiveScenarioClassification (fr)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 72.64963012777405
task:
type: Classification
- dataset:
config: he
name: MTEB MassiveScenarioClassification (he)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 61.698049764626774
task:
type: Classification
- dataset:
config: hi
name: MTEB MassiveScenarioClassification (hi)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 70.14458641560188
task:
type: Classification
- dataset:
config: hu
name: MTEB MassiveScenarioClassification (hu)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 70.51445864156018
task:
type: Classification
- dataset:
config: hy
name: MTEB MassiveScenarioClassification (hy)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 60.13786146603901
task:
type: Classification
- dataset:
config: id
name: MTEB MassiveScenarioClassification (id)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 70.61533288500337
task:
type: Classification
- dataset:
config: is
name: MTEB MassiveScenarioClassification (is)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 61.526563550773375
task:
type: Classification
- dataset:
config: it
name: MTEB MassiveScenarioClassification (it)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 71.99731002017484
task:
type: Classification
- dataset:
config: ja
name: MTEB MassiveScenarioClassification (ja)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 71.59381304640216
task:
type: Classification
- dataset:
config: jv
name: MTEB MassiveScenarioClassification (jv)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 57.010759919300604
task:
type: Classification
- dataset:
config: ka
name: MTEB MassiveScenarioClassification (ka)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 53.26160053799597
task:
type: Classification
- dataset:
config: km
name: MTEB MassiveScenarioClassification (km)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 57.800941492938804
task:
type: Classification
- dataset:
config: kn
name: MTEB MassiveScenarioClassification (kn)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 62.387357094821795
task:
type: Classification
- dataset:
config: ko
name: MTEB MassiveScenarioClassification (ko)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 69.5359784801614
task:
type: Classification
- dataset:
config: lv
name: MTEB MassiveScenarioClassification (lv)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 63.36919973100203
task:
type: Classification
- dataset:
config: ml
name: MTEB MassiveScenarioClassification (ml)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 64.81506388702084
task:
type: Classification
- dataset:
config: mn
name: MTEB MassiveScenarioClassification (mn)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 59.35104236718225
task:
type: Classification
- dataset:
config: ms
name: MTEB MassiveScenarioClassification (ms)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 66.67787491593813
task:
type: Classification
- dataset:
config: my
name: MTEB MassiveScenarioClassification (my)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 59.4250168123739
task:
type: Classification
- dataset:
config: nb
name: MTEB MassiveScenarioClassification (nb)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 71.49630127774043
task:
type: Classification
- dataset:
config: nl
name: MTEB MassiveScenarioClassification (nl)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 71.95696032279758
task:
type: Classification
- dataset:
config: pl
name: MTEB MassiveScenarioClassification (pl)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 70.11768661735036
task:
type: Classification
- dataset:
config: pt
name: MTEB MassiveScenarioClassification (pt)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 71.86953597848016
task:
type: Classification
- dataset:
config: ro
name: MTEB MassiveScenarioClassification (ro)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 68.51042367182247
task:
type: Classification
- dataset:
config: ru
name: MTEB MassiveScenarioClassification (ru)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 71.65097511768661
task:
type: Classification
- dataset:
config: sl
name: MTEB MassiveScenarioClassification (sl)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 66.81573638197713
task:
type: Classification
- dataset:
config: sq
name: MTEB MassiveScenarioClassification (sq)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 65.26227303295225
task:
type: Classification
- dataset:
config: sv
name: MTEB MassiveScenarioClassification (sv)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 72.51513113651646
task:
type: Classification
- dataset:
config: sw
name: MTEB MassiveScenarioClassification (sw)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 58.29858776059179
task:
type: Classification
- dataset:
config: ta
name: MTEB MassiveScenarioClassification (ta)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 62.72696704774714
task:
type: Classification
- dataset:
config: te
name: MTEB MassiveScenarioClassification (te)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 66.57700067249496
task:
type: Classification
- dataset:
config: th
name: MTEB MassiveScenarioClassification (th)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 68.22797579018157
task:
type: Classification
- dataset:
config: tl
name: MTEB MassiveScenarioClassification (tl)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 61.97041022192333
task:
type: Classification
- dataset:
config: tr
name: MTEB MassiveScenarioClassification (tr)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 70.72629455279085
task:
type: Classification
- dataset:
config: ur
name: MTEB MassiveScenarioClassification (ur)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 63.16072629455278
task:
type: Classification
- dataset:
config: vi
name: MTEB MassiveScenarioClassification (vi)
revision: 7d571f92784cd94a019292a1f45445077d0ef634
split: test
type: mteb/amazon_massive_scenario
metrics:
- type: accuracy
value: 67.92199058507062
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: 74.40484196368527
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: 71.61398789509079
task:
type: Classification
- dataset:
config: default
name: MTEB MedicalRetrieval
revision: 2039188fb5800a9803ba5048df7b76e6fb151fc6
split: dev
type: C-MTEB/MedicalRetrieval
metrics:
- type: ndcg_at_10
value: 61.934999999999995
task:
type: Retrieval
- dataset:
config: default
name: MTEB MedrxivClusteringP2P
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
split: test
type: mteb/medrxiv-clustering-p2p
metrics:
- type: v_measure
value: 33.052031054565205
task:
type: Clustering
- dataset:
config: default
name: MTEB MedrxivClusteringS2S
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
split: test
type: mteb/medrxiv-clustering-s2s
metrics:
- type: v_measure
value: 31.969909524076794
task:
type: Clustering
- dataset:
config: default
name: MTEB MindSmallReranking
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
split: test
type: mteb/mind_small
metrics:
- type: map
value: 31.7530992892652
task:
type: Reranking
- dataset:
config: fr
name: MTEB MintakaRetrieval (fr)
revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e
split: test
type: jinaai/mintakaqa
metrics:
- type: ndcg_at_10
value: 34.705999999999996
task:
type: Retrieval
- dataset:
config: ar
name: MTEB MultiLongDocRetrieval (ar)
revision: None
split: test
type: Shitao/MLDR
metrics:
- type: ndcg_at_10
value: 55.166000000000004
task:
type: Retrieval
- dataset:
config: de
name: MTEB MultiLongDocRetrieval (de)
revision: None
split: test
type: Shitao/MLDR
metrics:
- type: ndcg_at_10
value: 55.155
task:
type: Retrieval
- dataset:
config: en
name: MTEB MultiLongDocRetrieval (en)
revision: None
split: test
type: Shitao/MLDR
metrics:
- type: ndcg_at_10
value: 50.993
task:
type: Retrieval
- dataset:
config: es
name: MTEB MultiLongDocRetrieval (es)
revision: None
split: test
type: Shitao/MLDR
metrics:
- type: ndcg_at_10
value: 81.228
task:
type: Retrieval
- dataset:
config: fr
name: MTEB MultiLongDocRetrieval (fr)
revision: None
split: test
type: Shitao/MLDR
metrics:
- type: ndcg_at_10
value: 76.19
task:
type: Retrieval
- dataset:
config: hi
name: MTEB MultiLongDocRetrieval (hi)
revision: None
split: test
type: Shitao/MLDR
metrics:
- type: ndcg_at_10
value: 45.206
task:
type: Retrieval
- dataset:
config: it
name: MTEB MultiLongDocRetrieval (it)
revision: None
split: test
type: Shitao/MLDR
metrics:
- type: ndcg_at_10
value: 66.741
task:
type: Retrieval
- dataset:
config: ja
name: MTEB MultiLongDocRetrieval (ja)
revision: None
split: test
type: Shitao/MLDR
metrics:
- type: ndcg_at_10
value: 52.111
task:
type: Retrieval
- dataset:
config: ko
name: MTEB MultiLongDocRetrieval (ko)
revision: None
split: test
type: Shitao/MLDR
metrics:
- type: ndcg_at_10
value: 46.733000000000004
task:
type: Retrieval
- dataset:
config: pt
name: MTEB MultiLongDocRetrieval (pt)
revision: None
split: test
type: Shitao/MLDR
metrics:
- type: ndcg_at_10
value: 79.105
task:
type: Retrieval
- dataset:
config: ru
name: MTEB MultiLongDocRetrieval (ru)
revision: None
split: test
type: Shitao/MLDR
metrics:
- type: ndcg_at_10
value: 64.21
task:
type: Retrieval
- dataset:
config: th
name: MTEB MultiLongDocRetrieval (th)
revision: None
split: test
type: Shitao/MLDR
metrics:
- type: ndcg_at_10
value: 35.467
task:
type: Retrieval
- dataset:
config: zh
name: MTEB MultiLongDocRetrieval (zh)
revision: None
split: test
type: Shitao/MLDR
metrics:
- type: ndcg_at_10
value: 27.419
task:
type: Retrieval
- dataset:
config: default
name: MTEB MultilingualSentiment
revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a
split: validation
type: C-MTEB/MultilingualSentiment-classification
metrics:
- type: accuracy
value: 61.02000000000001
task:
type: Classification
- dataset:
config: default
name: MTEB NFCorpus
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
split: test
type: mteb/nfcorpus
metrics:
- type: ndcg_at_10
value: 36.65
task:
type: Retrieval
- dataset:
config: default
name: MTEB NFCorpus-PL
revision: 9a6f9567fda928260afed2de480d79c98bf0bec0
split: test
type: clarin-knext/nfcorpus-pl
metrics:
- type: ndcg_at_10
value: 26.831
task:
type: Retrieval
- dataset:
config: default
name: MTEB NQ
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
split: test
type: mteb/nq
metrics:
- type: ndcg_at_10
value: 58.111000000000004
task:
type: Retrieval
- dataset:
config: default
name: MTEB NQ-PL
revision: f171245712cf85dd4700b06bef18001578d0ca8d
split: test
type: clarin-knext/nq-pl
metrics:
- type: ndcg_at_10
value: 43.126999999999995
task:
type: Retrieval
- dataset:
config: default
name: MTEB Ocnli
revision: 66e76a618a34d6d565d5538088562851e6daa7ec
split: validation
type: C-MTEB/OCNLI
metrics:
- type: cos_sim_ap
value: 72.67630697316041
task:
type: PairClassification
- dataset:
config: default
name: MTEB OnlineShopping
revision: e610f2ebd179a8fda30ae534c3878750a96db120
split: test
type: C-MTEB/OnlineShopping-classification
metrics:
- type: accuracy
value: 84.85000000000001
task:
type: Classification
- dataset:
config: fr
name: MTEB OpusparcusPC (fr)
revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
split: test
type: GEM/opusparcus
metrics:
- type: cos_sim_ap
value: 100
task:
type: PairClassification
- dataset:
config: default
name: MTEB PAC
revision: None
split: test
type: laugustyniak/abusive-clauses-pl
metrics:
- type: accuracy
value: 65.99189110918043
task:
type: Classification
- dataset:
config: default
name: MTEB PAWSX
revision: 9c6a90e430ac22b5779fb019a23e820b11a8b5e1
split: test
type: C-MTEB/PAWSX
metrics:
- type: cos_sim_spearman
value: 16.124364530596228
task:
type: STS
- dataset:
config: default
name: MTEB PPC
revision: None
split: test
type: PL-MTEB/ppc-pairclassification
metrics:
- type: cos_sim_ap
value: 92.43431057460192
task:
type: PairClassification
- dataset:
config: default
name: MTEB PSC
revision: None
split: test
type: PL-MTEB/psc-pairclassification
metrics:
- type: cos_sim_ap
value: 99.06090138049724
task:
type: PairClassification
- dataset:
config: fr
name: MTEB PawsX (fr)
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
split: test
type: paws-x
metrics:
- type: cos_sim_ap
value: 58.9314954874314
task:
type: PairClassification
- dataset:
config: default
name: MTEB PolEmo2.0-IN
revision: None
split: test
type: PL-MTEB/polemo2_in
metrics:
- type: accuracy
value: 69.59833795013851
task:
type: Classification
- dataset:
config: default
name: MTEB PolEmo2.0-OUT
revision: None
split: test
type: PL-MTEB/polemo2_out
metrics:
- type: accuracy
value: 44.73684210526315
task:
type: Classification
- dataset:
config: default
name: MTEB QBQTC
revision: 790b0510dc52b1553e8c49f3d2afb48c0e5c48b7
split: test
type: C-MTEB/QBQTC
metrics:
- type: cos_sim_spearman
value: 39.36450754137984
task:
type: STS
- dataset:
config: default
name: MTEB Quora-PL
revision: 0be27e93455051e531182b85e85e425aba12e9d4
split: test
type: clarin-knext/quora-pl
metrics:
- type: ndcg_at_10
value: 80.76299999999999
task:
type: Retrieval
- dataset:
config: default
name: MTEB QuoraRetrieval
revision: None
split: test
type: mteb/quora
metrics:
- type: ndcg_at_10
value: 88.022
task:
type: Retrieval
- dataset:
config: default
name: MTEB RedditClustering
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
split: test
type: mteb/reddit-clustering
metrics:
- type: v_measure
value: 55.719165988934385
task:
type: Clustering
- dataset:
config: default
name: MTEB RedditClusteringP2P
revision: 282350215ef01743dc01b456c7f5241fa8937f16
split: test
type: mteb/reddit-clustering-p2p
metrics:
- type: v_measure
value: 62.25390069273025
task:
type: Clustering
- dataset:
config: default
name: MTEB SCIDOCS
revision: None
split: test
type: mteb/scidocs
metrics:
- type: ndcg_at_10
value: 18.243000000000002
task:
type: Retrieval
- dataset:
config: default
name: MTEB SCIDOCS-PL
revision: 45452b03f05560207ef19149545f168e596c9337
split: test
type: clarin-knext/scidocs-pl
metrics:
- type: ndcg_at_10
value: 14.219000000000001
task:
type: Retrieval
- dataset:
config: default
name: MTEB SICK-E-PL
revision: None
split: test
type: PL-MTEB/sicke-pl-pairclassification
metrics:
- type: cos_sim_ap
value: 75.4022630307816
task:
type: PairClassification
- dataset:
config: default
name: MTEB SICK-R
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
split: test
type: mteb/sickr-sts
metrics:
- type: cos_sim_spearman
value: 79.34269390198548
task:
type: STS
- dataset:
config: default
name: MTEB SICK-R-PL
revision: None
split: test
type: PL-MTEB/sickr-pl-sts
metrics:
- type: cos_sim_spearman
value: 74.0651660446132
task:
type: STS
- dataset:
config: default
name: MTEB SICKFr
revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a
split: test
type: Lajavaness/SICK-fr
metrics:
- type: cos_sim_spearman
value: 78.62693119733123
task:
type: STS
- dataset:
config: default
name: MTEB STS12
revision: a0d554a64d88156834ff5ae9920b964011b16384
split: test
type: mteb/sts12-sts
metrics:
- type: cos_sim_spearman
value: 77.50660544631359
task:
type: STS
- dataset:
config: default
name: MTEB STS13
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
split: test
type: mteb/sts13-sts
metrics:
- type: cos_sim_spearman
value: 85.55415077723738
task:
type: STS
- dataset:
config: default
name: MTEB STS14
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
split: test
type: mteb/sts14-sts
metrics:
- type: cos_sim_spearman
value: 81.67550814479077
task:
type: STS
- dataset:
config: default
name: MTEB STS15
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
split: test
type: mteb/sts15-sts
metrics:
- type: cos_sim_spearman
value: 88.94601412322764
task:
type: STS
- dataset:
config: default
name: MTEB STS16
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
split: test
type: mteb/sts16-sts
metrics:
- type: cos_sim_spearman
value: 84.33844259337481
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_spearman
value: 81.58650681159105
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_spearman
value: 78.82472265884256
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_spearman
value: 76.43637938260397
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_spearman
value: 84.71008299464059
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_spearman
value: 88.88074713413747
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_spearman
value: 76.36405640457285
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_spearman
value: 83.84737910084762
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_spearman
value: 87.03931621433031
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_spearman
value: 84.43335591752246
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_spearman
value: 83.85268648747021
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_spearman
value: 82.45786516224341
task:
type: STS
- dataset:
config: en
name: MTEB STS22 (en)
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_spearman
value: 67.20227303970304
task:
type: STS
- dataset:
config: de
name: MTEB STS22 (de)
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_spearman
value: 60.892838305537126
task:
type: STS
- dataset:
config: es
name: MTEB STS22 (es)
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_spearman
value: 72.01876318464508
task:
type: STS
- dataset:
config: pl
name: MTEB STS22 (pl)
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_spearman
value: 42.3879320510127
task:
type: STS
- dataset:
config: tr
name: MTEB STS22 (tr)
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_spearman
value: 65.54048784845729
task:
type: STS
- dataset:
config: ar
name: MTEB STS22 (ar)
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_spearman
value: 58.55244068334867
task:
type: STS
- dataset:
config: ru
name: MTEB STS22 (ru)
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_spearman
value: 66.48710288440624
task:
type: STS
- dataset:
config: zh
name: MTEB STS22 (zh)
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_spearman
value: 66.585754901838
task:
type: STS
- dataset:
config: fr
name: MTEB STS22 (fr)
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_spearman
value: 81.03001290557805
task:
type: STS
- dataset:
config: de-en
name: MTEB STS22 (de-en)
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_spearman
value: 62.28001859884359
task:
type: STS
- dataset:
config: es-en
name: MTEB STS22 (es-en)
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_spearman
value: 79.64106342105019
task:
type: STS
- dataset:
config: it
name: MTEB STS22 (it)
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_spearman
value: 78.27915339361124
task:
type: STS
- dataset:
config: pl-en
name: MTEB STS22 (pl-en)
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_spearman
value: 78.28574268257462
task:
type: STS
- dataset:
config: zh-en
name: MTEB STS22 (zh-en)
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_spearman
value: 72.92658860751482
task:
type: STS
- dataset:
config: es-it
name: MTEB STS22 (es-it)
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_spearman
value: 74.83418886368217
task:
type: STS
- dataset:
config: de-fr
name: MTEB STS22 (de-fr)
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_spearman
value: 56.01064022625769
task:
type: STS
- dataset:
config: de-pl
name: MTEB STS22 (de-pl)
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_spearman
value: 53.64332829635126
task:
type: STS
- dataset:
config: fr-pl
name: MTEB STS22 (fr-pl)
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cos_sim_spearman
value: 73.24670207647144
task:
type: STS
- dataset:
config: default
name: MTEB STSB
revision: 0cde68302b3541bb8b3c340dc0644b0b745b3dc0
split: test
type: C-MTEB/STSB
metrics:
- type: cos_sim_spearman
value: 80.7157790971544
task:
type: STS
- dataset:
config: default
name: MTEB STSBenchmark
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
split: test
type: mteb/stsbenchmark-sts
metrics:
- type: cos_sim_spearman
value: 86.45763616928973
task:
type: STS
- dataset:
config: fr
name: MTEB STSBenchmarkMultilingualSTS (fr)
revision: 93d57ef91790589e3ce9c365164337a8a78b7632
split: test
type: stsb_multi_mt
metrics:
- type: cos_sim_spearman
value: 84.4335500335282
task:
type: STS
- dataset:
config: default
name: MTEB SciDocsRR
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
split: test
type: mteb/scidocs-reranking
metrics:
- type: map
value: 84.15276484499303
task:
type: Reranking
- dataset:
config: default
name: MTEB SciFact
revision: 0228b52cf27578f30900b9e5271d331663a030d7
split: test
type: mteb/scifact
metrics:
- type: ndcg_at_10
value: 73.433
task:
type: Retrieval
- dataset:
config: default
name: MTEB SciFact-PL
revision: 47932a35f045ef8ed01ba82bf9ff67f6e109207e
split: test
type: clarin-knext/scifact-pl
metrics:
- type: ndcg_at_10
value: 58.919999999999995
task:
type: Retrieval
- dataset:
config: default
name: MTEB SprintDuplicateQuestions
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
split: test
type: mteb/sprintduplicatequestions-pairclassification
metrics:
- type: cos_sim_ap
value: 95.40564890916419
task:
type: PairClassification
- dataset:
config: default
name: MTEB StackExchangeClustering
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
split: test
type: mteb/stackexchange-clustering
metrics:
- type: v_measure
value: 63.41856697730145
task:
type: Clustering
- dataset:
config: default
name: MTEB StackExchangeClusteringP2P
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
split: test
type: mteb/stackexchange-clustering-p2p
metrics:
- type: v_measure
value: 31.709285904909112
task:
type: Clustering
- dataset:
config: default
name: MTEB StackOverflowDupQuestions
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
split: test
type: mteb/stackoverflowdupquestions-reranking
metrics:
- type: map
value: 52.09341030060322
task:
type: Reranking
- dataset:
config: default
name: MTEB SummEval
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
split: test
type: mteb/summeval
metrics:
- type: cos_sim_spearman
value: 30.58262517835034
task:
type: Summarization
- dataset:
config: default
name: MTEB SummEvalFr
revision: b385812de6a9577b6f4d0f88c6a6e35395a94054
split: test
type: lyon-nlp/summarization-summeval-fr-p2p
metrics:
- type: cos_sim_spearman
value: 29.744542072951358
task:
type: Summarization
- dataset:
config: default
name: MTEB SyntecReranking
revision: b205c5084a0934ce8af14338bf03feb19499c84d
split: test
type: lyon-nlp/mteb-fr-reranking-syntec-s2p
metrics:
- type: map
value: 88.03333333333333
task:
type: Reranking
- dataset:
config: default
name: MTEB SyntecRetrieval
revision: 77f7e271bf4a92b24fce5119f3486b583ca016ff
split: test
type: lyon-nlp/mteb-fr-retrieval-syntec-s2p
metrics:
- type: ndcg_at_10
value: 83.043
task:
type: Retrieval
- dataset:
config: default
name: MTEB T2Reranking
revision: 76631901a18387f85eaa53e5450019b87ad58ef9
split: dev
type: C-MTEB/T2Reranking
metrics:
- type: map
value: 67.08577894804324
task:
type: Reranking
- dataset:
config: default
name: MTEB T2Retrieval
revision: 8731a845f1bf500a4f111cf1070785c793d10e64
split: dev
type: C-MTEB/T2Retrieval
metrics:
- type: ndcg_at_10
value: 84.718
task:
type: Retrieval
- dataset:
config: default
name: MTEB TNews
revision: 317f262bf1e6126357bbe89e875451e4b0938fe4
split: validation
type: C-MTEB/TNews-classification
metrics:
- type: accuracy
value: 48.726
task:
type: Classification
- dataset:
config: default
name: MTEB TRECCOVID
revision: None
split: test
type: mteb/trec-covid
metrics:
- type: ndcg_at_10
value: 57.56
task:
type: Retrieval
- dataset:
config: default
name: MTEB TRECCOVID-PL
revision: 81bcb408f33366c2a20ac54adafad1ae7e877fdd
split: test
type: clarin-knext/trec-covid-pl
metrics:
- type: ndcg_at_10
value: 59.355999999999995
task:
type: Retrieval
- dataset:
config: sqi-eng
name: MTEB Tatoeba (sqi-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 82.765
task:
type: BitextMining
- dataset:
config: fry-eng
name: MTEB Tatoeba (fry-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 73.69942196531792
task:
type: BitextMining
- dataset:
config: kur-eng
name: MTEB Tatoeba (kur-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 32.86585365853657
task:
type: BitextMining
- dataset:
config: tur-eng
name: MTEB Tatoeba (tur-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 95.81666666666666
task:
type: BitextMining
- dataset:
config: deu-eng
name: MTEB Tatoeba (deu-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 97.75
task:
type: BitextMining
- dataset:
config: nld-eng
name: MTEB Tatoeba (nld-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 93.78333333333335
task:
type: BitextMining
- dataset:
config: ron-eng
name: MTEB Tatoeba (ron-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 90.72333333333333
task:
type: BitextMining
- dataset:
config: ang-eng
name: MTEB Tatoeba (ang-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 42.45202558635395
task:
type: BitextMining
- dataset:
config: ido-eng
name: MTEB Tatoeba (ido-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 77.59238095238095
task:
type: BitextMining
- dataset:
config: jav-eng
name: MTEB Tatoeba (jav-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 35.69686411149825
task:
type: BitextMining
- dataset:
config: isl-eng
name: MTEB Tatoeba (isl-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 82.59333333333333
task:
type: BitextMining
- dataset:
config: slv-eng
name: MTEB Tatoeba (slv-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 84.1456922987907
task:
type: BitextMining
- dataset:
config: cym-eng
name: MTEB Tatoeba (cym-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 52.47462133594857
task:
type: BitextMining
- dataset:
config: kaz-eng
name: MTEB Tatoeba (kaz-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 67.62965440356746
task:
type: BitextMining
- dataset:
config: est-eng
name: MTEB Tatoeba (est-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 79.48412698412699
task:
type: BitextMining
- dataset:
config: heb-eng
name: MTEB Tatoeba (heb-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 75.85
task:
type: BitextMining
- dataset:
config: gla-eng
name: MTEB Tatoeba (gla-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 27.32600866497127
task:
type: BitextMining
- dataset:
config: mar-eng
name: MTEB Tatoeba (mar-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 84.38
task:
type: BitextMining
- dataset:
config: lat-eng
name: MTEB Tatoeba (lat-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 42.98888712165028
task:
type: BitextMining
- dataset:
config: bel-eng
name: MTEB Tatoeba (bel-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 85.55690476190476
task:
type: BitextMining
- dataset:
config: pms-eng
name: MTEB Tatoeba (pms-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 46.68466031323174
task:
type: BitextMining
- dataset:
config: gle-eng
name: MTEB Tatoeba (gle-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 32.73071428571428
task:
type: BitextMining
- dataset:
config: pes-eng
name: MTEB Tatoeba (pes-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 88.26333333333334
task:
type: BitextMining
- dataset:
config: nob-eng
name: MTEB Tatoeba (nob-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 96.61666666666666
task:
type: BitextMining
- dataset:
config: bul-eng
name: MTEB Tatoeba (bul-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 91.30666666666666
task:
type: BitextMining
- dataset:
config: cbk-eng
name: MTEB Tatoeba (cbk-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 70.03714285714285
task:
type: BitextMining
- dataset:
config: hun-eng
name: MTEB Tatoeba (hun-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 89.09
task:
type: BitextMining
- dataset:
config: uig-eng
name: MTEB Tatoeba (uig-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 59.570476190476185
task:
type: BitextMining
- dataset:
config: rus-eng
name: MTEB Tatoeba (rus-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 92.9
task:
type: BitextMining
- dataset:
config: spa-eng
name: MTEB Tatoeba (spa-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 97.68333333333334
task:
type: BitextMining
- dataset:
config: hye-eng
name: MTEB Tatoeba (hye-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 80.40880503144653
task:
type: BitextMining
- dataset:
config: tel-eng
name: MTEB Tatoeba (tel-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 89.7008547008547
task:
type: BitextMining
- dataset:
config: afr-eng
name: MTEB Tatoeba (afr-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 81.84833333333333
task:
type: BitextMining
- dataset:
config: mon-eng
name: MTEB Tatoeba (mon-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 71.69696969696969
task:
type: BitextMining
- dataset:
config: arz-eng
name: MTEB Tatoeba (arz-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 55.76985790822269
task:
type: BitextMining
- dataset:
config: hrv-eng
name: MTEB Tatoeba (hrv-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 91.66666666666666
task:
type: BitextMining
- dataset:
config: nov-eng
name: MTEB Tatoeba (nov-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 68.36668519547896
task:
type: BitextMining
- dataset:
config: gsw-eng
name: MTEB Tatoeba (gsw-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 36.73992673992674
task:
type: BitextMining
- dataset:
config: nds-eng
name: MTEB Tatoeba (nds-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 63.420952380952365
task:
type: BitextMining
- dataset:
config: ukr-eng
name: MTEB Tatoeba (ukr-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 91.28999999999999
task:
type: BitextMining
- dataset:
config: uzb-eng
name: MTEB Tatoeba (uzb-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 40.95392490046146
task:
type: BitextMining
- dataset:
config: lit-eng
name: MTEB Tatoeba (lit-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 77.58936507936508
task:
type: BitextMining
- dataset:
config: ina-eng
name: MTEB Tatoeba (ina-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 91.28999999999999
task:
type: BitextMining
- dataset:
config: lfn-eng
name: MTEB Tatoeba (lfn-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 63.563650793650794
task:
type: BitextMining
- dataset:
config: zsm-eng
name: MTEB Tatoeba (zsm-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 94.35
task:
type: BitextMining
- dataset:
config: ita-eng
name: MTEB Tatoeba (ita-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 91.43
task:
type: BitextMining
- dataset:
config: cmn-eng
name: MTEB Tatoeba (cmn-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 95.73333333333332
task:
type: BitextMining
- dataset:
config: lvs-eng
name: MTEB Tatoeba (lvs-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 79.38666666666667
task:
type: BitextMining
- dataset:
config: glg-eng
name: MTEB Tatoeba (glg-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 89.64
task:
type: BitextMining
- dataset:
config: ceb-eng
name: MTEB Tatoeba (ceb-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 21.257184628237262
task:
type: BitextMining
- dataset:
config: bre-eng
name: MTEB Tatoeba (bre-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 13.592316017316017
task:
type: BitextMining
- dataset:
config: ben-eng
name: MTEB Tatoeba (ben-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 73.22666666666666
task:
type: BitextMining
- dataset:
config: swg-eng
name: MTEB Tatoeba (swg-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 51.711309523809526
task:
type: BitextMining
- dataset:
config: arq-eng
name: MTEB Tatoeba (arq-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 24.98790634904795
task:
type: BitextMining
- dataset:
config: kab-eng
name: MTEB Tatoeba (kab-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 17.19218192918193
task:
type: BitextMining
- dataset:
config: fra-eng
name: MTEB Tatoeba (fra-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 93.26666666666667
task:
type: BitextMining
- dataset:
config: por-eng
name: MTEB Tatoeba (por-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 94.57333333333334
task:
type: BitextMining
- dataset:
config: tat-eng
name: MTEB Tatoeba (tat-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 42.35127206127206
task:
type: BitextMining
- dataset:
config: oci-eng
name: MTEB Tatoeba (oci-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 51.12318903318903
task:
type: BitextMining
- dataset:
config: pol-eng
name: MTEB Tatoeba (pol-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 94.89999999999999
task:
type: BitextMining
- dataset:
config: war-eng
name: MTEB Tatoeba (war-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 23.856320290390055
task:
type: BitextMining
- dataset:
config: aze-eng
name: MTEB Tatoeba (aze-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 79.52833333333334
task:
type: BitextMining
- dataset:
config: vie-eng
name: MTEB Tatoeba (vie-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 95.93333333333334
task:
type: BitextMining
- dataset:
config: nno-eng
name: MTEB Tatoeba (nno-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 90.75333333333333
task:
type: BitextMining
- dataset:
config: cha-eng
name: MTEB Tatoeba (cha-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 30.802919708029197
task:
type: BitextMining
- dataset:
config: mhr-eng
name: MTEB Tatoeba (mhr-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 15.984076294076294
task:
type: BitextMining
- dataset:
config: dan-eng
name: MTEB Tatoeba (dan-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 91.82666666666667
task:
type: BitextMining
- dataset:
config: ell-eng
name: MTEB Tatoeba (ell-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 91.9
task:
type: BitextMining
- dataset:
config: amh-eng
name: MTEB Tatoeba (amh-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 76.36054421768706
task:
type: BitextMining
- dataset:
config: pam-eng
name: MTEB Tatoeba (pam-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 9.232711399711398
task:
type: BitextMining
- dataset:
config: hsb-eng
name: MTEB Tatoeba (hsb-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 45.640803181175855
task:
type: BitextMining
- dataset:
config: srp-eng
name: MTEB Tatoeba (srp-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 86.29
task:
type: BitextMining
- dataset:
config: epo-eng
name: MTEB Tatoeba (epo-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 88.90833333333332
task:
type: BitextMining
- dataset:
config: kzj-eng
name: MTEB Tatoeba (kzj-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 11.11880248978075
task:
type: BitextMining
- dataset:
config: awa-eng
name: MTEB Tatoeba (awa-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 48.45839345839346
task:
type: BitextMining
- dataset:
config: fao-eng
name: MTEB Tatoeba (fao-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 65.68157033805888
task:
type: BitextMining
- dataset:
config: mal-eng
name: MTEB Tatoeba (mal-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 94.63852498786997
task:
type: BitextMining
- dataset:
config: ile-eng
name: MTEB Tatoeba (ile-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 81.67904761904761
task:
type: BitextMining
- dataset:
config: bos-eng
name: MTEB Tatoeba (bos-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 89.35969868173258
task:
type: BitextMining
- dataset:
config: cor-eng
name: MTEB Tatoeba (cor-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 5.957229437229437
task:
type: BitextMining
- dataset:
config: cat-eng
name: MTEB Tatoeba (cat-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 91.50333333333333
task:
type: BitextMining
- dataset:
config: eus-eng
name: MTEB Tatoeba (eus-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 63.75498778998778
task:
type: BitextMining
- dataset:
config: yue-eng
name: MTEB Tatoeba (yue-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 82.99190476190476
task:
type: BitextMining
- dataset:
config: swe-eng
name: MTEB Tatoeba (swe-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 92.95
task:
type: BitextMining
- dataset:
config: dtp-eng
name: MTEB Tatoeba (dtp-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 9.054042624042623
task:
type: BitextMining
- dataset:
config: kat-eng
name: MTEB Tatoeba (kat-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 72.77064981488574
task:
type: BitextMining
- dataset:
config: jpn-eng
name: MTEB Tatoeba (jpn-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 93.14
task:
type: BitextMining
- dataset:
config: csb-eng
name: MTEB Tatoeba (csb-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 29.976786498525627
task:
type: BitextMining
- dataset:
config: xho-eng
name: MTEB Tatoeba (xho-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 67.6525821596244
task:
type: BitextMining
- dataset:
config: orv-eng
name: MTEB Tatoeba (orv-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 33.12964812964813
task:
type: BitextMining
- dataset:
config: ind-eng
name: MTEB Tatoeba (ind-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 92.30666666666666
task:
type: BitextMining
- dataset:
config: tuk-eng
name: MTEB Tatoeba (tuk-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 34.36077879427633
task:
type: BitextMining
- dataset:
config: max-eng
name: MTEB Tatoeba (max-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 52.571845212690285
task:
type: BitextMining
- dataset:
config: swh-eng
name: MTEB Tatoeba (swh-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 58.13107263107262
task:
type: BitextMining
- dataset:
config: hin-eng
name: MTEB Tatoeba (hin-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 93.33333333333333
task:
type: BitextMining
- dataset:
config: dsb-eng
name: MTEB Tatoeba (dsb-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 42.87370133925458
task:
type: BitextMining
- dataset:
config: ber-eng
name: MTEB Tatoeba (ber-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 20.394327616827614
task:
type: BitextMining
- dataset:
config: tam-eng
name: MTEB Tatoeba (tam-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 84.29967426710098
task:
type: BitextMining
- dataset:
config: slk-eng
name: MTEB Tatoeba (slk-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 88.80666666666667
task:
type: BitextMining
- dataset:
config: tgl-eng
name: MTEB Tatoeba (tgl-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 67.23062271062273
task:
type: BitextMining
- dataset:
config: ast-eng
name: MTEB Tatoeba (ast-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 78.08398950131233
task:
type: BitextMining
- dataset:
config: mkd-eng
name: MTEB Tatoeba (mkd-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 77.85166666666666
task:
type: BitextMining
- dataset:
config: khm-eng
name: MTEB Tatoeba (khm-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 67.63004001231148
task:
type: BitextMining
- dataset:
config: ces-eng
name: MTEB Tatoeba (ces-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 89.77000000000001
task:
type: BitextMining
- dataset:
config: tzl-eng
name: MTEB Tatoeba (tzl-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 40.2654503616042
task:
type: BitextMining
- dataset:
config: urd-eng
name: MTEB Tatoeba (urd-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 83.90333333333334
task:
type: BitextMining
- dataset:
config: ara-eng
name: MTEB Tatoeba (ara-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 77.80666666666666
task:
type: BitextMining
- dataset:
config: kor-eng
name: MTEB Tatoeba (kor-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 84.08
task:
type: BitextMining
- dataset:
config: yid-eng
name: MTEB Tatoeba (yid-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 60.43098607367475
task:
type: BitextMining
- dataset:
config: fin-eng
name: MTEB Tatoeba (fin-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 88.19333333333333
task:
type: BitextMining
- dataset:
config: tha-eng
name: MTEB Tatoeba (tha-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 90.55352798053529
task:
type: BitextMining
- dataset:
config: wuu-eng
name: MTEB Tatoeba (wuu-eng)
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
split: test
type: mteb/tatoeba-bitext-mining
metrics:
- type: f1
value: 88.44999999999999
task:
type: BitextMining
- dataset:
config: default
name: MTEB ThuNewsClusteringP2P
revision: 5798586b105c0434e4f0fe5e767abe619442cf93
split: test
type: C-MTEB/ThuNewsClusteringP2P
metrics:
- type: v_measure
value: 57.25416429643288
task:
type: Clustering
- dataset:
config: default
name: MTEB ThuNewsClusteringS2S
revision: 8a8b2caeda43f39e13c4bc5bea0f8a667896e10d
split: test
type: C-MTEB/ThuNewsClusteringS2S
metrics:
- type: v_measure
value: 56.616646560243524
task:
type: Clustering
- dataset:
config: default
name: MTEB Touche2020
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
split: test
type: mteb/touche2020
metrics:
- type: ndcg_at_10
value: 22.819
task:
type: Retrieval
- dataset:
config: default
name: MTEB ToxicConversationsClassification
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
split: test
type: mteb/toxic_conversations_50k
metrics:
- type: accuracy
value: 71.02579999999999
task:
type: Classification
- dataset:
config: default
name: MTEB TweetSentimentExtractionClassification
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
split: test
type: mteb/tweet_sentiment_extraction
metrics:
- type: accuracy
value: 57.60045274476514
task:
type: Classification
- dataset:
config: default
name: MTEB TwentyNewsgroupsClustering
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
split: test
type: mteb/twentynewsgroups-clustering
metrics:
- type: v_measure
value: 50.346666699466205
task:
type: Clustering
- dataset:
config: default
name: MTEB TwitterSemEval2015
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
split: test
type: mteb/twittersemeval2015-pairclassification
metrics:
- type: cos_sim_ap
value: 71.88199004440489
task:
type: PairClassification
- dataset:
config: default
name: MTEB TwitterURLCorpus
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
split: test
type: mteb/twitterurlcorpus-pairclassification
metrics:
- type: cos_sim_ap
value: 85.41587779677383
task:
type: PairClassification
- dataset:
config: default
name: MTEB VideoRetrieval
revision: 58c2597a5943a2ba48f4668c3b90d796283c5639
split: dev
type: C-MTEB/VideoRetrieval
metrics:
- type: ndcg_at_10
value: 72.792
task:
type: Retrieval
- dataset:
config: default
name: MTEB Waimai
revision: 339287def212450dcaa9df8c22bf93e9980c7023
split: test
type: C-MTEB/waimai-classification
metrics:
- type: accuracy
value: 82.58000000000001
task:
type: Classification
- dataset:
config: fr
name: MTEB XPQARetrieval (fr)
revision: c99d599f0a6ab9b85b065da6f9d94f9cf731679f
split: test
type: jinaai/xpqa
metrics:
- type: ndcg_at_10
value: 67.327
task:
type: Retrieval
tags:
- mteb
- multilingual
- sentence-similarity
- onnx
- teradata
---
***See Disclaimer below***
----
# A Teradata Vantage compatible Embeddings Model
# Alibaba-NLP/gte-multilingual-base
## Overview of this Model
An Embedding Model which maps text (sentence/ paragraphs) into a vector. The [Alibaba-NLP/gte-multilingual-base](https://huggingface.co./Alibaba-NLP/gte-multilingual-base) 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.
- 305.37M params (Sizes in ONNX format - "fp32": 1197.36MB, "int8": 324.17MB, "uint8": 324.17MB)
- 8192 maximum input tokens
- 768 dimensions of output vector
- Licence: apache-2.0. The released models can be used for commercial purposes free of charge.
- Reference to Original Model: https://huggingface.co./Alibaba-NLP/gte-multilingual-base
## 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.
```python
import teradataml as tdml
import getpass
from huggingface_hub import hf_hub_download
model_name = "gte-multilingual-base"
number_dimensions_output = 768
model_file_name = "model.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](https://github.com/Teradata/jupyter-demos/blob/main/UseCases/Language_Models_InVantage/Semantic_Clustering_Python.ipynb)
- **Semantic Distance with TD_VectorDistance:** [Semantic Similarity Python Notebook](https://github.com/Teradata/jupyter-demos/blob/main/UseCases/Language_Models_InVantage/Semantic_Similarity_Python.ipynb)
- **RAG-Based Application with TD_VectorDistance:** [RAG and Bedrock Query PDF Notebook](https://github.com/Teradata/jupyter-demos/blob/main/UseCases/Language_Models_InVantage/RAG_and_Bedrock_QueryPDF.ipynb)
## 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 [Alibaba-NLP/gte-multilingual-base](https://huggingface.co./Alibaba-NLP/gte-multilingual-base) model from Hugging Face.
To enhance performance and ensure compatibility with various execution environments, we'll use the [Optimum](https://github.com/huggingface/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: fp32, int8, uint8
You can find the detailed conversion steps in the file [convert.py](./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.
```python
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("Alibaba-NLP/gte-multilingual-base")
predef_sess = rt.InferenceSession("onnx/model.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](./test_local.py)
-----
DISCLAIMER: The content herein (“Content”) is provided “AS IS” and is not covered by any Teradata Operations, Inc. and its affiliates (“Teradata”) agreements. Its listing here does not constitute certification or endorsement by Teradata.
To the extent any of the Content contains or is related to any artificial intelligence (“AI”) or other language learning models (“Models”) that interoperate with the products and services of Teradata, by accessing, bringing, deploying or using such Models, you acknowledge and agree that you are solely responsible for ensuring compliance with all applicable laws, regulations, and restrictions governing the use, deployment, and distribution of AI technologies. This includes, but is not limited to, AI Diffusion Rules, European Union AI Act, AI-related laws and regulations, privacy laws, export controls, and financial or sector-specific regulations.
While Teradata may provide support, guidance, or assistance in the deployment or implementation of Models to interoperate with Teradata’s products and/or services, you remain fully responsible for ensuring that your Models, data, and applications comply with all relevant legal and regulatory obligations. Our assistance does not constitute legal or regulatory approval, and Teradata disclaims any liability arising from non-compliance with applicable laws.
You must determine the suitability of the Models for any purpose. Given the probabilistic nature of machine learning and modeling, the use of the Models may in some situations result in incorrect output that does not accurately reflect the action generated. You should evaluate the accuracy of any output as appropriate for your use case, including by using human review of the output.
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