metadata
tags:
- mteb
model-index:
- name: bge-m3
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 75.6268656716418
- type: ap
value: 39.50276109614102
- type: f1
value: 70.00224623431103
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 91.013675
- type: ap
value: 87.30227544778319
- type: f1
value: 91.00157923673694
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 46.986000000000004
- type: f1
value: 44.93316837240337
- task:
type: Retrieval
dataset:
type: arguana
name: MTEB ArguAna
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 28.521
- type: map_at_10
value: 45.062999999999995
- type: map_at_100
value: 45.965
- type: map_at_1000
value: 45.972
- type: map_at_3
value: 40.078
- type: map_at_5
value: 43.158
- type: mrr_at_1
value: 29.232000000000003
- type: mrr_at_10
value: 45.305
- type: mrr_at_100
value: 46.213
- type: mrr_at_1000
value: 46.22
- type: mrr_at_3
value: 40.339000000000006
- type: mrr_at_5
value: 43.394
- type: ndcg_at_1
value: 28.521
- type: ndcg_at_10
value: 53.959999999999994
- type: ndcg_at_100
value: 57.691
- type: ndcg_at_1000
value: 57.858
- type: ndcg_at_3
value: 43.867
- type: ndcg_at_5
value: 49.38
- type: precision_at_1
value: 28.521
- type: precision_at_10
value: 8.222
- type: precision_at_100
value: 0.9820000000000001
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 18.279
- type: precision_at_5
value: 13.627
- type: recall_at_1
value: 28.521
- type: recall_at_10
value: 82.219
- type: recall_at_100
value: 98.222
- type: recall_at_1000
value: 99.502
- type: recall_at_3
value: 54.836
- type: recall_at_5
value: 68.137
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 39.409674498704625
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 61.52757354203137
- type: mrr
value: 74.28241656773513
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 84.39442490594014
- type: cos_sim_spearman
value: 83.37599616417513
- type: euclidean_pearson
value: 83.23317790460271
- type: euclidean_spearman
value: 83.37599616417513
- type: manhattan_pearson
value: 83.23182214744224
- type: manhattan_spearman
value: 83.5428674363298
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 81.93181818181819
- type: f1
value: 81.0852312152688
- task:
type: Retrieval
dataset:
type: BeIR/cqadupstack
name: MTEB CQADupstackEnglishRetrieval
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 28.784
- type: map_at_10
value: 38.879000000000005
- type: map_at_100
value: 40.161
- type: map_at_1000
value: 40.291
- type: map_at_3
value: 36.104
- type: map_at_5
value: 37.671
- type: mrr_at_1
value: 35.924
- type: mrr_at_10
value: 44.471
- type: mrr_at_100
value: 45.251000000000005
- type: mrr_at_1000
value: 45.296
- type: mrr_at_3
value: 42.367
- type: mrr_at_5
value: 43.635000000000005
- type: ndcg_at_1
value: 35.924
- type: ndcg_at_10
value: 44.369
- type: ndcg_at_100
value: 48.925999999999995
- type: ndcg_at_1000
value: 50.964
- type: ndcg_at_3
value: 40.416999999999994
- type: ndcg_at_5
value: 42.309999999999995
- type: precision_at_1
value: 35.924
- type: precision_at_10
value: 8.344
- type: precision_at_100
value: 1.367
- type: precision_at_1000
value: 0.181
- type: precision_at_3
value: 19.469
- type: precision_at_5
value: 13.771
- type: recall_at_1
value: 28.784
- type: recall_at_10
value: 53.92400000000001
- type: recall_at_100
value: 72.962
- type: recall_at_1000
value: 85.90100000000001
- type: recall_at_3
value: 42.574
- type: recall_at_5
value: 47.798
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 50.16499999999999
- type: f1
value: 43.57906972116264
- task:
type: Retrieval
dataset:
type: fiqa
name: MTEB FiQA2018
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 20.737
- type: map_at_10
value: 33.566
- type: map_at_100
value: 35.367
- type: map_at_1000
value: 35.546
- type: map_at_3
value: 29.881999999999998
- type: map_at_5
value: 31.818
- type: mrr_at_1
value: 41.975
- type: mrr_at_10
value: 50.410999999999994
- type: mrr_at_100
value: 51.172
- type: mrr_at_1000
value: 51.214999999999996
- type: mrr_at_3
value: 48.611
- type: mrr_at_5
value: 49.522
- type: ndcg_at_1
value: 41.975
- type: ndcg_at_10
value: 41.299
- type: ndcg_at_100
value: 47.768
- type: ndcg_at_1000
value: 50.882000000000005
- type: ndcg_at_3
value: 38.769
- type: ndcg_at_5
value: 39.106
- type: precision_at_1
value: 41.975
- type: precision_at_10
value: 11.296000000000001
- type: precision_at_100
value: 1.7840000000000003
- type: precision_at_1000
value: 0.23500000000000001
- type: precision_at_3
value: 26.029000000000003
- type: precision_at_5
value: 18.457
- type: recall_at_1
value: 20.737
- type: recall_at_10
value: 47.284
- type: recall_at_100
value: 71.286
- type: recall_at_1000
value: 89.897
- type: recall_at_3
value: 35.411
- type: recall_at_5
value: 39.987
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 87.84
- type: ap
value: 82.68294664793142
- type: f1
value: 87.8226441992267
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 93.35841313269493
- type: f1
value: 93.060022693275
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 66.58002735978113
- type: f1
value: 46.995919480823055
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 71.07935440484196
- type: f1
value: 69.13197875645403
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 76.63752521856087
- type: f1
value: 75.61348469613843
- task:
type: Retrieval
dataset:
type: nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 5.234
- type: map_at_10
value: 11.718
- type: map_at_100
value: 14.396
- type: map_at_1000
value: 15.661
- type: map_at_3
value: 8.951
- type: map_at_5
value: 10.233
- type: mrr_at_1
value: 43.034
- type: mrr_at_10
value: 52.161
- type: mrr_at_100
value: 52.729000000000006
- type: mrr_at_1000
value: 52.776
- type: mrr_at_3
value: 50.671
- type: mrr_at_5
value: 51.476
- type: ndcg_at_1
value: 41.331
- type: ndcg_at_10
value: 31.411
- type: ndcg_at_100
value: 28.459
- type: ndcg_at_1000
value: 37.114000000000004
- type: ndcg_at_3
value: 37.761
- type: ndcg_at_5
value: 35.118
- type: precision_at_1
value: 43.034
- type: precision_at_10
value: 22.878999999999998
- type: precision_at_100
value: 7.093000000000001
- type: precision_at_1000
value: 1.9560000000000002
- type: precision_at_3
value: 35.707
- type: precision_at_5
value: 30.279
- type: recall_at_1
value: 5.234
- type: recall_at_10
value: 14.745
- type: recall_at_100
value: 28.259
- type: recall_at_1000
value: 59.16400000000001
- type: recall_at_3
value: 10.08
- type: recall_at_5
value: 11.985
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
metrics:
- type: cos_sim_pearson
value: 83.33269306539026
- type: cos_sim_spearman
value: 79.71441518631086
- type: euclidean_pearson
value: 80.98109404189279
- type: euclidean_spearman
value: 79.71444969096095
- type: manhattan_pearson
value: 80.97223989357175
- type: manhattan_spearman
value: 79.64929261210406
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 83.7127498314437
- type: cos_sim_spearman
value: 78.73426610516154
- type: euclidean_pearson
value: 79.72827173736742
- type: euclidean_spearman
value: 78.731973450314
- type: manhattan_pearson
value: 79.71391822179304
- type: manhattan_spearman
value: 78.69626503719782
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 78.33449726355023
- type: cos_sim_spearman
value: 79.59703323420547
- type: euclidean_pearson
value: 79.87238808505464
- type: euclidean_spearman
value: 79.59703323420547
- type: manhattan_pearson
value: 79.5006260085966
- type: manhattan_spearman
value: 79.21864659717262
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 79.00088445445654
- type: cos_sim_spearman
value: 78.99977508575147
- type: euclidean_pearson
value: 78.63222924140206
- type: euclidean_spearman
value: 78.99976994069327
- type: manhattan_pearson
value: 78.35504771673297
- type: manhattan_spearman
value: 78.76306077740067
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 87.13160613452308
- type: cos_sim_spearman
value: 87.81435104273643
- type: euclidean_pearson
value: 87.22395745487297
- type: euclidean_spearman
value: 87.81435041827874
- type: manhattan_pearson
value: 87.17630476262896
- type: manhattan_spearman
value: 87.76535338976686
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 83.76424652225954
- type: cos_sim_spearman
value: 85.39745570134193
- type: euclidean_pearson
value: 84.6971466556576
- type: euclidean_spearman
value: 85.39745570134193
- type: manhattan_pearson
value: 84.61210275324463
- type: manhattan_spearman
value: 85.30727114432379
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-en)
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson
value: 86.87956530541486
- type: cos_sim_spearman
value: 87.13412608536781
- type: euclidean_pearson
value: 87.80084186244981
- type: euclidean_spearman
value: 87.13412608536781
- type: manhattan_pearson
value: 87.73101535306475
- type: manhattan_spearman
value: 87.05897655963285
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 83.70737517925419
- type: cos_sim_spearman
value: 84.84687698325351
- type: euclidean_pearson
value: 84.36525309890885
- type: euclidean_spearman
value: 84.84688249844098
- type: manhattan_pearson
value: 84.31171573973266
- type: manhattan_spearman
value: 84.79550448196474
- task:
type: Retrieval
dataset:
type: scifact
name: MTEB SciFact
config: default
split: test
revision: None
metrics:
- type: map_at_1
value: 48.178
- type: map_at_10
value: 59.24
- type: map_at_100
value: 59.902
- type: map_at_1000
value: 59.941
- type: map_at_3
value: 56.999
- type: map_at_5
value: 58.167
- type: mrr_at_1
value: 51
- type: mrr_at_10
value: 60.827
- type: mrr_at_100
value: 61.307
- type: mrr_at_1000
value: 61.341
- type: mrr_at_3
value: 59
- type: mrr_at_5
value: 60.033
- type: ndcg_at_1
value: 51
- type: ndcg_at_10
value: 64.366
- type: ndcg_at_100
value: 67.098
- type: ndcg_at_1000
value: 68.08
- type: ndcg_at_3
value: 60.409
- type: ndcg_at_5
value: 62.150000000000006
- type: precision_at_1
value: 51
- type: precision_at_10
value: 8.799999999999999
- type: precision_at_100
value: 1.027
- type: precision_at_1000
value: 0.11100000000000002
- type: precision_at_3
value: 24.444
- type: precision_at_5
value: 15.8
- type: recall_at_1
value: 48.178
- type: recall_at_10
value: 78.34400000000001
- type: recall_at_100
value: 90.36699999999999
- type: recall_at_1000
value: 98
- type: recall_at_3
value: 67.35
- type: recall_at_5
value: 71.989
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.87722772277228
- type: cos_sim_ap
value: 97.32479581402639
- type: cos_sim_f1
value: 93.74369323915236
- type: cos_sim_precision
value: 94.60285132382892
- type: cos_sim_recall
value: 92.9
- type: dot_accuracy
value: 99.87722772277228
- type: dot_ap
value: 97.32479581402637
- type: dot_f1
value: 93.74369323915236
- type: dot_precision
value: 94.60285132382892
- type: dot_recall
value: 92.9
- type: euclidean_accuracy
value: 99.87722772277228
- type: euclidean_ap
value: 97.32479581402639
- type: euclidean_f1
value: 93.74369323915236
- type: euclidean_precision
value: 94.60285132382892
- type: euclidean_recall
value: 92.9
- type: manhattan_accuracy
value: 99.87524752475248
- type: manhattan_ap
value: 97.29133330261223
- type: manhattan_f1
value: 93.59359359359361
- type: manhattan_precision
value: 93.687374749499
- type: manhattan_recall
value: 93.5
- type: max_accuracy
value: 99.87722772277228
- type: max_ap
value: 97.32479581402639
- type: max_f1
value: 93.74369323915236
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
metrics:
- type: accuracy
value: 72.60060000000001
- type: ap
value: 15.719924742317021
- type: f1
value: 56.30561683159878
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 63.71250707413696
- type: f1
value: 63.54808116265952
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 85.110568039578
- type: cos_sim_ap
value: 70.28927714315245
- type: cos_sim_f1
value: 65.03893361488716
- type: cos_sim_precision
value: 65.06469500924214
- type: cos_sim_recall
value: 65.0131926121372
- type: dot_accuracy
value: 85.110568039578
- type: dot_ap
value: 70.28928082939848
- type: dot_f1
value: 65.03893361488716
- type: dot_precision
value: 65.06469500924214
- type: dot_recall
value: 65.0131926121372
- type: euclidean_accuracy
value: 85.110568039578
- type: euclidean_ap
value: 70.28928621260852
- type: euclidean_f1
value: 65.03893361488716
- type: euclidean_precision
value: 65.06469500924214
- type: euclidean_recall
value: 65.0131926121372
- type: manhattan_accuracy
value: 85.02115992132086
- type: manhattan_ap
value: 70.05813255171925
- type: manhattan_f1
value: 64.59658311510164
- type: manhattan_precision
value: 61.24379285883188
- type: manhattan_recall
value: 68.33773087071239
- type: max_accuracy
value: 85.110568039578
- type: max_ap
value: 70.28928621260852
- type: max_f1
value: 65.03893361488716
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 88.99949547871309
- type: cos_sim_ap
value: 85.82819569154559
- type: cos_sim_f1
value: 78.37315338318439
- type: cos_sim_precision
value: 74.46454564358494
- type: cos_sim_recall
value: 82.71481367416075
- type: dot_accuracy
value: 88.99949547871309
- type: dot_ap
value: 85.82820043407936
- type: dot_f1
value: 78.37315338318439
- type: dot_precision
value: 74.46454564358494
- type: dot_recall
value: 82.71481367416075
- type: euclidean_accuracy
value: 88.99949547871309
- type: euclidean_ap
value: 85.82819622588083
- type: euclidean_f1
value: 78.37315338318439
- type: euclidean_precision
value: 74.46454564358494
- type: euclidean_recall
value: 82.71481367416075
- type: manhattan_accuracy
value: 88.98009081383165
- type: manhattan_ap
value: 85.77393389750326
- type: manhattan_f1
value: 78.38852097130243
- type: manhattan_precision
value: 75.06341600901916
- type: manhattan_recall
value: 82.0218663381583
- type: max_accuracy
value: 88.99949547871309
- type: max_ap
value: 85.82820043407936
- type: max_f1
value: 78.38852097130243