metadata
language:
- en
license: mit
model-index:
- name: Zeta-Alpha-E5-Mistral
results:
- dataset:
config: en
name: MTEB AmazonCounterfactualClassification (en)
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
split: test
type: mteb/amazon_counterfactual
metrics:
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value: 77.76119402985074
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value: 39.97673988468886
- type: ap_weighted
value: 39.97673988468886
- type: f1
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- type: f1_weighted
value: 79.55230307558237
- type: main_score
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task:
type: Classification
- dataset:
config: default
name: MTEB AmazonPolarityClassification
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
split: test
type: mteb/amazon_polarity
metrics:
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value: 96.61810000000001
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- type: ap_weighted
value: 94.99559013902017
- type: f1
value: 96.61758649480731
- type: f1_weighted
value: 96.61758649480731
- type: main_score
value: 96.61810000000001
task:
type: Classification
- dataset:
config: en
name: MTEB AmazonReviewsClassification (en)
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
split: test
type: mteb/amazon_reviews_multi
metrics:
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- type: f1
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- type: f1_weighted
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- type: main_score
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task:
type: Classification
- dataset:
config: default
name: MTEB ArguAna
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
split: test
type: mteb/arguana
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task:
type: Retrieval
- dataset:
config: default
name: MTEB ArxivClusteringP2P
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
split: test
type: mteb/arxiv-clustering-p2p
metrics:
- type: main_score
value: 52.026635060007244
- type: v_measure
value: 52.026635060007244
- type: v_measure_std
value: 14.357137408692006
task:
type: Clustering
- dataset:
config: default
name: MTEB ArxivClusteringS2S
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
split: test
type: mteb/arxiv-clustering-s2s
metrics:
- type: main_score
value: 47.834914950269855
- type: v_measure
value: 47.834914950269855
- type: v_measure_std
value: 14.487028918517247
task:
type: Clustering
- dataset:
config: default
name: MTEB AskUbuntuDupQuestions
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
split: test
type: mteb/askubuntudupquestions-reranking
metrics:
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value: 64.5808745066313
- type: map
value: 64.5808745066313
- type: mrr
value: 77.56540473991997
- type: nAUC_map_diff1
value: 23.168800779252464
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value: 27.79261544540621
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value: 43.302228243606045
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task:
type: Reranking
- dataset:
config: default
name: MTEB BIOSSES
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
split: test
type: mteb/biosses-sts
metrics:
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value: 86.61402175201368
- type: pearson
value: 87.97778619889539
- type: spearman
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task:
type: STS
- dataset:
config: default
name: MTEB Banking77Classification
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
split: test
type: mteb/banking77
metrics:
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- type: f1_weighted
value: 82.2932953112279
- type: main_score
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task:
type: Classification
- dataset:
config: default
name: MTEB BiorxivClusteringP2P
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
split: test
type: mteb/biorxiv-clustering-p2p
metrics:
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- type: v_measure
value: 43.73746639290943
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task:
type: Clustering
- dataset:
config: default
name: MTEB BiorxivClusteringS2S
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
split: test
type: mteb/biorxiv-clustering-s2s
metrics:
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task:
type: Clustering
- dataset:
config: default
name: MTEB CQADupstackRetrieval
revision: 46989137a86843e03a6195de44b09deda022eec7
split: test
type: CQADupstackRetrieval_is_a_combined_dataset
metrics:
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task:
type: Retrieval
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config: default
name: MTEB ClimateFEVER
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
split: test
type: mteb/climate-fever
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task:
type: Retrieval
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config: default
name: MTEB ImdbClassification
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
split: test
type: mteb/imdb
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task:
type: Classification
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config: default
name: MTEB MSMARCO
revision: c5a29a104738b98a9e76336939199e264163d4a0
split: dev
type: mteb/msmarco
metrics:
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task:
type: Retrieval
- dataset:
config: en
name: MTEB MTOPDomainClassification (en)
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
split: test
type: mteb/mtop_domain
metrics:
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value: 96.35430916552667
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- type: f1_weighted
value: 96.35506582065435
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value: 96.35430916552667
task:
type: Classification
- dataset:
config: en
name: MTEB MTOPIntentClassification (en)
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
split: test
type: mteb/mtop_intent
metrics:
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value: 80.51527587779297
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value: 59.350461259612345
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value: 81.51891267687044
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value: 80.51527587779297
task:
type: Classification
- dataset:
config: en
name: MTEB MassiveIntentClassification (en)
revision: 4672e20407010da34463acc759c162ca9734bca6
split: test
type: mteb/amazon_massive_intent
metrics:
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value: 77.31338264963013
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value: 75.29547524788576
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value: 76.26831259224058
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value: 77.31338264963013
task:
type: Classification
- dataset:
config: en
name: MTEB MassiveScenarioClassification (en)
revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
split: test
type: mteb/amazon_massive_scenario
metrics:
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value: 79.34057805450769
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value: 79.73023446597212
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value: 79.97982515131137
task:
type: Classification
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config: default
name: MTEB MedrxivClusteringP2P
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
split: test
type: mteb/medrxiv-clustering-p2p
metrics:
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value: 38.37635785818304
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task:
type: Clustering
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config: default
name: MTEB MedrxivClusteringS2S
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
split: test
type: mteb/medrxiv-clustering-s2s
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task:
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config: default
name: MTEB MindSmallReranking
revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7
split: test
type: mteb/mind_small
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task:
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config: default
name: MTEB NFCorpus
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
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value: 90.52900000000001
- type: ndcg_at_3
value: 87.443
- type: ndcg_at_5
value: 88.81
- type: precision_at_1
value: 83.26
- type: precision_at_10
value: 13.581999999999999
- type: precision_at_100
value: 1.541
- type: precision_at_1000
value: 0.157
- type: precision_at_20
value: 7.210999999999999
- type: precision_at_3
value: 38.323
- type: precision_at_5
value: 25.069999999999997
- type: recall_at_1
value: 72.379
- type: recall_at_10
value: 96.261
- type: recall_at_100
value: 99.779
- type: recall_at_1000
value: 99.996
- type: recall_at_20
value: 98.301
- type: recall_at_3
value: 89.101
- type: recall_at_5
value: 93.11500000000001
task:
type: Retrieval
- dataset:
config: default
name: MTEB RedditClustering
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
split: test
type: mteb/reddit-clustering
metrics:
- type: main_score
value: 61.87769077476204
- type: v_measure
value: 61.87769077476204
- type: v_measure_std
value: 5.290405218730049
task:
type: Clustering
- dataset:
config: default
name: MTEB RedditClusteringP2P
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
split: test
type: mteb/reddit-clustering-p2p
metrics:
- type: main_score
value: 68.29553057563754
- type: v_measure
value: 68.29553057563754
- type: v_measure_std
value: 13.019229253711732
task:
type: Clustering
- dataset:
config: default
name: MTEB SCIDOCS
revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
split: test
type: mteb/scidocs
metrics:
- type: main_score
value: 20.86
- type: map_at_1
value: 4.843
- type: map_at_10
value: 12.457
- type: map_at_100
value: 14.648
- type: map_at_1000
value: 14.965
- type: map_at_20
value: 13.596
- type: map_at_3
value: 8.776
- type: map_at_5
value: 10.528
- type: mrr_at_1
value: 23.799999999999997
- type: mrr_at_10
value: 34.93765873015872
- type: mrr_at_100
value: 36.054095036751825
- type: mrr_at_1000
value: 36.10871797082569
- type: mrr_at_20
value: 35.57880859465608
- type: mrr_at_3
value: 31.54999999999999
- type: mrr_at_5
value: 33.53999999999998
- type: nauc_map_at_1000_diff1
value: 16.889540490911525
- type: nauc_map_at_1000_max
value: 25.726340275186143
- type: nauc_map_at_1000_std
value: 9.926911665196988
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value: 16.889355521248202
- type: nauc_map_at_100_max
value: 25.628741550328126
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value: 9.637062917997012
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value: 16.972521218507854
- type: nauc_map_at_10_max
value: 24.810172126870363
- type: nauc_map_at_10_std
value: 7.09295422867669
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value: 24.9292922418417
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value: 15.49253311874767
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value: -0.4754734108717385
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value: 16.945564955989113
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value: 25.197327599885362
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value: 7.972256233219635
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value: 19.503723922705067
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value: 20.795879090480057
- type: nauc_map_at_3_std
value: 1.5828913591118658
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value: 19.80474780705204
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value: 24.040173591299723
- type: nauc_map_at_5_std
value: 4.153642430396917
- type: nauc_mrr_at_1000_diff1
value: 21.80300741603344
- type: nauc_mrr_at_1000_max
value: 19.98123409846586
- type: nauc_mrr_at_1000_std
value: 3.6325335777371377
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value: 21.804966803578946
- type: nauc_mrr_at_100_max
value: 19.9965104601956
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value: 3.6713772865070107
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value: 21.66109150475663
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value: 19.873876575424404
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value: 3.3387503298795584
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value: 19.926480855266213
- type: nauc_mrr_at_5_std
value: 2.901801717093585
- type: nauc_ndcg_at_1000_diff1
value: 16.92599483752314
- type: nauc_ndcg_at_1000_max
value: 27.126582080942814
- type: nauc_ndcg_at_1000_std
value: 16.638448489514683
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value: 16.96586885959473
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value: 26.675878724175046
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value: 15.369335585614245
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value: 16.59779893225997
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- type: nauc_precision_at_10_max
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- type: nauc_precision_at_10_std
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- type: nauc_precision_at_5_max
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- type: nauc_precision_at_5_std
value: 8.924759582566171
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value: 25.001682297424594
- type: nauc_recall_at_1000_std
value: 33.86296283879721
- type: nauc_recall_at_100_diff1
value: 10.435705067998168
- type: nauc_recall_at_100_max
value: 25.31622603650995
- type: nauc_recall_at_100_std
value: 26.758897185352097
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value: 11.110953419292343
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value: 25.970593144433085
- type: nauc_recall_at_10_std
value: 13.92252981022314
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value: 24.9292922418417
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value: 15.49253311874767
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value: 11.050515317424548
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value: 26.068866115743134
- type: nauc_recall_at_20_std
value: 16.13787216291987
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value: 17.013383740580203
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value: 22.49105285578937
- type: nauc_recall_at_3_std
value: 3.5741090487413687
- type: nauc_recall_at_5_diff1
value: 16.973540662242602
- type: nauc_recall_at_5_max
value: 26.78087164318061
- type: nauc_recall_at_5_std
value: 8.68040862354009
- type: ndcg_at_1
value: 23.799999999999997
- type: ndcg_at_10
value: 20.86
- type: ndcg_at_100
value: 29.145
- type: ndcg_at_1000
value: 34.518
- type: ndcg_at_20
value: 23.892
- type: ndcg_at_3
value: 19.541
- type: ndcg_at_5
value: 17.166999999999998
- type: precision_at_1
value: 23.799999999999997
- type: precision_at_10
value: 10.9
- type: precision_at_100
value: 2.281
- type: precision_at_1000
value: 0.357
- type: precision_at_20
value: 7.21
- type: precision_at_3
value: 18.3
- type: precision_at_5
value: 15.120000000000001
- type: recall_at_1
value: 4.843
- type: recall_at_10
value: 22.12
- type: recall_at_100
value: 46.257
- type: recall_at_1000
value: 72.382
- type: recall_at_20
value: 29.253
- type: recall_at_3
value: 11.158
- type: recall_at_5
value: 15.347
task:
type: Retrieval
- dataset:
config: default
name: MTEB SICK-R
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
split: test
type: mteb/sickr-sts
metrics:
- type: cosine_pearson
value: 85.65467419133347
- type: cosine_spearman
value: 81.88046945336663
- type: euclidean_pearson
value: 82.82887106181879
- type: euclidean_spearman
value: 81.88047605481775
- type: main_score
value: 81.88046945336663
- type: manhattan_pearson
value: 82.7839019603756
- type: manhattan_spearman
value: 81.83505450284663
- type: pearson
value: 85.65467419133347
- type: spearman
value: 81.88046945336663
task:
type: STS
- dataset:
config: default
name: MTEB STS12
revision: a0d554a64d88156834ff5ae9920b964011b16384
split: test
type: mteb/sts12-sts
metrics:
- type: cosine_pearson
value: 85.8979872663498
- type: cosine_spearman
value: 78.63991285161867
- type: euclidean_pearson
value: 81.20243176386163
- type: euclidean_spearman
value: 78.64021127260493
- type: main_score
value: 78.63991285161867
- type: manhattan_pearson
value: 81.58673652635328
- type: manhattan_spearman
value: 79.03930665482164
- type: pearson
value: 85.8979872663498
- type: spearman
value: 78.63991285161867
task:
type: STS
- dataset:
config: default
name: MTEB STS13
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
split: test
type: mteb/sts13-sts
metrics:
- type: cosine_pearson
value: 87.10598414063074
- type: cosine_spearman
value: 87.12110799581852
- type: euclidean_pearson
value: 86.52284239759508
- type: euclidean_spearman
value: 87.12110799581852
- type: main_score
value: 87.12110799581852
- type: manhattan_pearson
value: 86.61105352996736
- type: manhattan_spearman
value: 87.34100209521596
- type: pearson
value: 87.10598414063074
- type: spearman
value: 87.12110799581852
task:
type: STS
- dataset:
config: default
name: MTEB STS14
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
split: test
type: mteb/sts14-sts
metrics:
- type: cosine_pearson
value: 85.66540041627184
- type: cosine_spearman
value: 83.55263671417923
- type: euclidean_pearson
value: 84.2332532036626
- type: euclidean_spearman
value: 83.55264421653584
- type: main_score
value: 83.55263671417923
- type: manhattan_pearson
value: 84.14418954784165
- type: manhattan_spearman
value: 83.58193360267302
- type: pearson
value: 85.66540041627184
- type: spearman
value: 83.55263671417923
task:
type: STS
- dataset:
config: default
name: MTEB STS15
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
split: test
type: mteb/sts15-sts
metrics:
- type: cosine_pearson
value: 89.83404956912175
- type: cosine_spearman
value: 90.09569633194636
- type: euclidean_pearson
value: 89.31121256629982
- type: euclidean_spearman
value: 90.09569632193572
- type: main_score
value: 90.09569633194636
- type: manhattan_pearson
value: 89.30064909066367
- type: manhattan_spearman
value: 90.20232732019451
- type: pearson
value: 89.83404956912175
- type: spearman
value: 90.09569633194636
task:
type: STS
- dataset:
config: default
name: MTEB STS16
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
split: test
type: mteb/sts16-sts
metrics:
- type: cosine_pearson
value: 86.27894370732598
- type: cosine_spearman
value: 87.22000226558832
- type: euclidean_pearson
value: 85.92822715155758
- type: euclidean_spearman
value: 87.22000226558832
- type: main_score
value: 87.22000226558832
- type: manhattan_pearson
value: 85.9498561399522
- type: manhattan_spearman
value: 87.28837300894288
- type: pearson
value: 86.27894370732598
- type: spearman
value: 87.22000226558832
task:
type: STS
- dataset:
config: en-en
name: MTEB STS17 (en-en)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: cosine_pearson
value: 91.60185356782324
- type: cosine_spearman
value: 91.43471625912765
- type: euclidean_pearson
value: 91.52529087606635
- type: euclidean_spearman
value: 91.43471625912765
- type: main_score
value: 91.43471625912765
- type: manhattan_pearson
value: 91.34917173506308
- type: manhattan_spearman
value: 91.2112665439884
- type: pearson
value: 91.60185356782324
- type: spearman
value: 91.43471625912765
task:
type: STS
- dataset:
config: en
name: MTEB STS22 (en)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cosine_pearson
value: 68.735098373629
- type: cosine_spearman
value: 67.76156085991387
- type: euclidean_pearson
value: 68.38053954511516
- type: euclidean_spearman
value: 67.76156085991387
- type: main_score
value: 67.76156085991387
- type: manhattan_pearson
value: 68.4533080173714
- type: manhattan_spearman
value: 67.76676959397871
- type: pearson
value: 68.735098373629
- type: spearman
value: 67.76156085991387
task:
type: STS
- dataset:
config: default
name: MTEB STSBenchmark
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
split: test
type: mteb/stsbenchmark-sts
metrics:
- type: cosine_pearson
value: 87.63236624274985
- type: cosine_spearman
value: 88.27561759951514
- type: euclidean_pearson
value: 87.61137355553329
- type: euclidean_spearman
value: 88.27561759951514
- type: main_score
value: 88.27561759951514
- type: manhattan_pearson
value: 87.63505381780153
- type: manhattan_spearman
value: 88.41268943146845
- type: pearson
value: 87.63236624274985
- type: spearman
value: 88.27561759951514
task:
type: STS
- dataset:
config: default
name: MTEB SciDocsRR
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
split: test
type: mteb/scidocs-reranking
metrics:
- type: main_score
value: 85.16412972900244
- type: map
value: 85.16412972900244
- type: mrr
value: 96.15786628041529
- type: nAUC_map_diff1
value: -1.5068306084088756
- type: nAUC_map_max
value: 48.81296049442589
- type: nAUC_map_std
value: 65.0187132933644
- type: nAUC_mrr_diff1
value: 44.22872564939586
- type: nAUC_mrr_max
value: 85.19719227096536
- type: nAUC_mrr_std
value: 79.62669870868876
task:
type: Reranking
- dataset:
config: default
name: MTEB SciFact
revision: 0228b52cf27578f30900b9e5271d331663a030d7
split: test
type: mteb/scifact
metrics:
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value: 77.377
- type: map_at_1
value: 62.217
- type: map_at_10
value: 73.115
- type: map_at_100
value: 73.63499999999999
- type: map_at_1000
value: 73.644
- type: map_at_20
value: 73.528
- type: map_at_3
value: 70.62
- type: map_at_5
value: 72.16
- type: mrr_at_1
value: 65
- type: mrr_at_10
value: 74.05542328042326
- type: mrr_at_100
value: 74.46295785951277
- type: mrr_at_1000
value: 74.47168088874803
- type: mrr_at_20
value: 74.35632423132421
- type: mrr_at_3
value: 72.55555555555556
- type: mrr_at_5
value: 73.38888888888887
- type: nauc_map_at_1000_diff1
value: 72.31754010838618
- type: nauc_map_at_1000_max
value: 60.59518156728312
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value: -3.601504782295705
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value: 72.32057771059107
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value: 60.60481879601873
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value: -3.6030430073837167
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value: 72.15009895006031
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value: 60.49958178006608
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value: -4.305475753173601
- type: nauc_map_at_1_diff1
value: 76.32919417574946
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value: 54.77358788281581
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value: -9.773898055794557
- type: nauc_map_at_20_diff1
value: 72.15740734516393
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value: 60.61318821265446
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value: -3.6016854193910803
- type: nauc_map_at_3_diff1
value: 72.07435404889445
- type: nauc_map_at_3_max
value: 56.93970890047747
- type: nauc_map_at_3_std
value: -8.697324220121793
- type: nauc_map_at_5_diff1
value: 72.42599960854554
- type: nauc_map_at_5_max
value: 60.12535137001906
- type: nauc_map_at_5_std
value: -4.437892354037166
- type: nauc_mrr_at_1000_diff1
value: 72.75103842052889
- type: nauc_mrr_at_1000_max
value: 62.72341811793062
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value: -0.7759889099766357
- type: nauc_mrr_at_100_diff1
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task:
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value: -27.623494423158906
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value: 35.714
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value: 27.095999999999997
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value: 37.577
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value: 50.234
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value: 28.706
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value: 34.808
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value: 31.657999999999998
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value: 38.775999999999996
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value: 23.061
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value: 7.388
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value: 1.5650000000000002
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value: 18.776
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value: 36.735
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value: 31.429000000000002
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value: 2.708
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value: 16.645
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value: 45.953
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value: 84.553
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value: 26.259
- type: recall_at_3
value: 7.869
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value: 11.166
task:
type: Retrieval
- dataset:
config: default
name: MTEB ToxicConversationsClassification
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
split: test
type: mteb/toxic_conversations_50k
metrics:
- type: accuracy
value: 75.87890625
- type: ap
value: 16.4629793865873
- type: ap_weighted
value: 16.4629793865873
- type: f1
value: 58.32993265544471
- type: f1_weighted
value: 80.94360012442658
- type: main_score
value: 75.87890625
task:
type: Classification
- dataset:
config: default
name: MTEB TweetSentimentExtractionClassification
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
split: test
type: mteb/tweet_sentiment_extraction
metrics:
- type: accuracy
value: 65.21788341822298
- type: f1
value: 65.00914562845475
- type: f1_weighted
value: 63.672388825903845
- type: main_score
value: 65.21788341822298
task:
type: Classification
- dataset:
config: default
name: MTEB TwentyNewsgroupsClustering
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
split: test
type: mteb/twentynewsgroups-clustering
metrics:
- type: main_score
value: 57.152337838073485
- type: v_measure
value: 57.152337838073485
- type: v_measure_std
value: 0.8799366494028795
task:
type: Clustering
- dataset:
config: default
name: MTEB TwitterSemEval2015
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
split: test
type: mteb/twittersemeval2015-pairclassification
metrics:
- type: max_accuracy
value: 88.63920844012637
- type: max_ap
value: 81.41444048232692
- type: max_f1
value: 74.84396892115653
- type: accuracy
value: 88.63920844012637
- type: accuracy_threshold
value: 84.5294713973999
- type: ap
value: 81.41443623323144
- type: f1
value: 74.84396892115653
- type: f1_threshold
value: 82.87262320518494
- type: precision
value: 72.34671263235656
- type: recall
value: 77.5197889182058
- type: main_score
value: 81.41444048232692
task:
type: PairClassification
- dataset:
config: default
name: MTEB TwitterURLCorpus
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
split: test
type: mteb/twitterurlcorpus-pairclassification
metrics:
- type: max_accuracy
value: 89.90569332867622
- type: max_ap
value: 87.91825594329686
- type: max_f1
value: 80.35081439054949
- type: accuracy
value: 89.90569332867622
- type: accuracy_threshold
value: 81.01733326911926
- type: ap
value: 87.91824445175028
- type: f1
value: 80.35081439054949
- type: f1_threshold
value: 78.65387201309204
- type: precision
value: 75.0853013982739
- type: recall
value: 86.41053279950724
- type: main_score
value: 87.91825594329686
task:
type: PairClassification
tags:
- mteb
- transformers
- sentence-transformers
Zeta-Alpha-E5-Mistral
We introduce Zeta Alpha's first public embedding model, a retrieval-specialized, 7B parameter embedding model trained on top of E5-mistral-7b-instruct. This model marks the first published model from Zeta Alpha's open science embedding models.
Check out our blog post for a complete breakdown of the training set we used and all the training details: Zeta Alpha blog
We are also making available our internal evaluation set, called NanoBEIR, a collection of Nano (i.e., 50 queries+~10k documents) per BEIR dataset.
Lora Weights
The lora weights are also available, so there is no need to download the full model.
How to Run
The model was trained with the same instruction-tuning strategy as the original E5-mistral-7b-instruct model. Therefore, queries should be formatted as follows:
Instruct: <task description>\nQuery: <query>
Sentence Transformers
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("zeta-alpha-ai/Zeta-Alpha-E5-Mistral")
def get_detailed_instruct(task_description: str, query: str) -> str:
return f'Instruct: {task_description}\nQuery: {query}'
task = "Given a claim about climate change, retrieve documents that support or refute the claim"
queries = [
get_detailed_instruct(task, "In Alaska, brown bears are changing their feeding habits to eat elderberries that ripen earlier."),
get_detailed_instruct(task, "Local and regional sea levels continue to exhibit typical natural variability—in some places rising and in others falling.")
]
passages = [
"The brown bear ( Ursus arctos ) is a large bear with the widest distribution of any living ursid . The species is distributed across much of northern Eurasia and North America . It is one of the two largest terrestrial carnivorans alive today , rivaled in body size only by its close cousin , the polar bear ( Ursus maritimus ) , which is much less variable in size and averages larger due to this . There are several recognized subspecies , many of which are quite well-known within their native ranges , found in the brown bear species . The brown bear 's principal range includes parts of Russia , Central Asia , China , Canada , the United States ( mostly Alaska ) , Scandinavia and the Carpathian region ( especially Romania ) , Anatolia , and Caucasus . The brown bear is recognized as a national and state animal in several European countries . While the brown bear 's range has shrunk and it has faced local extinctions , it remains listed as a least concern species by the International Union for Conservation of Nature ( IUCN ) with a total population of approximately 200,000 . As of 2012 , this and the American black bear are the only bear species not classified as threatened by the IUCN . However , the Californian , North African ( Atlas bear ) , and Mexican subspecies were hunted to extinction in the nineteenth and early twentieth centuries , and many of the southern Asian subspecies are highly endangered . One of the smaller-bodied subspecies , the Himalayan brown bear , is critically endangered , occupying only 2 % of its former range and threatened by uncontrolled poaching for its parts . The Marsican brown bear , one of several currently isolated populations of the main Eurasian brown bear race , in central Italy is believed to have a population of just 30 to 40 bears .",
"ean sea level ( MSL ) ( abbreviated simply sea level ) is an average level of the surface of one or more of Earth 's oceans from which heights such as elevations may be measured . MSL is a type of vertical datuma standardised geodetic reference pointthat is used , for example , as a chart datum in cartography and marine navigation , or , in aviation , as the standard sea level at which atmospheric pressure is measured in order to calibrate altitude and , consequently , aircraft flight levels . A common and relatively straightforward mean sea-level standard is the midpoint between a mean low and mean high tide at a particular location . Sea levels can be affected by many factors and are known to have varied greatly over geological time scales . The careful measurement of variations in MSL can offer insights into ongoing climate change , and sea level rise has been widely quoted as evidence of ongoing global warming . The term above sea level generally refers to above mean sea level ( AMSL ) ."
]
embeddings = model.encode(queries + passages)
scores = model.similarity(embeddings[:2], embeddings[2:]) * 100
print(scores.tolist())
# [[66.12603759765625, 43.760101318359375], [47.67058563232422, 63.7889518737793]]
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}'
task = "Given a claim about climate change, retrieve documents that support or refute the claim"
queries = [
get_detailed_instruct(task, "In Alaska, brown bears are changing their feeding habits to eat elderberries that ripen earlier."),
get_detailed_instruct(task, "Local and regional sea levels continue to exhibit typical natural variability—in some places rising and in others falling.")
]
passages = [
"The brown bear ( Ursus arctos ) is a large bear with the widest distribution of any living ursid . The species is distributed across much of northern Eurasia and North America . It is one of the two largest terrestrial carnivorans alive today , rivaled in body size only by its close cousin , the polar bear ( Ursus maritimus ) , which is much less variable in size and averages larger due to this . There are several recognized subspecies , many of which are quite well-known within their native ranges , found in the brown bear species . The brown bear 's principal range includes parts of Russia , Central Asia , China , Canada , the United States ( mostly Alaska ) , Scandinavia and the Carpathian region ( especially Romania ) , Anatolia , and Caucasus . The brown bear is recognized as a national and state animal in several European countries . While the brown bear 's range has shrunk and it has faced local extinctions , it remains listed as a least concern species by the International Union for Conservation of Nature ( IUCN ) with a total population of approximately 200,000 . As of 2012 , this and the American black bear are the only bear species not classified as threatened by the IUCN . However , the Californian , North African ( Atlas bear ) , and Mexican subspecies were hunted to extinction in the nineteenth and early twentieth centuries , and many of the southern Asian subspecies are highly endangered . One of the smaller-bodied subspecies , the Himalayan brown bear , is critically endangered , occupying only 2 % of its former range and threatened by uncontrolled poaching for its parts . The Marsican brown bear , one of several currently isolated populations of the main Eurasian brown bear race , in central Italy is believed to have a population of just 30 to 40 bears .",
"ean sea level ( MSL ) ( abbreviated simply sea level ) is an average level of the surface of one or more of Earth 's oceans from which heights such as elevations may be measured . MSL is a type of vertical datuma standardised geodetic reference pointthat is used , for example , as a chart datum in cartography and marine navigation , or , in aviation , as the standard sea level at which atmospheric pressure is measured in order to calibrate altitude and , consequently , aircraft flight levels . A common and relatively straightforward mean sea-level standard is the midpoint between a mean low and mean high tide at a particular location . Sea levels can be affected by many factors and are known to have varied greatly over geological time scales . The careful measurement of variations in MSL can offer insights into ongoing climate change , and sea level rise has been widely quoted as evidence of ongoing global warming . The term above sea level generally refers to above mean sea level ( AMSL ) ."
]
# load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("zeta-alpha-ai/Zeta-Alpha-E5-Mistral")
model = AutoModel.from_pretrained("zeta-alpha-ai/Zeta-Alpha-E5-Mistral")
# get the embeddings
max_length = 4096
input_texts = queries + passages
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())
[[66.15530395507812, 43.65541458129883], [47.681705474853516, 63.67986297607422]]
Zeta Alpha
Zeta Alpha is the premier Neural Discovery Platform for AI and more. We are an Amsterdam-based R&D and product lab with a passion for AI technology, with offices on the Science Park campus of the University of Amsterdam. and in San Francisco.
The Zeta Alpha Research team:
- Arthur Câmara
- Dinos Papakostas
- Mathias Parisot
- Fernando Rejon Barrera
- Jakub Zavrel