metadata
language:
- en
license: mit
tags:
- mteb
- sentence-transformers
- transformers
model-index:
- name: e5-mistral-7b-instruct
results:
- task:
type: STS
dataset:
name: MTEB AFQMC
type: C-MTEB/AFQMC
config: default
split: validation
revision: None
metrics:
- type: cos_sim_pearson
value: 37.863226091673866
- type: cos_sim_spearman
value: 38.98733013335281
- type: euclidean_pearson
value: 37.51783380497874
- type: euclidean_spearman
value: 38.98733012753365
- type: manhattan_pearson
value: 37.26706888081721
- type: manhattan_spearman
value: 38.709750161903834
- task:
type: STS
dataset:
name: MTEB ATEC
type: C-MTEB/ATEC
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 43.33924583134623
- type: cos_sim_spearman
value: 42.84316155158754
- type: euclidean_pearson
value: 45.62709879515238
- type: euclidean_spearman
value: 42.843155921732404
- type: manhattan_pearson
value: 45.4786950991229
- type: manhattan_spearman
value: 42.657334751855984
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en)
type: mteb/amazon_counterfactual
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 78.68656716417911
- type: ap
value: 41.71522322900398
- type: f1
value: 72.37207703532552
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (de)
type: mteb/amazon_counterfactual
config: de
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 74.04710920770879
- type: ap
value: 83.42622221864045
- type: f1
value: 72.14388257905772
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (en-ext)
type: mteb/amazon_counterfactual
config: en-ext
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 77.93103448275862
- type: ap
value: 26.039284760509513
- type: f1
value: 64.81092954450712
- task:
type: Classification
dataset:
name: MTEB AmazonCounterfactualClassification (ja)
type: mteb/amazon_counterfactual
config: ja
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 77.21627408993577
- type: ap
value: 24.876490553983036
- type: f1
value: 63.8773359684989
- task:
type: Classification
dataset:
name: MTEB AmazonPolarityClassification
type: mteb/amazon_polarity
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 95.90679999999999
- type: ap
value: 94.32357863164454
- type: f1
value: 95.90485634708557
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (en)
type: mteb/amazon_reviews_multi
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 55.786
- type: f1
value: 55.31211995815146
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (de)
type: mteb/amazon_reviews_multi
config: de
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 53.26
- type: f1
value: 52.156230111544986
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (es)
type: mteb/amazon_reviews_multi
config: es
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 50.33
- type: f1
value: 49.195023008878145
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (fr)
type: mteb/amazon_reviews_multi
config: fr
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 49.3
- type: f1
value: 48.434470184108
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (ja)
type: mteb/amazon_reviews_multi
config: ja
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 48.68599999999999
- type: f1
value: 47.62681775202072
- task:
type: Classification
dataset:
name: MTEB AmazonReviewsClassification (zh)
type: mteb/amazon_reviews_multi
config: zh
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 46.238
- type: f1
value: 45.014030559653705
- task:
type: Retrieval
dataset:
name: MTEB ArguAna
type: arguana
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 36.486000000000004
- type: map_at_10
value: 53.076
- type: map_at_100
value: 53.657999999999994
- type: map_at_1000
value: 53.659
- type: map_at_3
value: 48.234
- type: map_at_5
value: 51.121
- type: mrr_at_1
value: 37.269000000000005
- type: mrr_at_10
value: 53.335
- type: mrr_at_100
value: 53.916
- type: mrr_at_1000
value: 53.918
- type: mrr_at_3
value: 48.518
- type: mrr_at_5
value: 51.406
- type: ndcg_at_1
value: 36.486000000000004
- type: ndcg_at_10
value: 61.882000000000005
- type: ndcg_at_100
value: 64.165
- type: ndcg_at_1000
value: 64.203
- type: ndcg_at_3
value: 52.049
- type: ndcg_at_5
value: 57.199
- type: precision_at_1
value: 36.486000000000004
- type: precision_at_10
value: 8.982999999999999
- type: precision_at_100
value: 0.9939999999999999
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 21.029
- type: precision_at_5
value: 15.092
- type: recall_at_1
value: 36.486000000000004
- type: recall_at_10
value: 89.82900000000001
- type: recall_at_100
value: 99.36
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 63.087
- type: recall_at_5
value: 75.46199999999999
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringP2P
type: mteb/arxiv-clustering-p2p
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 50.45119266859667
- task:
type: Clustering
dataset:
name: MTEB ArxivClusteringS2S
type: mteb/arxiv-clustering-s2s
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 45.4958298992051
- task:
type: Reranking
dataset:
name: MTEB AskUbuntuDupQuestions
type: mteb/askubuntudupquestions-reranking
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 66.98177472838887
- type: mrr
value: 79.91854636591478
- task:
type: STS
dataset:
name: MTEB BIOSSES
type: mteb/biosses-sts
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 87.67086498650698
- type: cos_sim_spearman
value: 85.54773239564638
- type: euclidean_pearson
value: 86.48229161588425
- type: euclidean_spearman
value: 85.54773239564638
- type: manhattan_pearson
value: 86.67533327742343
- type: manhattan_spearman
value: 85.76099026691983
- task:
type: STS
dataset:
name: MTEB BQ
type: C-MTEB/BQ
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 50.31998888922809
- type: cos_sim_spearman
value: 50.6369940530675
- type: euclidean_pearson
value: 50.055544636296055
- type: euclidean_spearman
value: 50.63699405154838
- type: manhattan_pearson
value: 50.00739378036807
- type: manhattan_spearman
value: 50.607237418676945
- task:
type: BitextMining
dataset:
name: MTEB BUCC (de-en)
type: mteb/bucc-bitext-mining
config: de-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 99.5615866388309
- type: f1
value: 99.49895615866389
- type: precision
value: 99.46764091858039
- type: recall
value: 99.5615866388309
- task:
type: BitextMining
dataset:
name: MTEB BUCC (fr-en)
type: mteb/bucc-bitext-mining
config: fr-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 99.19656614571869
- type: f1
value: 99.08650671362535
- type: precision
value: 99.0314769975787
- type: recall
value: 99.19656614571869
- task:
type: BitextMining
dataset:
name: MTEB BUCC (ru-en)
type: mteb/bucc-bitext-mining
config: ru-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 98.0256321440942
- type: f1
value: 97.83743216718624
- type: precision
value: 97.74390947927492
- type: recall
value: 98.0256321440942
- task:
type: BitextMining
dataset:
name: MTEB BUCC (zh-en)
type: mteb/bucc-bitext-mining
config: zh-en
split: test
revision: d51519689f32196a32af33b075a01d0e7c51e252
metrics:
- type: accuracy
value: 99.26276987888363
- type: f1
value: 99.22766368264
- type: precision
value: 99.21011058451816
- type: recall
value: 99.26276987888363
- task:
type: Classification
dataset:
name: MTEB Banking77Classification
type: mteb/banking77
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 88.22727272727272
- type: f1
value: 88.17411732496673
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringP2P
type: mteb/biorxiv-clustering-p2p
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 43.530637846246975
- task:
type: Clustering
dataset:
name: MTEB BiorxivClusteringS2S
type: mteb/biorxiv-clustering-s2s
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 40.23505728593893
- task:
type: Clustering
dataset:
name: MTEB CLSClusteringP2P
type: C-MTEB/CLSClusteringP2P
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 44.419028279451275
- task:
type: Clustering
dataset:
name: MTEB CLSClusteringS2S
type: C-MTEB/CLSClusteringS2S
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 42.5820277929776
- task:
type: Reranking
dataset:
name: MTEB CMedQAv1
type: C-MTEB/CMedQAv1-reranking
config: default
split: test
revision: None
metrics:
- type: map
value: 77.67811726152972
- type: mrr
value: 80.99003968253969
- task:
type: Reranking
dataset:
name: MTEB CMedQAv2
type: C-MTEB/CMedQAv2-reranking
config: default
split: test
revision: None
metrics:
- type: map
value: 78.66055354534922
- type: mrr
value: 81.66119047619047
- task:
type: Retrieval
dataset:
name: MTEB CQADupstackRetrieval
type: BeIR/cqadupstack
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 27.162333333333333
- type: map_at_10
value: 37.22291666666667
- type: map_at_100
value: 38.56733333333333
- type: map_at_1000
value: 38.684250000000006
- type: map_at_3
value: 34.22858333333333
- type: map_at_5
value: 35.852500000000006
- type: mrr_at_1
value: 32.459833333333336
- type: mrr_at_10
value: 41.65358333333333
- type: mrr_at_100
value: 42.566916666666664
- type: mrr_at_1000
value: 42.61766666666667
- type: mrr_at_3
value: 39.210499999999996
- type: mrr_at_5
value: 40.582166666666666
- type: ndcg_at_1
value: 32.459833333333336
- type: ndcg_at_10
value: 42.96758333333333
- type: ndcg_at_100
value: 48.5065
- type: ndcg_at_1000
value: 50.556583333333336
- type: ndcg_at_3
value: 38.004416666666664
- type: ndcg_at_5
value: 40.25916666666667
- type: precision_at_1
value: 32.459833333333336
- type: precision_at_10
value: 7.664583333333333
- type: precision_at_100
value: 1.2349999999999999
- type: precision_at_1000
value: 0.15966666666666668
- type: precision_at_3
value: 17.731166666666663
- type: precision_at_5
value: 12.575333333333335
- type: recall_at_1
value: 27.162333333333333
- type: recall_at_10
value: 55.44158333333334
- type: recall_at_100
value: 79.56966666666666
- type: recall_at_1000
value: 93.45224999999999
- type: recall_at_3
value: 41.433083333333336
- type: recall_at_5
value: 47.31108333333333
- task:
type: Retrieval
dataset:
name: MTEB ClimateFEVER
type: climate-fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 16.539
- type: map_at_10
value: 28.494999999999997
- type: map_at_100
value: 30.568
- type: map_at_1000
value: 30.741000000000003
- type: map_at_3
value: 23.846999999999998
- type: map_at_5
value: 26.275
- type: mrr_at_1
value: 37.394
- type: mrr_at_10
value: 50.068
- type: mrr_at_100
value: 50.727
- type: mrr_at_1000
value: 50.751000000000005
- type: mrr_at_3
value: 46.938
- type: mrr_at_5
value: 48.818
- type: ndcg_at_1
value: 37.394
- type: ndcg_at_10
value: 38.349
- type: ndcg_at_100
value: 45.512
- type: ndcg_at_1000
value: 48.321
- type: ndcg_at_3
value: 32.172
- type: ndcg_at_5
value: 34.265
- type: precision_at_1
value: 37.394
- type: precision_at_10
value: 11.927999999999999
- type: precision_at_100
value: 1.966
- type: precision_at_1000
value: 0.25
- type: precision_at_3
value: 24.126
- type: precision_at_5
value: 18.306
- type: recall_at_1
value: 16.539
- type: recall_at_10
value: 44.504
- type: recall_at_100
value: 68.605
- type: recall_at_1000
value: 84.1
- type: recall_at_3
value: 29.008
- type: recall_at_5
value: 35.58
- task:
type: Retrieval
dataset:
name: MTEB CmedqaRetrieval
type: C-MTEB/CmedqaRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 19.482
- type: map_at_10
value: 28.622999999999998
- type: map_at_100
value: 30.262
- type: map_at_1000
value: 30.432
- type: map_at_3
value: 25.647
- type: map_at_5
value: 27.128000000000004
- type: mrr_at_1
value: 30.408
- type: mrr_at_10
value: 37.188
- type: mrr_at_100
value: 38.196000000000005
- type: mrr_at_1000
value: 38.273
- type: mrr_at_3
value: 35.067
- type: mrr_at_5
value: 36.124
- type: ndcg_at_1
value: 30.408
- type: ndcg_at_10
value: 34.215
- type: ndcg_at_100
value: 41.349999999999994
- type: ndcg_at_1000
value: 44.689
- type: ndcg_at_3
value: 30.264999999999997
- type: ndcg_at_5
value: 31.572
- type: precision_at_1
value: 30.408
- type: precision_at_10
value: 7.6770000000000005
- type: precision_at_100
value: 1.352
- type: precision_at_1000
value: 0.178
- type: precision_at_3
value: 17.213
- type: precision_at_5
value: 12.198
- type: recall_at_1
value: 19.482
- type: recall_at_10
value: 42.368
- type: recall_at_100
value: 72.694
- type: recall_at_1000
value: 95.602
- type: recall_at_3
value: 30.101
- type: recall_at_5
value: 34.708
- task:
type: PairClassification
dataset:
name: MTEB Cmnli
type: C-MTEB/CMNLI
config: default
split: validation
revision: None
metrics:
- type: cos_sim_accuracy
value: 71.16055321707758
- type: cos_sim_ap
value: 80.21073839711723
- type: cos_sim_f1
value: 72.9740932642487
- type: cos_sim_precision
value: 65.53136050623488
- type: cos_sim_recall
value: 82.3240589198036
- type: dot_accuracy
value: 71.16055321707758
- type: dot_ap
value: 80.212299264122
- type: dot_f1
value: 72.9740932642487
- type: dot_precision
value: 65.53136050623488
- type: dot_recall
value: 82.3240589198036
- type: euclidean_accuracy
value: 71.16055321707758
- type: euclidean_ap
value: 80.21076298680417
- type: euclidean_f1
value: 72.9740932642487
- type: euclidean_precision
value: 65.53136050623488
- type: euclidean_recall
value: 82.3240589198036
- type: manhattan_accuracy
value: 70.71557426337944
- type: manhattan_ap
value: 79.93448977199749
- type: manhattan_f1
value: 72.83962726826877
- type: manhattan_precision
value: 62.7407908077053
- type: manhattan_recall
value: 86.81318681318682
- type: max_accuracy
value: 71.16055321707758
- type: max_ap
value: 80.212299264122
- type: max_f1
value: 72.9740932642487
- task:
type: Retrieval
dataset:
name: MTEB CovidRetrieval
type: C-MTEB/CovidRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 60.643
- type: map_at_10
value: 69.011
- type: map_at_100
value: 69.533
- type: map_at_1000
value: 69.545
- type: map_at_3
value: 67.167
- type: map_at_5
value: 68.12700000000001
- type: mrr_at_1
value: 60.801
- type: mrr_at_10
value: 69.111
- type: mrr_at_100
value: 69.6
- type: mrr_at_1000
value: 69.611
- type: mrr_at_3
value: 67.229
- type: mrr_at_5
value: 68.214
- type: ndcg_at_1
value: 60.801
- type: ndcg_at_10
value: 73.128
- type: ndcg_at_100
value: 75.614
- type: ndcg_at_1000
value: 75.92
- type: ndcg_at_3
value: 69.261
- type: ndcg_at_5
value: 70.973
- type: precision_at_1
value: 60.801
- type: precision_at_10
value: 8.662
- type: precision_at_100
value: 0.9860000000000001
- type: precision_at_1000
value: 0.101
- type: precision_at_3
value: 25.149
- type: precision_at_5
value: 15.953999999999999
- type: recall_at_1
value: 60.643
- type: recall_at_10
value: 85.959
- type: recall_at_100
value: 97.576
- type: recall_at_1000
value: 100
- type: recall_at_3
value: 75.184
- type: recall_at_5
value: 79.32000000000001
- task:
type: Retrieval
dataset:
name: MTEB DBPedia
type: dbpedia-entity
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 10.183
- type: map_at_10
value: 23.958
- type: map_at_100
value: 34.354
- type: map_at_1000
value: 36.442
- type: map_at_3
value: 16.345000000000002
- type: map_at_5
value: 19.647000000000002
- type: mrr_at_1
value: 74.25
- type: mrr_at_10
value: 80.976
- type: mrr_at_100
value: 81.256
- type: mrr_at_1000
value: 81.262
- type: mrr_at_3
value: 79.958
- type: mrr_at_5
value: 80.37100000000001
- type: ndcg_at_1
value: 62
- type: ndcg_at_10
value: 48.894999999999996
- type: ndcg_at_100
value: 53.867
- type: ndcg_at_1000
value: 61.304
- type: ndcg_at_3
value: 53.688
- type: ndcg_at_5
value: 50.900999999999996
- type: precision_at_1
value: 74.25
- type: precision_at_10
value: 39.525
- type: precision_at_100
value: 12.323
- type: precision_at_1000
value: 2.539
- type: precision_at_3
value: 57.49999999999999
- type: precision_at_5
value: 49.1
- type: recall_at_1
value: 10.183
- type: recall_at_10
value: 29.296
- type: recall_at_100
value: 60.394999999999996
- type: recall_at_1000
value: 83.12
- type: recall_at_3
value: 17.495
- type: recall_at_5
value: 22.235
- task:
type: Retrieval
dataset:
name: MTEB DuRetrieval
type: C-MTEB/DuRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 26.613999999999997
- type: map_at_10
value: 79.77300000000001
- type: map_at_100
value: 82.71
- type: map_at_1000
value: 82.75
- type: map_at_3
value: 55.92700000000001
- type: map_at_5
value: 70.085
- type: mrr_at_1
value: 90.7
- type: mrr_at_10
value: 93.438
- type: mrr_at_100
value: 93.504
- type: mrr_at_1000
value: 93.50699999999999
- type: mrr_at_3
value: 93.125
- type: mrr_at_5
value: 93.34
- type: ndcg_at_1
value: 90.7
- type: ndcg_at_10
value: 87.023
- type: ndcg_at_100
value: 90.068
- type: ndcg_at_1000
value: 90.43299999999999
- type: ndcg_at_3
value: 86.339
- type: ndcg_at_5
value: 85.013
- type: precision_at_1
value: 90.7
- type: precision_at_10
value: 41.339999999999996
- type: precision_at_100
value: 4.806
- type: precision_at_1000
value: 0.48900000000000005
- type: precision_at_3
value: 76.983
- type: precision_at_5
value: 64.69
- type: recall_at_1
value: 26.613999999999997
- type: recall_at_10
value: 87.681
- type: recall_at_100
value: 97.44699999999999
- type: recall_at_1000
value: 99.348
- type: recall_at_3
value: 57.809999999999995
- type: recall_at_5
value: 74.258
- task:
type: Retrieval
dataset:
name: MTEB EcomRetrieval
type: C-MTEB/EcomRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 30.9
- type: map_at_10
value: 40.467
- type: map_at_100
value: 41.423
- type: map_at_1000
value: 41.463
- type: map_at_3
value: 37.25
- type: map_at_5
value: 39.31
- type: mrr_at_1
value: 30.9
- type: mrr_at_10
value: 40.467
- type: mrr_at_100
value: 41.423
- type: mrr_at_1000
value: 41.463
- type: mrr_at_3
value: 37.25
- type: mrr_at_5
value: 39.31
- type: ndcg_at_1
value: 30.9
- type: ndcg_at_10
value: 45.957
- type: ndcg_at_100
value: 50.735
- type: ndcg_at_1000
value: 51.861999999999995
- type: ndcg_at_3
value: 39.437
- type: ndcg_at_5
value: 43.146
- type: precision_at_1
value: 30.9
- type: precision_at_10
value: 6.35
- type: precision_at_100
value: 0.861
- type: precision_at_1000
value: 0.095
- type: precision_at_3
value: 15.267
- type: precision_at_5
value: 10.96
- type: recall_at_1
value: 30.9
- type: recall_at_10
value: 63.5
- type: recall_at_100
value: 86.1
- type: recall_at_1000
value: 95.1
- type: recall_at_3
value: 45.800000000000004
- type: recall_at_5
value: 54.800000000000004
- task:
type: Classification
dataset:
name: MTEB EmotionClassification
type: mteb/emotion
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 49.765
- type: f1
value: 45.93242203574485
- task:
type: Retrieval
dataset:
name: MTEB FEVER
type: fever
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 75.138
- type: map_at_10
value: 84.21300000000001
- type: map_at_100
value: 84.43
- type: map_at_1000
value: 84.441
- type: map_at_3
value: 83.071
- type: map_at_5
value: 83.853
- type: mrr_at_1
value: 80.948
- type: mrr_at_10
value: 88.175
- type: mrr_at_100
value: 88.24
- type: mrr_at_1000
value: 88.241
- type: mrr_at_3
value: 87.516
- type: mrr_at_5
value: 87.997
- type: ndcg_at_1
value: 80.948
- type: ndcg_at_10
value: 87.84100000000001
- type: ndcg_at_100
value: 88.576
- type: ndcg_at_1000
value: 88.75699999999999
- type: ndcg_at_3
value: 86.176
- type: ndcg_at_5
value: 87.214
- type: precision_at_1
value: 80.948
- type: precision_at_10
value: 10.632
- type: precision_at_100
value: 1.123
- type: precision_at_1000
value: 0.11499999999999999
- type: precision_at_3
value: 33.193
- type: precision_at_5
value: 20.663
- type: recall_at_1
value: 75.138
- type: recall_at_10
value: 94.89699999999999
- type: recall_at_100
value: 97.751
- type: recall_at_1000
value: 98.833
- type: recall_at_3
value: 90.455
- type: recall_at_5
value: 93.085
- task:
type: Retrieval
dataset:
name: MTEB FiQA2018
type: fiqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 29.45
- type: map_at_10
value: 48.596000000000004
- type: map_at_100
value: 50.70400000000001
- type: map_at_1000
value: 50.83800000000001
- type: map_at_3
value: 42.795
- type: map_at_5
value: 46.085
- type: mrr_at_1
value: 56.172999999999995
- type: mrr_at_10
value: 64.35300000000001
- type: mrr_at_100
value: 64.947
- type: mrr_at_1000
value: 64.967
- type: mrr_at_3
value: 62.653999999999996
- type: mrr_at_5
value: 63.534
- type: ndcg_at_1
value: 56.172999999999995
- type: ndcg_at_10
value: 56.593
- type: ndcg_at_100
value: 62.942
- type: ndcg_at_1000
value: 64.801
- type: ndcg_at_3
value: 53.024
- type: ndcg_at_5
value: 53.986999999999995
- type: precision_at_1
value: 56.172999999999995
- type: precision_at_10
value: 15.494
- type: precision_at_100
value: 2.222
- type: precision_at_1000
value: 0.254
- type: precision_at_3
value: 35.185
- type: precision_at_5
value: 25.556
- type: recall_at_1
value: 29.45
- type: recall_at_10
value: 62.882000000000005
- type: recall_at_100
value: 85.56099999999999
- type: recall_at_1000
value: 96.539
- type: recall_at_3
value: 47.911
- type: recall_at_5
value: 54.52
- task:
type: Retrieval
dataset:
name: MTEB HotpotQA
type: hotpotqa
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 39.581
- type: map_at_10
value: 68.401
- type: map_at_100
value: 69.207
- type: map_at_1000
value: 69.25200000000001
- type: map_at_3
value: 64.689
- type: map_at_5
value: 67.158
- type: mrr_at_1
value: 79.163
- type: mrr_at_10
value: 85.22999999999999
- type: mrr_at_100
value: 85.386
- type: mrr_at_1000
value: 85.39099999999999
- type: mrr_at_3
value: 84.432
- type: mrr_at_5
value: 84.952
- type: ndcg_at_1
value: 79.163
- type: ndcg_at_10
value: 75.721
- type: ndcg_at_100
value: 78.411
- type: ndcg_at_1000
value: 79.23599999999999
- type: ndcg_at_3
value: 70.68799999999999
- type: ndcg_at_5
value: 73.694
- type: precision_at_1
value: 79.163
- type: precision_at_10
value: 16.134
- type: precision_at_100
value: 1.821
- type: precision_at_1000
value: 0.193
- type: precision_at_3
value: 46.446
- type: precision_at_5
value: 30.242
- type: recall_at_1
value: 39.581
- type: recall_at_10
value: 80.66799999999999
- type: recall_at_100
value: 91.033
- type: recall_at_1000
value: 96.408
- type: recall_at_3
value: 69.669
- type: recall_at_5
value: 75.604
- task:
type: Classification
dataset:
name: MTEB IFlyTek
type: C-MTEB/IFlyTek-classification
config: default
split: validation
revision: None
metrics:
- type: accuracy
value: 45.04809542131589
- type: f1
value: 37.01181779071118
- task:
type: Classification
dataset:
name: MTEB ImdbClassification
type: mteb/imdb
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 94.78120000000001
- type: ap
value: 92.52931921594387
- type: f1
value: 94.77902110732532
- task:
type: Classification
dataset:
name: MTEB JDReview
type: C-MTEB/JDReview-classification
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 85.81613508442777
- type: ap
value: 52.430320593468394
- type: f1
value: 79.95467268178068
- task:
type: STS
dataset:
name: MTEB LCQMC
type: C-MTEB/LCQMC
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 71.05801751913393
- type: cos_sim_spearman
value: 75.47954644971965
- type: euclidean_pearson
value: 74.27472296759713
- type: euclidean_spearman
value: 75.47954201369866
- type: manhattan_pearson
value: 74.30508190186474
- type: manhattan_spearman
value: 75.51326518159436
- task:
type: Reranking
dataset:
name: MTEB MMarcoReranking
type: C-MTEB/Mmarco-reranking
config: default
split: dev
revision: None
metrics:
- type: map
value: 24.21110921666315
- type: mrr
value: 22.863492063492064
- task:
type: Retrieval
dataset:
name: MTEB MMarcoRetrieval
type: C-MTEB/MMarcoRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 61.38400000000001
- type: map_at_10
value: 70.895
- type: map_at_100
value: 71.314
- type: map_at_1000
value: 71.331
- type: map_at_3
value: 69.016
- type: map_at_5
value: 70.179
- type: mrr_at_1
value: 63.481
- type: mrr_at_10
value: 71.543
- type: mrr_at_100
value: 71.91300000000001
- type: mrr_at_1000
value: 71.928
- type: mrr_at_3
value: 69.90899999999999
- type: mrr_at_5
value: 70.907
- type: ndcg_at_1
value: 63.481
- type: ndcg_at_10
value: 74.833
- type: ndcg_at_100
value: 76.705
- type: ndcg_at_1000
value: 77.13600000000001
- type: ndcg_at_3
value: 71.236
- type: ndcg_at_5
value: 73.199
- type: precision_at_1
value: 63.481
- type: precision_at_10
value: 9.179
- type: precision_at_100
value: 1.011
- type: precision_at_1000
value: 0.105
- type: precision_at_3
value: 27.044
- type: precision_at_5
value: 17.272000000000002
- type: recall_at_1
value: 61.38400000000001
- type: recall_at_10
value: 86.318
- type: recall_at_100
value: 94.786
- type: recall_at_1000
value: 98.14500000000001
- type: recall_at_3
value: 76.717
- type: recall_at_5
value: 81.416
- task:
type: Retrieval
dataset:
name: MTEB MSMARCO
type: msmarco
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 23.363999999999997
- type: map_at_10
value: 36.022
- type: map_at_100
value: 37.229
- type: map_at_1000
value: 37.274
- type: map_at_3
value: 32.131
- type: map_at_5
value: 34.391
- type: mrr_at_1
value: 24.069
- type: mrr_at_10
value: 36.620000000000005
- type: mrr_at_100
value: 37.769999999999996
- type: mrr_at_1000
value: 37.809
- type: mrr_at_3
value: 32.846
- type: mrr_at_5
value: 35.02
- type: ndcg_at_1
value: 24.069
- type: ndcg_at_10
value: 43.056
- type: ndcg_at_100
value: 48.754
- type: ndcg_at_1000
value: 49.829
- type: ndcg_at_3
value: 35.167
- type: ndcg_at_5
value: 39.168
- type: precision_at_1
value: 24.069
- type: precision_at_10
value: 6.762
- type: precision_at_100
value: 0.96
- type: precision_at_1000
value: 0.105
- type: precision_at_3
value: 14.957
- type: precision_at_5
value: 11.023
- type: recall_at_1
value: 23.363999999999997
- type: recall_at_10
value: 64.696
- type: recall_at_100
value: 90.795
- type: recall_at_1000
value: 98.892
- type: recall_at_3
value: 43.247
- type: recall_at_5
value: 52.86300000000001
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (en)
type: mteb/mtop_domain
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 96.11947104423166
- type: f1
value: 95.89561841159332
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (de)
type: mteb/mtop_domain
config: de
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 92.97548605240912
- type: f1
value: 92.17133696717212
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (es)
type: mteb/mtop_domain
config: es
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 93.37224816544364
- type: f1
value: 93.19978829237863
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (fr)
type: mteb/mtop_domain
config: fr
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 91.28719072972127
- type: f1
value: 91.28448045979604
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (hi)
type: mteb/mtop_domain
config: hi
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 88.8131946934385
- type: f1
value: 88.27883019362747
- task:
type: Classification
dataset:
name: MTEB MTOPDomainClassification (th)
type: mteb/mtop_domain
config: th
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 85.52260397830018
- type: f1
value: 85.15528226728568
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (en)
type: mteb/mtop_intent
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 86.10807113543093
- type: f1
value: 70.88498219072167
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (de)
type: mteb/mtop_intent
config: de
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 77.77120315581854
- type: f1
value: 57.97153920153224
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (es)
type: mteb/mtop_intent
config: es
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 79.93995997331554
- type: f1
value: 58.839203810064866
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (fr)
type: mteb/mtop_intent
config: fr
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 77.801440651425
- type: f1
value: 58.68009647839332
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (hi)
type: mteb/mtop_intent
config: hi
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 72.90785227680172
- type: f1
value: 49.83760954655788
- task:
type: Classification
dataset:
name: MTEB MTOPIntentClassification (th)
type: mteb/mtop_intent
config: th
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 73.24050632911391
- type: f1
value: 52.0562553541082
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (af)
type: mteb/amazon_massive_intent
config: af
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 66.47948890383321
- type: f1
value: 63.334877563135485
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (am)
type: mteb/amazon_massive_intent
config: am
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 44.2871553463349
- type: f1
value: 43.17658050605427
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (ar)
type: mteb/amazon_massive_intent
config: ar
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 63.174176193678555
- type: f1
value: 59.236659587042425
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (az)
type: mteb/amazon_massive_intent
config: az
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 64.226630800269
- type: f1
value: 60.951842696956184
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (bn)
type: mteb/amazon_massive_intent
config: bn
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 64.94283792871555
- type: f1
value: 61.40057652844215
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (cy)
type: mteb/amazon_massive_intent
config: cy
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 55.480833893745796
- type: f1
value: 52.5298332072816
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (da)
type: mteb/amazon_massive_intent
config: da
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 72.52858103564223
- type: f1
value: 69.3770851919204
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (de)
type: mteb/amazon_massive_intent
config: de
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 74.09213180901143
- type: f1
value: 71.13518469365879
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (el)
type: mteb/amazon_massive_intent
config: el
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 68.31203765971756
- type: f1
value: 66.05906970865144
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (en)
type: mteb/amazon_massive_intent
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 80.57162071284465
- type: f1
value: 77.7866172598823
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (es)
type: mteb/amazon_massive_intent
config: es
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 75.09414929388029
- type: f1
value: 72.5712594833695
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (fa)
type: mteb/amazon_massive_intent
config: fa
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 72.20914593140553
- type: f1
value: 68.90619124909186
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (fi)
type: mteb/amazon_massive_intent
config: fi
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 68.74243443174176
- type: f1
value: 64.72743141749955
- task:
type: Classification
dataset:
name: MTEB MassiveIntentClassification (fr)
type: mteb/amazon_massive_intent
config: fr
split: test
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dataset:
name: MTEB MassiveScenarioClassification (ml)
type: mteb/amazon_massive_scenario
config: ml
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 54.02824478816408
- type: f1
value: 52.732738981386504
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (mn)
type: mteb/amazon_massive_scenario
config: mn
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 54.23671822461331
- type: f1
value: 52.688080372545286
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ms)
type: mteb/amazon_massive_scenario
config: ms
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 75.5312710154674
- type: f1
value: 74.59368478550698
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (my)
type: mteb/amazon_massive_scenario
config: my
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 52.192333557498316
- type: f1
value: 50.18302290152229
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (nb)
type: mteb/amazon_massive_scenario
config: nb
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 75.6960322797579
- type: f1
value: 75.25331182714856
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (nl)
type: mteb/amazon_massive_scenario
config: nl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 78.47679892400808
- type: f1
value: 78.24044732352424
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (pl)
type: mteb/amazon_massive_scenario
config: pl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 77.36718224613315
- type: f1
value: 77.2714452985389
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (pt)
type: mteb/amazon_massive_scenario
config: pt
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 77.96234028244788
- type: f1
value: 78.21282127011372
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ro)
type: mteb/amazon_massive_scenario
config: ro
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 73.19435104236717
- type: f1
value: 73.1963711292812
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ru)
type: mteb/amazon_massive_scenario
config: ru
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 80.52118359112306
- type: f1
value: 80.4179964390288
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (sl)
type: mteb/amazon_massive_scenario
config: sl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 73.65837256220577
- type: f1
value: 73.07156989634905
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (sq)
type: mteb/amazon_massive_scenario
config: sq
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 64.02824478816409
- type: f1
value: 62.972399027713664
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (sv)
type: mteb/amazon_massive_scenario
config: sv
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 78.87020847343645
- type: f1
value: 78.224240866849
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (sw)
type: mteb/amazon_massive_scenario
config: sw
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 64.6570275722932
- type: f1
value: 63.274871811412545
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ta)
type: mteb/amazon_massive_scenario
config: ta
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 57.760591795561524
- type: f1
value: 56.73711528075771
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (te)
type: mteb/amazon_massive_scenario
config: te
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 57.26967047747142
- type: f1
value: 55.74735330863165
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (th)
type: mteb/amazon_massive_scenario
config: th
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 72.46133154001345
- type: f1
value: 71.9644168952811
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (tl)
type: mteb/amazon_massive_scenario
config: tl
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 73.70880968392737
- type: f1
value: 73.61543141070884
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (tr)
type: mteb/amazon_massive_scenario
config: tr
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 75.0437121721587
- type: f1
value: 74.83359868879921
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (ur)
type: mteb/amazon_massive_scenario
config: ur
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 67.05110961667788
- type: f1
value: 66.25869819274315
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (vi)
type: mteb/amazon_massive_scenario
config: vi
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 75.52118359112306
- type: f1
value: 75.92098546052303
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (zh-CN)
type: mteb/amazon_massive_scenario
config: zh-CN
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 79.92938802958977
- type: f1
value: 79.79833572573796
- task:
type: Classification
dataset:
name: MTEB MassiveScenarioClassification (zh-TW)
type: mteb/amazon_massive_scenario
config: zh-TW
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 76.86617350369872
- type: f1
value: 77.42645654909516
- task:
type: Retrieval
dataset:
name: MTEB MedicalRetrieval
type: C-MTEB/MedicalRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 44.6
- type: map_at_10
value: 50.019000000000005
- type: map_at_100
value: 50.611
- type: map_at_1000
value: 50.67
- type: map_at_3
value: 48.699999999999996
- type: map_at_5
value: 49.455
- type: mrr_at_1
value: 44.800000000000004
- type: mrr_at_10
value: 50.119
- type: mrr_at_100
value: 50.711
- type: mrr_at_1000
value: 50.77
- type: mrr_at_3
value: 48.8
- type: mrr_at_5
value: 49.555
- type: ndcg_at_1
value: 44.6
- type: ndcg_at_10
value: 52.754
- type: ndcg_at_100
value: 55.935
- type: ndcg_at_1000
value: 57.607
- type: ndcg_at_3
value: 50.012
- type: ndcg_at_5
value: 51.393
- type: precision_at_1
value: 44.6
- type: precision_at_10
value: 6.140000000000001
- type: precision_at_100
value: 0.77
- type: precision_at_1000
value: 0.09
- type: precision_at_3
value: 17.933
- type: precision_at_5
value: 11.44
- type: recall_at_1
value: 44.6
- type: recall_at_10
value: 61.4
- type: recall_at_100
value: 77
- type: recall_at_1000
value: 90.4
- type: recall_at_3
value: 53.800000000000004
- type: recall_at_5
value: 57.199999999999996
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringP2P
type: mteb/medrxiv-clustering-p2p
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 38.192667527616315
- task:
type: Clustering
dataset:
name: MTEB MedrxivClusteringS2S
type: mteb/medrxiv-clustering-s2s
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 37.44738902946689
- task:
type: Reranking
dataset:
name: MTEB MindSmallReranking
type: mteb/mind_small
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 32.59661273103955
- type: mrr
value: 33.82024242497473
- task:
type: Classification
dataset:
name: MTEB MultilingualSentiment
type: C-MTEB/MultilingualSentiment-classification
config: default
split: validation
revision: None
metrics:
- type: accuracy
value: 73.31333333333335
- type: f1
value: 73.0873466527602
- task:
type: Retrieval
dataset:
name: MTEB NFCorpus
type: nfcorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.471
- type: map_at_10
value: 14.142
- type: map_at_100
value: 18.179000000000002
- type: map_at_1000
value: 19.772000000000002
- type: map_at_3
value: 9.716
- type: map_at_5
value: 11.763
- type: mrr_at_1
value: 51.393
- type: mrr_at_10
value: 58.814
- type: mrr_at_100
value: 59.330000000000005
- type: mrr_at_1000
value: 59.35
- type: mrr_at_3
value: 56.398
- type: mrr_at_5
value: 58.038999999999994
- type: ndcg_at_1
value: 49.69
- type: ndcg_at_10
value: 38.615
- type: ndcg_at_100
value: 35.268
- type: ndcg_at_1000
value: 43.745
- type: ndcg_at_3
value: 43.187
- type: ndcg_at_5
value: 41.528999999999996
- type: precision_at_1
value: 51.083999999999996
- type: precision_at_10
value: 29.474
- type: precision_at_100
value: 9.167
- type: precision_at_1000
value: 2.2089999999999996
- type: precision_at_3
value: 40.351
- type: precision_at_5
value: 36.285000000000004
- type: recall_at_1
value: 5.471
- type: recall_at_10
value: 19.242
- type: recall_at_100
value: 37.14
- type: recall_at_1000
value: 68.35900000000001
- type: recall_at_3
value: 10.896
- type: recall_at_5
value: 14.75
- task:
type: Retrieval
dataset:
name: MTEB NQ
type: nq
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 39.499
- type: map_at_10
value: 55.862
- type: map_at_100
value: 56.667
- type: map_at_1000
value: 56.684999999999995
- type: map_at_3
value: 51.534
- type: map_at_5
value: 54.2
- type: mrr_at_1
value: 44.351
- type: mrr_at_10
value: 58.567
- type: mrr_at_100
value: 59.099000000000004
- type: mrr_at_1000
value: 59.109
- type: mrr_at_3
value: 55.218999999999994
- type: mrr_at_5
value: 57.391999999999996
- type: ndcg_at_1
value: 44.322
- type: ndcg_at_10
value: 63.535
- type: ndcg_at_100
value: 66.654
- type: ndcg_at_1000
value: 66.991
- type: ndcg_at_3
value: 55.701
- type: ndcg_at_5
value: 60.06700000000001
- type: precision_at_1
value: 44.322
- type: precision_at_10
value: 10.026
- type: precision_at_100
value: 1.18
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 24.865000000000002
- type: precision_at_5
value: 17.48
- type: recall_at_1
value: 39.499
- type: recall_at_10
value: 84.053
- type: recall_at_100
value: 97.11
- type: recall_at_1000
value: 99.493
- type: recall_at_3
value: 64.091
- type: recall_at_5
value: 74.063
- task:
type: PairClassification
dataset:
name: MTEB Ocnli
type: C-MTEB/OCNLI
config: default
split: validation
revision: None
metrics:
- type: cos_sim_accuracy
value: 61.18029236599891
- type: cos_sim_ap
value: 64.18398769398412
- type: cos_sim_f1
value: 67.96347757046446
- type: cos_sim_precision
value: 54.4529262086514
- type: cos_sim_recall
value: 90.3907074973601
- type: dot_accuracy
value: 61.18029236599891
- type: dot_ap
value: 64.18393484706077
- type: dot_f1
value: 67.96347757046446
- type: dot_precision
value: 54.4529262086514
- type: dot_recall
value: 90.3907074973601
- type: euclidean_accuracy
value: 61.18029236599891
- type: euclidean_ap
value: 64.18395024821486
- type: euclidean_f1
value: 67.96347757046446
- type: euclidean_precision
value: 54.4529262086514
- type: euclidean_recall
value: 90.3907074973601
- type: manhattan_accuracy
value: 61.451001624255554
- type: manhattan_ap
value: 64.38232708763513
- type: manhattan_f1
value: 68.05860805860804
- type: manhattan_precision
value: 52.10319685922602
- type: manhattan_recall
value: 98.09926082365365
- type: max_accuracy
value: 61.451001624255554
- type: max_ap
value: 64.38232708763513
- type: max_f1
value: 68.05860805860804
- task:
type: Classification
dataset:
name: MTEB OnlineShopping
type: C-MTEB/OnlineShopping-classification
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 92.19000000000001
- type: ap
value: 89.73918431886767
- type: f1
value: 92.17175032574507
- task:
type: STS
dataset:
name: MTEB PAWSX
type: C-MTEB/PAWSX
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 15.079320253752224
- type: cos_sim_spearman
value: 16.813772504404263
- type: euclidean_pearson
value: 19.476541162041762
- type: euclidean_spearman
value: 16.813772498098782
- type: manhattan_pearson
value: 19.497429832915277
- type: manhattan_spearman
value: 16.869600674180607
- task:
type: STS
dataset:
name: MTEB QBQTC
type: C-MTEB/QBQTC
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 30.36139599797913
- type: cos_sim_spearman
value: 31.80296402851347
- type: euclidean_pearson
value: 30.10387888252793
- type: euclidean_spearman
value: 31.80297780103808
- type: manhattan_pearson
value: 30.86720382849436
- type: manhattan_spearman
value: 32.70491131366606
- task:
type: Retrieval
dataset:
name: MTEB QuoraRetrieval
type: quora
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 71.911
- type: map_at_10
value: 86.087
- type: map_at_100
value: 86.701
- type: map_at_1000
value: 86.715
- type: map_at_3
value: 83.231
- type: map_at_5
value: 85.051
- type: mrr_at_1
value: 82.75
- type: mrr_at_10
value: 88.759
- type: mrr_at_100
value: 88.844
- type: mrr_at_1000
value: 88.844
- type: mrr_at_3
value: 87.935
- type: mrr_at_5
value: 88.504
- type: ndcg_at_1
value: 82.75
- type: ndcg_at_10
value: 89.605
- type: ndcg_at_100
value: 90.664
- type: ndcg_at_1000
value: 90.733
- type: ndcg_at_3
value: 87.03
- type: ndcg_at_5
value: 88.473
- type: precision_at_1
value: 82.75
- type: precision_at_10
value: 13.575000000000001
- type: precision_at_100
value: 1.539
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 38.153
- type: precision_at_5
value: 25.008000000000003
- type: recall_at_1
value: 71.911
- type: recall_at_10
value: 96.261
- type: recall_at_100
value: 99.72800000000001
- type: recall_at_1000
value: 99.993
- type: recall_at_3
value: 88.762
- type: recall_at_5
value: 92.949
- task:
type: Clustering
dataset:
name: MTEB RedditClustering
type: mteb/reddit-clustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 57.711581165572376
- task:
type: Clustering
dataset:
name: MTEB RedditClusteringP2P
type: mteb/reddit-clustering-p2p
config: default
split: test
revision: 282350215ef01743dc01b456c7f5241fa8937f16
metrics:
- type: v_measure
value: 66.48938885750297
- task:
type: Retrieval
dataset:
name: MTEB SCIDOCS
type: scidocs
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 3.7379999999999995
- type: map_at_10
value: 9.261
- type: map_at_100
value: 11.001
- type: map_at_1000
value: 11.262
- type: map_at_3
value: 6.816
- type: map_at_5
value: 8
- type: mrr_at_1
value: 18.4
- type: mrr_at_10
value: 28.755999999999997
- type: mrr_at_100
value: 29.892000000000003
- type: mrr_at_1000
value: 29.961
- type: mrr_at_3
value: 25.467000000000002
- type: mrr_at_5
value: 27.332
- type: ndcg_at_1
value: 18.4
- type: ndcg_at_10
value: 16.296
- type: ndcg_at_100
value: 23.52
- type: ndcg_at_1000
value: 28.504
- type: ndcg_at_3
value: 15.485
- type: ndcg_at_5
value: 13.471
- type: precision_at_1
value: 18.4
- type: precision_at_10
value: 8.469999999999999
- type: precision_at_100
value: 1.8950000000000002
- type: precision_at_1000
value: 0.309
- type: precision_at_3
value: 14.6
- type: precision_at_5
value: 11.84
- type: recall_at_1
value: 3.7379999999999995
- type: recall_at_10
value: 17.185
- type: recall_at_100
value: 38.397
- type: recall_at_1000
value: 62.798
- type: recall_at_3
value: 8.896999999999998
- type: recall_at_5
value: 12.021999999999998
- task:
type: STS
dataset:
name: MTEB SICK-R
type: mteb/sickr-sts
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 86.43977757480083
- type: cos_sim_spearman
value: 82.64182475199533
- type: euclidean_pearson
value: 83.71756009999591
- type: euclidean_spearman
value: 82.64182331395057
- type: manhattan_pearson
value: 83.8028936913025
- type: manhattan_spearman
value: 82.71024597804252
- task:
type: STS
dataset:
name: MTEB STS12
type: mteb/sts12-sts
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 86.85653060698912
- type: cos_sim_spearman
value: 79.65598885228324
- type: euclidean_pearson
value: 83.1205137628455
- type: euclidean_spearman
value: 79.65629387709038
- type: manhattan_pearson
value: 83.71108853545837
- type: manhattan_spearman
value: 80.25617619716708
- task:
type: STS
dataset:
name: MTEB STS13
type: mteb/sts13-sts
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 88.22921688565664
- type: cos_sim_spearman
value: 88.42662103041957
- type: euclidean_pearson
value: 87.91679798473325
- type: euclidean_spearman
value: 88.42662103041957
- type: manhattan_pearson
value: 88.16927537961303
- type: manhattan_spearman
value: 88.81581680062541
- task:
type: STS
dataset:
name: MTEB STS14
type: mteb/sts14-sts
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 86.77261424554293
- type: cos_sim_spearman
value: 84.53930146434155
- type: euclidean_pearson
value: 85.67420491389697
- type: euclidean_spearman
value: 84.53929771783851
- type: manhattan_pearson
value: 85.74306784515618
- type: manhattan_spearman
value: 84.7399304675314
- task:
type: STS
dataset:
name: MTEB STS15
type: mteb/sts15-sts
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 89.86138395166455
- type: cos_sim_spearman
value: 90.42577823022054
- type: euclidean_pearson
value: 89.8787763797515
- type: euclidean_spearman
value: 90.42577823022054
- type: manhattan_pearson
value: 89.9592937492158
- type: manhattan_spearman
value: 90.63535505335524
- task:
type: STS
dataset:
name: MTEB STS16
type: mteb/sts16-sts
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 86.5176674585941
- type: cos_sim_spearman
value: 87.6842917085397
- type: euclidean_pearson
value: 86.70213081520711
- type: euclidean_spearman
value: 87.6842917085397
- type: manhattan_pearson
value: 86.83702628983627
- type: manhattan_spearman
value: 87.87791000374443
- task:
type: STS
dataset:
name: MTEB STS17 (ko-ko)
type: mteb/sts17-crosslingual-sts
config: ko-ko
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 83.86395454805867
- type: cos_sim_spearman
value: 83.69454595252267
- type: euclidean_pearson
value: 83.04743892608313
- type: euclidean_spearman
value: 83.69454026433006
- type: manhattan_pearson
value: 83.4032095553322
- type: manhattan_spearman
value: 84.11527379013802
- task:
type: STS
dataset:
name: MTEB STS17 (ar-ar)
type: mteb/sts17-crosslingual-sts
config: ar-ar
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 81.80249894729546
- type: cos_sim_spearman
value: 81.87004960533409
- type: euclidean_pearson
value: 80.0392760044179
- type: euclidean_spearman
value: 81.87004960533409
- type: manhattan_pearson
value: 80.38096542355912
- type: manhattan_spearman
value: 82.40774679630341
- task:
type: STS
dataset:
name: MTEB STS17 (en-ar)
type: mteb/sts17-crosslingual-sts
config: en-ar
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 77.6158201787172
- type: cos_sim_spearman
value: 77.934651044009
- type: euclidean_pearson
value: 77.7874683895269
- type: euclidean_spearman
value: 77.934651044009
- type: manhattan_pearson
value: 78.36151849193052
- type: manhattan_spearman
value: 78.52439586349938
- task:
type: STS
dataset:
name: MTEB STS17 (en-de)
type: mteb/sts17-crosslingual-sts
config: en-de
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 87.04363311392207
- type: cos_sim_spearman
value: 87.30483659369973
- type: euclidean_pearson
value: 87.62634489502616
- type: euclidean_spearman
value: 87.30483659369973
- type: manhattan_pearson
value: 88.02340837141445
- type: manhattan_spearman
value: 87.55012003294
- task:
type: STS
dataset:
name: MTEB STS17 (en-en)
type: mteb/sts17-crosslingual-sts
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 91.69172851958248
- type: cos_sim_spearman
value: 91.7546879482416
- type: euclidean_pearson
value: 91.84843039183963
- type: euclidean_spearman
value: 91.7546879482416
- type: manhattan_pearson
value: 91.72325753804357
- type: manhattan_spearman
value: 91.55330259513397
- task:
type: STS
dataset:
name: MTEB STS17 (en-tr)
type: mteb/sts17-crosslingual-sts
config: en-tr
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 73.95572901084864
- type: cos_sim_spearman
value: 72.56217821552626
- type: euclidean_pearson
value: 74.24242980323574
- type: euclidean_spearman
value: 72.56217821552626
- type: manhattan_pearson
value: 74.57473362519922
- type: manhattan_spearman
value: 72.76048826648497
- task:
type: STS
dataset:
name: MTEB STS17 (es-en)
type: mteb/sts17-crosslingual-sts
config: es-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 86.93329396008296
- type: cos_sim_spearman
value: 88.2406635486219
- type: euclidean_pearson
value: 87.49687343908533
- type: euclidean_spearman
value: 88.2406635486219
- type: manhattan_pearson
value: 88.14088309231084
- type: manhattan_spearman
value: 88.93314020908534
- task:
type: STS
dataset:
name: MTEB STS17 (es-es)
type: mteb/sts17-crosslingual-sts
config: es-es
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 88.70124451546057
- type: cos_sim_spearman
value: 87.45988160052252
- type: euclidean_pearson
value: 88.44395505247728
- type: euclidean_spearman
value: 87.45988160052252
- type: manhattan_pearson
value: 88.69269783495425
- type: manhattan_spearman
value: 87.65383425621
- task:
type: STS
dataset:
name: MTEB STS17 (fr-en)
type: mteb/sts17-crosslingual-sts
config: fr-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 87.64109149761346
- type: cos_sim_spearman
value: 88.06459637689733
- type: euclidean_pearson
value: 88.02313315797703
- type: euclidean_spearman
value: 88.06459637689733
- type: manhattan_pearson
value: 88.28328539133253
- type: manhattan_spearman
value: 88.06605708379142
- task:
type: STS
dataset:
name: MTEB STS17 (it-en)
type: mteb/sts17-crosslingual-sts
config: it-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 88.9040028177525
- type: cos_sim_spearman
value: 89.68152202933464
- type: euclidean_pearson
value: 89.23684469601253
- type: euclidean_spearman
value: 89.68152202933464
- type: manhattan_pearson
value: 89.59504307277454
- type: manhattan_spearman
value: 89.88060100313582
- task:
type: STS
dataset:
name: MTEB STS17 (nl-en)
type: mteb/sts17-crosslingual-sts
config: nl-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 87.69891585325125
- type: cos_sim_spearman
value: 88.25252785071736
- type: euclidean_pearson
value: 87.99932873748662
- type: euclidean_spearman
value: 88.25252785071736
- type: manhattan_pearson
value: 88.26959683009446
- type: manhattan_spearman
value: 88.32583227300715
- task:
type: STS
dataset:
name: MTEB STS22 (en)
type: mteb/sts22-crosslingual-sts
config: en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 67.53235909794135
- type: cos_sim_spearman
value: 66.97521740529574
- type: euclidean_pearson
value: 68.19502223613912
- type: euclidean_spearman
value: 66.97521740529574
- type: manhattan_pearson
value: 68.39070714774539
- type: manhattan_spearman
value: 67.1072812364868
- task:
type: STS
dataset:
name: MTEB STS22 (de)
type: mteb/sts22-crosslingual-sts
config: de
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 43.715742021204775
- type: cos_sim_spearman
value: 49.12255971271453
- type: euclidean_pearson
value: 40.76848562610837
- type: euclidean_spearman
value: 49.12255971271453
- type: manhattan_pearson
value: 40.92204625614112
- type: manhattan_spearman
value: 49.23333793661129
- task:
type: STS
dataset:
name: MTEB STS22 (es)
type: mteb/sts22-crosslingual-sts
config: es
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 63.35268345563588
- type: cos_sim_spearman
value: 66.99661626042061
- type: euclidean_pearson
value: 65.85589122857066
- type: euclidean_spearman
value: 66.99661626042061
- type: manhattan_pearson
value: 66.78454301512294
- type: manhattan_spearman
value: 67.17570330149233
- task:
type: STS
dataset:
name: MTEB STS22 (pl)
type: mteb/sts22-crosslingual-sts
config: pl
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 33.36599908204445
- type: cos_sim_spearman
value: 39.20768331939503
- type: euclidean_pearson
value: 22.16066769530468
- type: euclidean_spearman
value: 39.20768331939503
- type: manhattan_pearson
value: 22.386053195546022
- type: manhattan_spearman
value: 39.70172817465986
- task:
type: STS
dataset:
name: MTEB STS22 (tr)
type: mteb/sts22-crosslingual-sts
config: tr
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 63.06813956986753
- type: cos_sim_spearman
value: 68.72065117995668
- type: euclidean_pearson
value: 66.97373456344194
- type: euclidean_spearman
value: 68.72065117995668
- type: manhattan_pearson
value: 67.34907265771595
- type: manhattan_spearman
value: 68.73705769957843
- task:
type: STS
dataset:
name: MTEB STS22 (ar)
type: mteb/sts22-crosslingual-sts
config: ar
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 47.17664865207108
- type: cos_sim_spearman
value: 54.115568323148864
- type: euclidean_pearson
value: 48.56418162879182
- type: euclidean_spearman
value: 54.115568323148864
- type: manhattan_pearson
value: 48.85951643453165
- type: manhattan_spearman
value: 54.13599784169052
- task:
type: STS
dataset:
name: MTEB STS22 (ru)
type: mteb/sts22-crosslingual-sts
config: ru
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 55.87514136275987
- type: cos_sim_spearman
value: 60.82923573674973
- type: euclidean_pearson
value: 53.724183308215615
- type: euclidean_spearman
value: 60.82923573674973
- type: manhattan_pearson
value: 53.954305573102445
- type: manhattan_spearman
value: 60.957483900644526
- task:
type: STS
dataset:
name: MTEB STS22 (zh)
type: mteb/sts22-crosslingual-sts
config: zh
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 59.55001413648593
- type: cos_sim_spearman
value: 63.395777040381276
- type: euclidean_pearson
value: 59.869972550293305
- type: euclidean_spearman
value: 63.395777040381276
- type: manhattan_pearson
value: 61.16195496847885
- type: manhattan_spearman
value: 63.41968682525581
- task:
type: STS
dataset:
name: MTEB STS22 (fr)
type: mteb/sts22-crosslingual-sts
config: fr
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 79.13334972675852
- type: cos_sim_spearman
value: 79.86263136371802
- type: euclidean_pearson
value: 78.2433603592541
- type: euclidean_spearman
value: 79.86263136371802
- type: manhattan_pearson
value: 78.87337106318412
- type: manhattan_spearman
value: 80.31230584758441
- task:
type: STS
dataset:
name: MTEB STS22 (de-en)
type: mteb/sts22-crosslingual-sts
config: de-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 63.559700748242356
- type: cos_sim_spearman
value: 60.92342109509558
- type: euclidean_pearson
value: 66.07256437521119
- type: euclidean_spearman
value: 60.92342109509558
- type: manhattan_pearson
value: 67.72769744612663
- type: manhattan_spearman
value: 59.64714507774168
- task:
type: STS
dataset:
name: MTEB STS22 (es-en)
type: mteb/sts22-crosslingual-sts
config: es-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 73.93491616145891
- type: cos_sim_spearman
value: 75.84242594400156
- type: euclidean_pearson
value: 74.87279745626121
- type: euclidean_spearman
value: 75.84242594400156
- type: manhattan_pearson
value: 76.47764144677505
- type: manhattan_spearman
value: 77.08411157845183
- task:
type: STS
dataset:
name: MTEB STS22 (it)
type: mteb/sts22-crosslingual-sts
config: it
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 72.75624124540954
- type: cos_sim_spearman
value: 75.8667941654703
- type: euclidean_pearson
value: 73.74314588451925
- type: euclidean_spearman
value: 75.8667941654703
- type: manhattan_pearson
value: 73.99641425871518
- type: manhattan_spearman
value: 76.1982840205817
- task:
type: STS
dataset:
name: MTEB STS22 (pl-en)
type: mteb/sts22-crosslingual-sts
config: pl-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 75.20898141298767
- type: cos_sim_spearman
value: 73.18060375331436
- type: euclidean_pearson
value: 75.44489280944619
- type: euclidean_spearman
value: 73.18060375331436
- type: manhattan_pearson
value: 75.65451039552286
- type: manhattan_spearman
value: 72.97744006123156
- task:
type: STS
dataset:
name: MTEB STS22 (zh-en)
type: mteb/sts22-crosslingual-sts
config: zh-en
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 72.04278252247816
- type: cos_sim_spearman
value: 71.8846446821539
- type: euclidean_pearson
value: 73.16043307050612
- type: euclidean_spearman
value: 71.8846446821539
- type: manhattan_pearson
value: 74.76905116839777
- type: manhattan_spearman
value: 72.66237093518471
- task:
type: STS
dataset:
name: MTEB STS22 (es-it)
type: mteb/sts22-crosslingual-sts
config: es-it
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 71.71033173838558
- type: cos_sim_spearman
value: 75.043122881885
- type: euclidean_pearson
value: 72.77579680345087
- type: euclidean_spearman
value: 75.043122881885
- type: manhattan_pearson
value: 72.99901534854922
- type: manhattan_spearman
value: 75.15418335015957
- task:
type: STS
dataset:
name: MTEB STS22 (de-fr)
type: mteb/sts22-crosslingual-sts
config: de-fr
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 55.75733447190482
- type: cos_sim_spearman
value: 61.38968334176681
- type: euclidean_pearson
value: 55.479231520643744
- type: euclidean_spearman
value: 61.38968334176681
- type: manhattan_pearson
value: 56.05230571465244
- type: manhattan_spearman
value: 62.69383054007398
- task:
type: STS
dataset:
name: MTEB STS22 (de-pl)
type: mteb/sts22-crosslingual-sts
config: de-pl
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 41.72244325050302
- type: cos_sim_spearman
value: 54.47476909084119
- type: euclidean_pearson
value: 43.94629756436873
- type: euclidean_spearman
value: 54.47476909084119
- type: manhattan_pearson
value: 46.36533046394657
- type: manhattan_spearman
value: 54.87509243633636
- task:
type: STS
dataset:
name: MTEB STS22 (fr-pl)
type: mteb/sts22-crosslingual-sts
config: fr-pl
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 70.75183711835146
- type: cos_sim_spearman
value: 84.51542547285167
- type: euclidean_pearson
value: 71.84188960126669
- type: euclidean_spearman
value: 84.51542547285167
- type: manhattan_pearson
value: 73.94847166379994
- type: manhattan_spearman
value: 84.51542547285167
- task:
type: STS
dataset:
name: MTEB STSB
type: C-MTEB/STSB
config: default
split: test
revision: None
metrics:
- type: cos_sim_pearson
value: 81.78690149086131
- type: cos_sim_spearman
value: 81.81202616916873
- type: euclidean_pearson
value: 80.98792254251062
- type: euclidean_spearman
value: 81.81202616916873
- type: manhattan_pearson
value: 81.46953021346732
- type: manhattan_spearman
value: 82.34259562492315
- task:
type: STS
dataset:
name: MTEB STSBenchmark
type: mteb/stsbenchmark-sts
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 87.68273341294419
- type: cos_sim_spearman
value: 88.59927164210958
- type: euclidean_pearson
value: 88.10745681818025
- type: euclidean_spearman
value: 88.59927164210958
- type: manhattan_pearson
value: 88.25166703784649
- type: manhattan_spearman
value: 88.85343247873482
- task:
type: Reranking
dataset:
name: MTEB SciDocsRR
type: mteb/scidocs-reranking
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 86.3340463345719
- type: mrr
value: 96.5182611506141
- task:
type: Retrieval
dataset:
name: MTEB SciFact
type: scifact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 60.967000000000006
- type: map_at_10
value: 71.873
- type: map_at_100
value: 72.271
- type: map_at_1000
value: 72.292
- type: map_at_3
value: 69.006
- type: map_at_5
value: 70.856
- type: mrr_at_1
value: 63.666999999999994
- type: mrr_at_10
value: 72.929
- type: mrr_at_100
value: 73.26
- type: mrr_at_1000
value: 73.282
- type: mrr_at_3
value: 71.111
- type: mrr_at_5
value: 72.328
- type: ndcg_at_1
value: 63.666999999999994
- type: ndcg_at_10
value: 76.414
- type: ndcg_at_100
value: 78.152
- type: ndcg_at_1000
value: 78.604
- type: ndcg_at_3
value: 71.841
- type: ndcg_at_5
value: 74.435
- type: precision_at_1
value: 63.666999999999994
- type: precision_at_10
value: 10.067
- type: precision_at_100
value: 1.097
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 27.667
- type: precision_at_5
value: 18.467
- type: recall_at_1
value: 60.967000000000006
- type: recall_at_10
value: 88.922
- type: recall_at_100
value: 96.667
- type: recall_at_1000
value: 100
- type: recall_at_3
value: 77.228
- type: recall_at_5
value: 83.428
- task:
type: PairClassification
dataset:
name: MTEB SprintDuplicateQuestions
type: mteb/sprintduplicatequestions-pairclassification
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.82277227722773
- type: cos_sim_ap
value: 95.66279851444406
- type: cos_sim_f1
value: 90.9367088607595
- type: cos_sim_precision
value: 92.1025641025641
- type: cos_sim_recall
value: 89.8
- type: dot_accuracy
value: 99.82277227722773
- type: dot_ap
value: 95.66279851444406
- type: dot_f1
value: 90.9367088607595
- type: dot_precision
value: 92.1025641025641
- type: dot_recall
value: 89.8
- type: euclidean_accuracy
value: 99.82277227722773
- type: euclidean_ap
value: 95.66279851444406
- type: euclidean_f1
value: 90.9367088607595
- type: euclidean_precision
value: 92.1025641025641
- type: euclidean_recall
value: 89.8
- type: manhattan_accuracy
value: 99.82673267326733
- type: manhattan_ap
value: 95.86094873177069
- type: manhattan_f1
value: 91.26788357178096
- type: manhattan_precision
value: 90.06815968841285
- type: manhattan_recall
value: 92.5
- type: max_accuracy
value: 99.82673267326733
- type: max_ap
value: 95.86094873177069
- type: max_f1
value: 91.26788357178096
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClustering
type: mteb/stackexchange-clustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 73.09533925852372
- task:
type: Clustering
dataset:
name: MTEB StackExchangeClusteringP2P
type: mteb/stackexchange-clustering-p2p
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 45.90745648090035
- task:
type: Reranking
dataset:
name: MTEB StackOverflowDupQuestions
type: mteb/stackoverflowdupquestions-reranking
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 54.91147686504404
- type: mrr
value: 56.03900082760377
- task:
type: Summarization
dataset:
name: MTEB SummEval
type: mteb/summeval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 31.46908662038217
- type: cos_sim_spearman
value: 31.40325730367437
- type: dot_pearson
value: 31.469083969291894
- type: dot_spearman
value: 31.40325730367437
- task:
type: Reranking
dataset:
name: MTEB T2Reranking
type: C-MTEB/T2Reranking
config: default
split: dev
revision: None
metrics:
- type: map
value: 66.90300783402137
- type: mrr
value: 77.06451972574179
- task:
type: Retrieval
dataset:
name: MTEB T2Retrieval
type: C-MTEB/T2Retrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 25.82
- type: map_at_10
value: 72.32300000000001
- type: map_at_100
value: 76.198
- type: map_at_1000
value: 76.281
- type: map_at_3
value: 50.719
- type: map_at_5
value: 62.326
- type: mrr_at_1
value: 86.599
- type: mrr_at_10
value: 89.751
- type: mrr_at_100
value: 89.876
- type: mrr_at_1000
value: 89.88000000000001
- type: mrr_at_3
value: 89.151
- type: mrr_at_5
value: 89.519
- type: ndcg_at_1
value: 86.599
- type: ndcg_at_10
value: 80.676
- type: ndcg_at_100
value: 85.03
- type: ndcg_at_1000
value: 85.854
- type: ndcg_at_3
value: 82.057
- type: ndcg_at_5
value: 80.537
- type: precision_at_1
value: 86.599
- type: precision_at_10
value: 40.373
- type: precision_at_100
value: 4.95
- type: precision_at_1000
value: 0.514
- type: precision_at_3
value: 71.918
- type: precision_at_5
value: 60.246
- type: recall_at_1
value: 25.82
- type: recall_at_10
value: 79.905
- type: recall_at_100
value: 93.88499999999999
- type: recall_at_1000
value: 98.073
- type: recall_at_3
value: 52.623
- type: recall_at_5
value: 66.233
- task:
type: Classification
dataset:
name: MTEB TNews
type: C-MTEB/TNews-classification
config: default
split: validation
revision: None
metrics:
- type: accuracy
value: 47.050000000000004
- type: f1
value: 45.704071498353294
- task:
type: Retrieval
dataset:
name: MTEB TRECCOVID
type: trec-covid
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 0.243
- type: map_at_10
value: 2.278
- type: map_at_100
value: 14.221
- type: map_at_1000
value: 33.474
- type: map_at_3
value: 0.7270000000000001
- type: map_at_5
value: 1.183
- type: mrr_at_1
value: 94
- type: mrr_at_10
value: 97
- type: mrr_at_100
value: 97
- type: mrr_at_1000
value: 97
- type: mrr_at_3
value: 97
- type: mrr_at_5
value: 97
- type: ndcg_at_1
value: 90
- type: ndcg_at_10
value: 87.249
- type: ndcg_at_100
value: 67.876
- type: ndcg_at_1000
value: 59.205
- type: ndcg_at_3
value: 90.12299999999999
- type: ndcg_at_5
value: 89.126
- type: precision_at_1
value: 94
- type: precision_at_10
value: 90.8
- type: precision_at_100
value: 69.28
- type: precision_at_1000
value: 25.85
- type: precision_at_3
value: 94.667
- type: precision_at_5
value: 92.80000000000001
- type: recall_at_1
value: 0.243
- type: recall_at_10
value: 2.392
- type: recall_at_100
value: 16.982
- type: recall_at_1000
value: 55.214
- type: recall_at_3
value: 0.745
- type: recall_at_5
value: 1.2229999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (sqi-eng)
type: mteb/tatoeba-bitext-mining
config: sqi-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 70.5
- type: f1
value: 67.05501804646966
- type: precision
value: 65.73261904761904
- type: recall
value: 70.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (fry-eng)
type: mteb/tatoeba-bitext-mining
config: fry-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 75.14450867052022
- type: f1
value: 70.98265895953759
- type: precision
value: 69.26782273603082
- type: recall
value: 75.14450867052022
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kur-eng)
type: mteb/tatoeba-bitext-mining
config: kur-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 33.170731707317074
- type: f1
value: 29.92876500193573
- type: precision
value: 28.669145894755648
- type: recall
value: 33.170731707317074
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tur-eng)
type: mteb/tatoeba-bitext-mining
config: tur-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.5
- type: f1
value: 94.13333333333333
- type: precision
value: 93.46666666666667
- type: recall
value: 95.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (deu-eng)
type: mteb/tatoeba-bitext-mining
config: deu-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 99.6
- type: f1
value: 99.46666666666665
- type: precision
value: 99.4
- type: recall
value: 99.6
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (nld-eng)
type: mteb/tatoeba-bitext-mining
config: nld-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.2
- type: f1
value: 96.39999999999999
- type: precision
value: 96
- type: recall
value: 97.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ron-eng)
type: mteb/tatoeba-bitext-mining
config: ron-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.5
- type: f1
value: 92.99666666666667
- type: precision
value: 92.31666666666666
- type: recall
value: 94.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ang-eng)
type: mteb/tatoeba-bitext-mining
config: ang-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 85.82089552238806
- type: f1
value: 81.59203980099502
- type: precision
value: 79.60199004975124
- type: recall
value: 85.82089552238806
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ido-eng)
type: mteb/tatoeba-bitext-mining
config: ido-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 79.5
- type: f1
value: 75.11246031746032
- type: precision
value: 73.38734126984127
- type: recall
value: 79.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (jav-eng)
type: mteb/tatoeba-bitext-mining
config: jav-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 44.390243902439025
- type: f1
value: 38.48896631823461
- type: precision
value: 36.57220286488579
- type: recall
value: 44.390243902439025
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (isl-eng)
type: mteb/tatoeba-bitext-mining
config: isl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.2
- type: f1
value: 87.57333333333334
- type: precision
value: 86.34166666666665
- type: recall
value: 90.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (slv-eng)
type: mteb/tatoeba-bitext-mining
config: slv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 88.82138517618469
- type: f1
value: 85.98651854423423
- type: precision
value: 84.79257073424753
- type: recall
value: 88.82138517618469
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (cym-eng)
type: mteb/tatoeba-bitext-mining
config: cym-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 77.04347826086956
- type: f1
value: 72.32108147606868
- type: precision
value: 70.37207357859532
- type: recall
value: 77.04347826086956
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kaz-eng)
type: mteb/tatoeba-bitext-mining
config: kaz-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 53.04347826086957
- type: f1
value: 46.88868184955141
- type: precision
value: 44.71730105643149
- type: recall
value: 53.04347826086957
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (est-eng)
type: mteb/tatoeba-bitext-mining
config: est-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 68
- type: f1
value: 62.891813186813195
- type: precision
value: 61.037906162464985
- type: recall
value: 68
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (heb-eng)
type: mteb/tatoeba-bitext-mining
config: heb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 86.3
- type: f1
value: 82.82000000000001
- type: precision
value: 81.25690476190475
- type: recall
value: 86.3
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (gla-eng)
type: mteb/tatoeba-bitext-mining
config: gla-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 68.87816646562122
- type: f1
value: 63.53054933272062
- type: precision
value: 61.47807816331196
- type: recall
value: 68.87816646562122
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (mar-eng)
type: mteb/tatoeba-bitext-mining
config: mar-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 74.4
- type: f1
value: 68.99388888888889
- type: precision
value: 66.81035714285713
- type: recall
value: 74.4
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (lat-eng)
type: mteb/tatoeba-bitext-mining
config: lat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.5
- type: f1
value: 87.93666666666667
- type: precision
value: 86.825
- type: recall
value: 90.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (bel-eng)
type: mteb/tatoeba-bitext-mining
config: bel-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.7
- type: f1
value: 88.09
- type: precision
value: 86.85833333333333
- type: recall
value: 90.7
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (pms-eng)
type: mteb/tatoeba-bitext-mining
config: pms-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 67.61904761904762
- type: f1
value: 62.30239247214037
- type: precision
value: 60.340702947845806
- type: recall
value: 67.61904761904762
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (gle-eng)
type: mteb/tatoeba-bitext-mining
config: gle-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 77.9
- type: f1
value: 73.81285714285714
- type: precision
value: 72.21570818070818
- type: recall
value: 77.9
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (pes-eng)
type: mteb/tatoeba-bitext-mining
config: pes-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 91.8
- type: f1
value: 89.66666666666667
- type: precision
value: 88.66666666666666
- type: recall
value: 91.8
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (nob-eng)
type: mteb/tatoeba-bitext-mining
config: nob-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 97.6
- type: f1
value: 96.85666666666665
- type: precision
value: 96.50833333333333
- type: recall
value: 97.6
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (bul-eng)
type: mteb/tatoeba-bitext-mining
config: bul-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.39999999999999
- type: f1
value: 93.98333333333333
- type: precision
value: 93.30000000000001
- type: recall
value: 95.39999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (cbk-eng)
type: mteb/tatoeba-bitext-mining
config: cbk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 85
- type: f1
value: 81.31538461538462
- type: precision
value: 79.70666666666666
- type: recall
value: 85
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (hun-eng)
type: mteb/tatoeba-bitext-mining
config: hun-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 91.60000000000001
- type: f1
value: 89.81888888888888
- type: precision
value: 89.08583333333333
- type: recall
value: 91.60000000000001
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (uig-eng)
type: mteb/tatoeba-bitext-mining
config: uig-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 44.3
- type: f1
value: 38.8623088023088
- type: precision
value: 37.03755623461505
- type: recall
value: 44.3
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (rus-eng)
type: mteb/tatoeba-bitext-mining
config: rus-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.19999999999999
- type: f1
value: 93.75
- type: precision
value: 93.05
- type: recall
value: 95.19999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (spa-eng)
type: mteb/tatoeba-bitext-mining
config: spa-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 99.1
- type: f1
value: 98.8
- type: precision
value: 98.65
- type: recall
value: 99.1
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (hye-eng)
type: mteb/tatoeba-bitext-mining
config: hye-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 69.6765498652291
- type: f1
value: 63.991785393402644
- type: precision
value: 61.7343729944808
- type: recall
value: 69.6765498652291
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tel-eng)
type: mteb/tatoeba-bitext-mining
config: tel-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 50
- type: f1
value: 42.79341029341029
- type: precision
value: 40.25098358431692
- type: recall
value: 50
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (afr-eng)
type: mteb/tatoeba-bitext-mining
config: afr-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 89.7
- type: f1
value: 87.19023809523809
- type: precision
value: 86.12595238095237
- type: recall
value: 89.7
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (mon-eng)
type: mteb/tatoeba-bitext-mining
config: mon-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 42.72727272727273
- type: f1
value: 37.78789518562245
- type: precision
value: 36.24208471267295
- type: recall
value: 42.72727272727273
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (arz-eng)
type: mteb/tatoeba-bitext-mining
config: arz-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 75.26205450733752
- type: f1
value: 70.72842833849123
- type: precision
value: 68.93256464011182
- type: recall
value: 75.26205450733752
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (hrv-eng)
type: mteb/tatoeba-bitext-mining
config: hrv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
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type: BitextMining
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
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split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 95.5
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dataset:
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config: uzb-eng
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 58.41121495327103
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 73.3
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
config: ina-eng
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
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value: 96.5
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
config: lfn-eng
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 78.4
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
config: zsm-eng
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 95.3
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 93.7
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type: BitextMining
dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
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metrics:
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 92.4
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type: BitextMining
dataset:
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metrics:
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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metrics:
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
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metrics:
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type: BitextMining
dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 97.3
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
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split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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config: srp-eng
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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dataset:
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config: epo-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
config: kzj-eng
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
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config: awa-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 73.16017316017316
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
config: fao-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 75.19083969465649
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 62.154294032023294
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
config: ile-eng
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 86.8
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
config: bos-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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type: BitextMining
dataset:
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type: mteb/tatoeba-bitext-mining
config: cor-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 11.4
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type: BitextMining
dataset:
name: MTEB Tatoeba (cat-eng)
type: mteb/tatoeba-bitext-mining
config: cat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 93.4
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type: BitextMining
dataset:
name: MTEB Tatoeba (eus-eng)
type: mteb/tatoeba-bitext-mining
config: eus-eng
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revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 46
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value: 46
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type: BitextMining
dataset:
name: MTEB Tatoeba (yue-eng)
type: mteb/tatoeba-bitext-mining
config: yue-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 91
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value: 91
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type: BitextMining
dataset:
name: MTEB Tatoeba (swe-eng)
type: mteb/tatoeba-bitext-mining
config: swe-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 94.39999999999999
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value: 94.39999999999999
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type: BitextMining
dataset:
name: MTEB Tatoeba (dtp-eng)
type: mteb/tatoeba-bitext-mining
config: dtp-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 14.2
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value: 11.73701698770113
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value: 14.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kat-eng)
type: mteb/tatoeba-bitext-mining
config: kat-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 57.560853653346946
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value: 65.14745308310992
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type: BitextMining
dataset:
name: MTEB Tatoeba (jpn-eng)
type: mteb/tatoeba-bitext-mining
config: jpn-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
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value: 95.39999999999999
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value: 95.39999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (csb-eng)
type: mteb/tatoeba-bitext-mining
config: csb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 69.56521739130434
- type: f1
value: 62.92490118577074
- type: precision
value: 60.27009222661397
- type: recall
value: 69.56521739130434
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (xho-eng)
type: mteb/tatoeba-bitext-mining
config: xho-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 40.140845070422536
- type: f1
value: 35.96411804158283
- type: precision
value: 34.89075869357559
- type: recall
value: 40.140845070422536
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (orv-eng)
type: mteb/tatoeba-bitext-mining
config: orv-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 65.86826347305389
- type: f1
value: 59.646248628284546
- type: precision
value: 57.22982606216139
- type: recall
value: 65.86826347305389
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ind-eng)
type: mteb/tatoeba-bitext-mining
config: ind-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.89999999999999
- type: f1
value: 93.48333333333333
- type: precision
value: 92.83666666666667
- type: recall
value: 94.89999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tuk-eng)
type: mteb/tatoeba-bitext-mining
config: tuk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 47.783251231527096
- type: f1
value: 42.006447302013804
- type: precision
value: 40.12747105111637
- type: recall
value: 47.783251231527096
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (max-eng)
type: mteb/tatoeba-bitext-mining
config: max-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 69.71830985915493
- type: f1
value: 64.80266212660578
- type: precision
value: 63.08098591549296
- type: recall
value: 69.71830985915493
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (swh-eng)
type: mteb/tatoeba-bitext-mining
config: swh-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 67.94871794871796
- type: f1
value: 61.59912309912309
- type: precision
value: 59.17338217338218
- type: recall
value: 67.94871794871796
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (hin-eng)
type: mteb/tatoeba-bitext-mining
config: hin-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.39999999999999
- type: f1
value: 95.28333333333335
- type: precision
value: 94.75
- type: recall
value: 96.39999999999999
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (dsb-eng)
type: mteb/tatoeba-bitext-mining
config: dsb-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 70.14613778705638
- type: f1
value: 65.4349338900487
- type: precision
value: 63.57599255302805
- type: recall
value: 70.14613778705638
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ber-eng)
type: mteb/tatoeba-bitext-mining
config: ber-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 9.2
- type: f1
value: 7.622184434339607
- type: precision
value: 7.287048159682417
- type: recall
value: 9.2
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tam-eng)
type: mteb/tatoeba-bitext-mining
config: tam-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 77.85016286644951
- type: f1
value: 72.83387622149837
- type: precision
value: 70.58450959102424
- type: recall
value: 77.85016286644951
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (slk-eng)
type: mteb/tatoeba-bitext-mining
config: slk-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 90.8
- type: f1
value: 88.84333333333333
- type: precision
value: 87.96666666666665
- type: recall
value: 90.8
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tgl-eng)
type: mteb/tatoeba-bitext-mining
config: tgl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 94.6
- type: f1
value: 93.14
- type: precision
value: 92.49833333333333
- type: recall
value: 94.6
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ast-eng)
type: mteb/tatoeba-bitext-mining
config: ast-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 84.25196850393701
- type: f1
value: 80.94488188976378
- type: precision
value: 79.65879265091863
- type: recall
value: 84.25196850393701
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (mkd-eng)
type: mteb/tatoeba-bitext-mining
config: mkd-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 89.5
- type: f1
value: 86.89666666666666
- type: precision
value: 85.7
- type: recall
value: 89.5
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (khm-eng)
type: mteb/tatoeba-bitext-mining
config: khm-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 42.797783933518005
- type: f1
value: 37.30617360155193
- type: precision
value: 35.34933825792552
- type: recall
value: 42.797783933518005
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ces-eng)
type: mteb/tatoeba-bitext-mining
config: ces-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 96.1
- type: f1
value: 94.93333333333332
- type: precision
value: 94.38333333333333
- type: recall
value: 96.1
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tzl-eng)
type: mteb/tatoeba-bitext-mining
config: tzl-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 54.807692307692314
- type: f1
value: 49.506903353057204
- type: precision
value: 47.54807692307693
- type: recall
value: 54.807692307692314
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (urd-eng)
type: mteb/tatoeba-bitext-mining
config: urd-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 87.1
- type: f1
value: 83.61857142857143
- type: precision
value: 81.975
- type: recall
value: 87.1
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (ara-eng)
type: mteb/tatoeba-bitext-mining
config: ara-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 91.10000000000001
- type: f1
value: 88.76333333333332
- type: precision
value: 87.67
- type: recall
value: 91.10000000000001
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (kor-eng)
type: mteb/tatoeba-bitext-mining
config: kor-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 93.10000000000001
- type: f1
value: 91.28999999999999
- type: precision
value: 90.44500000000001
- type: recall
value: 93.10000000000001
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (yid-eng)
type: mteb/tatoeba-bitext-mining
config: yid-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 39.97641509433962
- type: f1
value: 33.12271889998028
- type: precision
value: 30.95185381542554
- type: recall
value: 39.97641509433962
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (fin-eng)
type: mteb/tatoeba-bitext-mining
config: fin-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 92.60000000000001
- type: f1
value: 90.69
- type: precision
value: 89.84500000000001
- type: recall
value: 92.60000000000001
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (tha-eng)
type: mteb/tatoeba-bitext-mining
config: tha-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 95.07299270072993
- type: f1
value: 93.64355231143554
- type: precision
value: 92.94403892944038
- type: recall
value: 95.07299270072993
- task:
type: BitextMining
dataset:
name: MTEB Tatoeba (wuu-eng)
type: mteb/tatoeba-bitext-mining
config: wuu-eng
split: test
revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
metrics:
- type: accuracy
value: 91.9
- type: f1
value: 89.61333333333333
- type: precision
value: 88.53333333333333
- type: recall
value: 91.9
- task:
type: Clustering
dataset:
name: MTEB ThuNewsClusteringP2P
type: C-MTEB/ThuNewsClusteringP2P
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 64.68478289806511
- task:
type: Clustering
dataset:
name: MTEB ThuNewsClusteringS2S
type: C-MTEB/ThuNewsClusteringS2S
config: default
split: test
revision: None
metrics:
- type: v_measure
value: 57.53010296184097
- task:
type: Retrieval
dataset:
name: MTEB Touche2020
type: webis-touche2020
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 2.519
- type: map_at_10
value: 10.31
- type: map_at_100
value: 16.027
- type: map_at_1000
value: 17.827
- type: map_at_3
value: 5.721
- type: map_at_5
value: 7.7829999999999995
- type: mrr_at_1
value: 34.694
- type: mrr_at_10
value: 52.642999999999994
- type: mrr_at_100
value: 53.366
- type: mrr_at_1000
value: 53.366
- type: mrr_at_3
value: 48.638999999999996
- type: mrr_at_5
value: 50.578
- type: ndcg_at_1
value: 31.633
- type: ndcg_at_10
value: 26.394000000000002
- type: ndcg_at_100
value: 36.41
- type: ndcg_at_1000
value: 49.206
- type: ndcg_at_3
value: 31.694
- type: ndcg_at_5
value: 29.529
- type: precision_at_1
value: 34.694
- type: precision_at_10
value: 23.469
- type: precision_at_100
value: 7.286
- type: precision_at_1000
value: 1.5610000000000002
- type: precision_at_3
value: 34.014
- type: precision_at_5
value: 29.796
- type: recall_at_1
value: 2.519
- type: recall_at_10
value: 17.091
- type: recall_at_100
value: 45.429
- type: recall_at_1000
value: 84.621
- type: recall_at_3
value: 7.208
- type: recall_at_5
value: 10.523
- task:
type: Classification
dataset:
name: MTEB ToxicConversationsClassification
type: mteb/toxic_conversations_50k
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 69.58659999999999
- type: ap
value: 14.735696532619
- type: f1
value: 54.23517220069903
- task:
type: Classification
dataset:
name: MTEB TweetSentimentExtractionClassification
type: mteb/tweet_sentiment_extraction
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 63.723825693265425
- type: f1
value: 64.02405729449103
- task:
type: Clustering
dataset:
name: MTEB TwentyNewsgroupsClustering
type: mteb/twentynewsgroups-clustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 54.310161547491006
- task:
type: PairClassification
dataset:
name: MTEB TwitterSemEval2015
type: mteb/twittersemeval2015-pairclassification
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 88.77630088812064
- type: cos_sim_ap
value: 81.61725457333809
- type: cos_sim_f1
value: 74.91373801916932
- type: cos_sim_precision
value: 72.63940520446097
- type: cos_sim_recall
value: 77.33509234828496
- type: dot_accuracy
value: 88.77630088812064
- type: dot_ap
value: 81.61725317476251
- type: dot_f1
value: 74.91373801916932
- type: dot_precision
value: 72.63940520446097
- type: dot_recall
value: 77.33509234828496
- type: euclidean_accuracy
value: 88.77630088812064
- type: euclidean_ap
value: 81.61724596869566
- type: euclidean_f1
value: 74.91373801916932
- type: euclidean_precision
value: 72.63940520446097
- type: euclidean_recall
value: 77.33509234828496
- type: manhattan_accuracy
value: 88.67497168742922
- type: manhattan_ap
value: 81.430251048948
- type: manhattan_f1
value: 74.79593118171543
- type: manhattan_precision
value: 71.3635274382938
- type: manhattan_recall
value: 78.57519788918206
- type: max_accuracy
value: 88.77630088812064
- type: max_ap
value: 81.61725457333809
- type: max_f1
value: 74.91373801916932
- task:
type: PairClassification
dataset:
name: MTEB TwitterURLCorpus
type: mteb/twitterurlcorpus-pairclassification
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.85136026700819
- type: cos_sim_ap
value: 87.74656687446567
- type: cos_sim_f1
value: 80.3221673073403
- type: cos_sim_precision
value: 76.56871640957633
- type: cos_sim_recall
value: 84.46258084385587
- type: dot_accuracy
value: 89.85136026700819
- type: dot_ap
value: 87.74656471395072
- type: dot_f1
value: 80.3221673073403
- type: dot_precision
value: 76.56871640957633
- type: dot_recall
value: 84.46258084385587
- type: euclidean_accuracy
value: 89.85136026700819
- type: euclidean_ap
value: 87.74656885754466
- type: euclidean_f1
value: 80.3221673073403
- type: euclidean_precision
value: 76.56871640957633
- type: euclidean_recall
value: 84.46258084385587
- type: manhattan_accuracy
value: 89.86300306593705
- type: manhattan_ap
value: 87.78807479093082
- type: manhattan_f1
value: 80.31663429471911
- type: manhattan_precision
value: 76.63472970137772
- type: manhattan_recall
value: 84.3701878657222
- type: max_accuracy
value: 89.86300306593705
- type: max_ap
value: 87.78807479093082
- type: max_f1
value: 80.3221673073403
- task:
type: Retrieval
dataset:
name: MTEB VideoRetrieval
type: C-MTEB/VideoRetrieval
config: default
split: dev
revision: None
metrics:
- type: map_at_1
value: 32.4
- type: map_at_10
value: 40.961999999999996
- type: map_at_100
value: 41.660000000000004
- type: map_at_1000
value: 41.721000000000004
- type: map_at_3
value: 38.550000000000004
- type: map_at_5
value: 40.06
- type: mrr_at_1
value: 32.4
- type: mrr_at_10
value: 40.961999999999996
- type: mrr_at_100
value: 41.660000000000004
- type: mrr_at_1000
value: 41.721000000000004
- type: mrr_at_3
value: 38.550000000000004
- type: mrr_at_5
value: 40.06
- type: ndcg_at_1
value: 32.4
- type: ndcg_at_10
value: 45.388
- type: ndcg_at_100
value: 49.012
- type: ndcg_at_1000
value: 50.659
- type: ndcg_at_3
value: 40.47
- type: ndcg_at_5
value: 43.232
- type: precision_at_1
value: 32.4
- type: precision_at_10
value: 5.94
- type: precision_at_100
value: 0.769
- type: precision_at_1000
value: 0.09
- type: precision_at_3
value: 15.333
- type: precision_at_5
value: 10.56
- type: recall_at_1
value: 32.4
- type: recall_at_10
value: 59.4
- type: recall_at_100
value: 76.9
- type: recall_at_1000
value: 90
- type: recall_at_3
value: 46
- type: recall_at_5
value: 52.800000000000004
- task:
type: Classification
dataset:
name: MTEB Waimai
type: C-MTEB/waimai-classification
config: default
split: test
revision: None
metrics:
- type: accuracy
value: 86.94000000000001
- type: ap
value: 70.57373468481975
- type: f1
value: 85.26264784928323
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 29.61
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=intfloat/e5-mistral-7b-instruct
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 27.05
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=intfloat/e5-mistral-7b-instruct
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 23.12
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=intfloat/e5-mistral-7b-instruct
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 47.53
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=intfloat/e5-mistral-7b-instruct
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 51.93
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=intfloat/e5-mistral-7b-instruct
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 0
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=intfloat/e5-mistral-7b-instruct
name: Open LLM Leaderboard
E5-mistral-7b-instruct
Improving Text Embeddings with Large Language Models. Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei, arXiv 2024
This model has 32 layers and the embedding size is 4096.
Usage
Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset.
Sentence Transformers
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("intfloat/e5-mistral-7b-instruct")
# In case you want to reduce the maximum sequence length:
model.max_seq_length = 4096
queries = [
"how much protein should a female eat",
"summit define",
]
documents = [
"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."
]
query_embeddings = model.encode(queries, prompt_name="web_search_query")
document_embeddings = model.encode(documents)
scores = (query_embeddings @ document_embeddings.T) * 100
print(scores.tolist())
Have a look at config_sentence_transformers.json for the prompts that are pre-configured, such as web_search_query, sts_query, and summarization_query. Additionally, check out unilm/e5/utils.py for prompts we used for evaluation. You can use these via e.g. model.encode(queries, prompt="Instruct: Given a claim, find documents that refute the claim\nQuery: ").
Transformers
import torch
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
def last_token_pool(last_hidden_states: Tensor,
attention_mask: Tensor) -> Tensor:
left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
if left_padding:
return last_hidden_states[:, -1]
else:
sequence_lengths = attention_mask.sum(dim=1) - 1
batch_size = last_hidden_states.shape[0]
return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]
def get_detailed_instruct(task_description: str, query: str) -> str:
return f'Instruct: {task_description}\nQuery: {query}'
# Each query must come with a one-sentence instruction that describes the task
task = 'Given a web search query, retrieve relevant passages that answer the query'
queries = [
get_detailed_instruct(task, 'how much protein should a female eat'),
get_detailed_instruct(task, 'summit define')
]
# No need to add instruction for retrieval documents
documents = [
"As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
"Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments."
]
input_texts = queries + documents
tokenizer = AutoTokenizer.from_pretrained('intfloat/e5-mistral-7b-instruct')
model = AutoModel.from_pretrained('intfloat/e5-mistral-7b-instruct')
max_length = 4096
# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=max_length, padding=True, truncation=True, return_tensors='pt')
outputs = model(**batch_dict)
embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())
Supported Languages
This model is initialized from Mistral-7B-v0.1 and fine-tuned on a mixture of multilingual datasets. As a result, it has some multilingual capability. However, since Mistral-7B-v0.1 is mainly trained on English data, we recommend using this model for English only. For multilingual use cases, please refer to multilingual-e5-large.
MTEB Benchmark Evaluation
Check out unilm/e5 to reproduce evaluation results on the BEIR and MTEB benchmark.
FAQ
1. Do I need to add instructions to the query?
Yes, this is how the model is trained, otherwise you will see a performance degradation. The task definition should be a one-sentence instruction that describes the task. This is a way to customize text embeddings for different scenarios through natural language instructions.
Please check out unilm/e5/utils.py for instructions we used for evaluation.
On the other hand, there is no need to add instructions to the document side.
2. Why are my reproduced results slightly different from reported in the model card?
Different versions of transformers and pytorch could cause negligible but non-zero performance differences.
3. Where are the LoRA-only weights?
You can find the LoRA-only weights at https://huggingface.co/intfloat/e5-mistral-7b-instruct/tree/main/lora.
Citation
If you find our paper or models helpful, please consider cite as follows:
@article{wang2023improving,
title={Improving Text Embeddings with Large Language Models},
author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Yang, Linjun and Majumder, Rangan and Wei, Furu},
journal={arXiv preprint arXiv:2401.00368},
year={2023}
}
@article{wang2022text,
title={Text Embeddings by Weakly-Supervised Contrastive Pre-training},
author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Jiao, Binxing and Yang, Linjun and Jiang, Daxin and Majumder, Rangan and Wei, Furu},
journal={arXiv preprint arXiv:2212.03533},
year={2022}
}
Limitations
Using this model for inputs longer than 4096 tokens is not recommended.
This model's multilingual capability is still inferior to multilingual-e5-large for some cases.
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 29.87 |
| AI2 Reasoning Challenge (25-Shot) | 29.61 |
| HellaSwag (10-Shot) | 27.05 |
| MMLU (5-Shot) | 23.12 |
| TruthfulQA (0-shot) | 47.53 |
| Winogrande (5-shot) | 51.93 |
| GSM8k (5-shot) | 0.00 |