---
tags:
- mteb
- transformers
- sentence-transformers
model-index:
- name: Linq-Embed-Mistral
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy
value: 84.43283582089552
- type: ap
value: 50.39222584035829
- type: f1
value: 78.47906270064071
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy
value: 95.70445
- type: ap
value: 94.28273900595173
- type: f1
value: 95.70048412173735
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy
value: 57.644000000000005
- type: f1
value: 56.993648296704876
- task:
type: Retrieval
dataset:
type: mteb/arguana
name: MTEB ArguAna
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: map_at_1
value: 45.804
- type: map_at_10
value: 61.742
- type: map_at_100
value: 62.07899999999999
- type: map_at_1000
value: 62.08
- type: map_at_3
value: 57.717
- type: map_at_5
value: 60.27
- type: mrr_at_1
value: 47.226
- type: mrr_at_10
value: 62.256
- type: mrr_at_100
value: 62.601
- type: mrr_at_1000
value: 62.601
- type: mrr_at_3
value: 58.203
- type: mrr_at_5
value: 60.767
- type: ndcg_at_1
value: 45.804
- type: ndcg_at_10
value: 69.649
- type: ndcg_at_100
value: 70.902
- type: ndcg_at_1000
value: 70.91199999999999
- type: ndcg_at_3
value: 61.497
- type: ndcg_at_5
value: 66.097
- type: precision_at_1
value: 45.804
- type: precision_at_10
value: 9.452
- type: precision_at_100
value: 0.996
- type: precision_at_1000
value: 0.1
- type: precision_at_3
value: 24.135
- type: precision_at_5
value: 16.714000000000002
- type: recall_at_1
value: 45.804
- type: recall_at_10
value: 94.523
- type: recall_at_100
value: 99.57300000000001
- type: recall_at_1000
value: 99.644
- type: recall_at_3
value: 72.404
- type: recall_at_5
value: 83.57
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure
value: 51.47612678878609
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure
value: 47.2977392340418
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map
value: 66.82016765243456
- type: mrr
value: 79.55227982236292
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson
value: 89.15068664186332
- type: cos_sim_spearman
value: 86.4013663041054
- type: euclidean_pearson
value: 87.36391302921588
- type: euclidean_spearman
value: 86.4013663041054
- type: manhattan_pearson
value: 87.46116676558589
- type: manhattan_spearman
value: 86.78149544753352
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy
value: 87.88311688311688
- type: f1
value: 87.82368154811464
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure
value: 42.72860396750569
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure
value: 39.58412067938718
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: map_at_1
value: 30.082666666666665
- type: map_at_10
value: 41.13875
- type: map_at_100
value: 42.45525
- type: map_at_1000
value: 42.561249999999994
- type: map_at_3
value: 37.822750000000006
- type: map_at_5
value: 39.62658333333333
- type: mrr_at_1
value: 35.584
- type: mrr_at_10
value: 45.4675
- type: mrr_at_100
value: 46.31016666666667
- type: mrr_at_1000
value: 46.35191666666666
- type: mrr_at_3
value: 42.86674999999999
- type: mrr_at_5
value: 44.31341666666666
- type: ndcg_at_1
value: 35.584
- type: ndcg_at_10
value: 47.26516666666667
- type: ndcg_at_100
value: 52.49108333333332
- type: ndcg_at_1000
value: 54.24575
- type: ndcg_at_3
value: 41.83433333333334
- type: ndcg_at_5
value: 44.29899999999999
- type: precision_at_1
value: 35.584
- type: precision_at_10
value: 8.390333333333334
- type: precision_at_100
value: 1.2941666666666667
- type: precision_at_1000
value: 0.16308333333333336
- type: precision_at_3
value: 19.414583333333333
- type: precision_at_5
value: 13.751
- type: recall_at_1
value: 30.082666666666665
- type: recall_at_10
value: 60.88875
- type: recall_at_100
value: 83.35141666666667
- type: recall_at_1000
value: 95.0805
- type: recall_at_3
value: 45.683749999999996
- type: recall_at_5
value: 52.08208333333333
- task:
type: Retrieval
dataset:
type: mteb/climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
metrics:
- type: map_at_1
value: 16.747
- type: map_at_10
value: 29.168
- type: map_at_100
value: 31.304
- type: map_at_1000
value: 31.496000000000002
- type: map_at_3
value: 24.57
- type: map_at_5
value: 26.886
- type: mrr_at_1
value: 37.524
- type: mrr_at_10
value: 50.588
- type: mrr_at_100
value: 51.28
- type: mrr_at_1000
value: 51.29899999999999
- type: mrr_at_3
value: 47.438
- type: mrr_at_5
value: 49.434
- type: ndcg_at_1
value: 37.524
- type: ndcg_at_10
value: 39.11
- type: ndcg_at_100
value: 46.373999999999995
- type: ndcg_at_1000
value: 49.370999999999995
- type: ndcg_at_3
value: 32.964
- type: ndcg_at_5
value: 35.028
- type: precision_at_1
value: 37.524
- type: precision_at_10
value: 12.137
- type: precision_at_100
value: 1.9929999999999999
- type: precision_at_1000
value: 0.256
- type: precision_at_3
value: 24.886
- type: precision_at_5
value: 18.762
- type: recall_at_1
value: 16.747
- type: recall_at_10
value: 45.486
- type: recall_at_100
value: 69.705
- type: recall_at_1000
value: 86.119
- type: recall_at_3
value: 30.070999999999998
- type: recall_at_5
value: 36.565
- task:
type: Retrieval
dataset:
type: mteb/dbpedia
name: MTEB DBPedia
config: default
split: test
revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
metrics:
- type: map_at_1
value: 10.495000000000001
- type: map_at_10
value: 24.005000000000003
- type: map_at_100
value: 34.37
- type: map_at_1000
value: 36.268
- type: map_at_3
value: 16.694
- type: map_at_5
value: 19.845
- type: mrr_at_1
value: 75.5
- type: mrr_at_10
value: 82.458
- type: mrr_at_100
value: 82.638
- type: mrr_at_1000
value: 82.64
- type: mrr_at_3
value: 81.25
- type: mrr_at_5
value: 82.125
- type: ndcg_at_1
value: 64.625
- type: ndcg_at_10
value: 51.322
- type: ndcg_at_100
value: 55.413999999999994
- type: ndcg_at_1000
value: 62.169
- type: ndcg_at_3
value: 56.818999999999996
- type: ndcg_at_5
value: 54.32900000000001
- type: precision_at_1
value: 75.5
- type: precision_at_10
value: 40.849999999999994
- type: precision_at_100
value: 12.882
- type: precision_at_1000
value: 2.394
- type: precision_at_3
value: 59.667
- type: precision_at_5
value: 52.2
- type: recall_at_1
value: 10.495000000000001
- type: recall_at_10
value: 29.226000000000003
- type: recall_at_100
value: 59.614
- type: recall_at_1000
value: 81.862
- type: recall_at_3
value: 17.97
- type: recall_at_5
value: 22.438
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy
value: 51.82
- type: f1
value: 47.794956731921054
- task:
type: Retrieval
dataset:
type: mteb/fever
name: MTEB FEVER
config: default
split: test
revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
metrics:
- type: map_at_1
value: 82.52199999999999
- type: map_at_10
value: 89.794
- type: map_at_100
value: 89.962
- type: map_at_1000
value: 89.972
- type: map_at_3
value: 88.95100000000001
- type: map_at_5
value: 89.524
- type: mrr_at_1
value: 88.809
- type: mrr_at_10
value: 93.554
- type: mrr_at_100
value: 93.577
- type: mrr_at_1000
value: 93.577
- type: mrr_at_3
value: 93.324
- type: mrr_at_5
value: 93.516
- type: ndcg_at_1
value: 88.809
- type: ndcg_at_10
value: 92.419
- type: ndcg_at_100
value: 92.95
- type: ndcg_at_1000
value: 93.10000000000001
- type: ndcg_at_3
value: 91.45299999999999
- type: ndcg_at_5
value: 92.05
- type: precision_at_1
value: 88.809
- type: precision_at_10
value: 10.911999999999999
- type: precision_at_100
value: 1.143
- type: precision_at_1000
value: 0.117
- type: precision_at_3
value: 34.623
- type: precision_at_5
value: 21.343999999999998
- type: recall_at_1
value: 82.52199999999999
- type: recall_at_10
value: 96.59400000000001
- type: recall_at_100
value: 98.55699999999999
- type: recall_at_1000
value: 99.413
- type: recall_at_3
value: 94.02199999999999
- type: recall_at_5
value: 95.582
- task:
type: Retrieval
dataset:
type: mteb/fiqa
name: MTEB FiQA2018
config: default
split: test
revision: 27a168819829fe9bcd655c2df245fb19452e8e06
metrics:
- type: map_at_1
value: 32.842
- type: map_at_10
value: 53.147
- type: map_at_100
value: 55.265
- type: map_at_1000
value: 55.37
- type: map_at_3
value: 46.495
- type: map_at_5
value: 50.214999999999996
- type: mrr_at_1
value: 61.574
- type: mrr_at_10
value: 68.426
- type: mrr_at_100
value: 68.935
- type: mrr_at_1000
value: 68.95400000000001
- type: mrr_at_3
value: 66.307
- type: mrr_at_5
value: 67.611
- type: ndcg_at_1
value: 61.574
- type: ndcg_at_10
value: 61.205
- type: ndcg_at_100
value: 67.25999999999999
- type: ndcg_at_1000
value: 68.657
- type: ndcg_at_3
value: 56.717
- type: ndcg_at_5
value: 58.196999999999996
- type: precision_at_1
value: 61.574
- type: precision_at_10
value: 16.852
- type: precision_at_100
value: 2.33
- type: precision_at_1000
value: 0.256
- type: precision_at_3
value: 37.5
- type: precision_at_5
value: 27.468999999999998
- type: recall_at_1
value: 32.842
- type: recall_at_10
value: 68.157
- type: recall_at_100
value: 89.5
- type: recall_at_1000
value: 97.68599999999999
- type: recall_at_3
value: 50.783
- type: recall_at_5
value: 58.672000000000004
- task:
type: Retrieval
dataset:
type: mteb/hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: ab518f4d6fcca38d87c25209f94beba119d02014
metrics:
- type: map_at_1
value: 39.068000000000005
- type: map_at_10
value: 69.253
- type: map_at_100
value: 70.036
- type: map_at_1000
value: 70.081
- type: map_at_3
value: 65.621
- type: map_at_5
value: 67.976
- type: mrr_at_1
value: 78.13600000000001
- type: mrr_at_10
value: 84.328
- type: mrr_at_100
value: 84.515
- type: mrr_at_1000
value: 84.52300000000001
- type: mrr_at_3
value: 83.52199999999999
- type: mrr_at_5
value: 84.019
- type: ndcg_at_1
value: 78.13600000000001
- type: ndcg_at_10
value: 76.236
- type: ndcg_at_100
value: 78.891
- type: ndcg_at_1000
value: 79.73400000000001
- type: ndcg_at_3
value: 71.258
- type: ndcg_at_5
value: 74.129
- type: precision_at_1
value: 78.13600000000001
- type: precision_at_10
value: 16.347
- type: precision_at_100
value: 1.839
- type: precision_at_1000
value: 0.19499999999999998
- type: precision_at_3
value: 47.189
- type: precision_at_5
value: 30.581999999999997
- type: recall_at_1
value: 39.068000000000005
- type: recall_at_10
value: 81.735
- type: recall_at_100
value: 91.945
- type: recall_at_1000
value: 97.44800000000001
- type: recall_at_3
value: 70.783
- type: recall_at_5
value: 76.455
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy
value: 94.7764
- type: ap
value: 92.67841294818406
- type: f1
value: 94.77375157383646
- task:
type: Retrieval
dataset:
type: mteb/msmarco
name: MTEB MSMARCO
config: default
split: dev
revision: c5a29a104738b98a9e76336939199e264163d4a0
metrics:
- type: map_at_1
value: 24.624
- type: map_at_10
value: 37.861
- type: map_at_100
value: 39.011
- type: map_at_1000
value: 39.052
- type: map_at_3
value: 33.76
- type: map_at_5
value: 36.153
- type: mrr_at_1
value: 25.358000000000004
- type: mrr_at_10
value: 38.5
- type: mrr_at_100
value: 39.572
- type: mrr_at_1000
value: 39.607
- type: mrr_at_3
value: 34.491
- type: mrr_at_5
value: 36.83
- type: ndcg_at_1
value: 25.358000000000004
- type: ndcg_at_10
value: 45.214999999999996
- type: ndcg_at_100
value: 50.56
- type: ndcg_at_1000
value: 51.507999999999996
- type: ndcg_at_3
value: 36.925999999999995
- type: ndcg_at_5
value: 41.182
- type: precision_at_1
value: 25.358000000000004
- type: precision_at_10
value: 7.090000000000001
- type: precision_at_100
value: 0.9740000000000001
- type: precision_at_1000
value: 0.106
- type: precision_at_3
value: 15.697
- type: precision_at_5
value: 11.599
- type: recall_at_1
value: 24.624
- type: recall_at_10
value: 67.78699999999999
- type: recall_at_100
value: 92.11200000000001
- type: recall_at_1000
value: 99.208
- type: recall_at_3
value: 45.362
- type: recall_at_5
value: 55.58
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy
value: 96.83310533515733
- type: f1
value: 96.57069781347995
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy
value: 89.5690834473324
- type: f1
value: 73.7275204564728
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 82.67316745124411
- type: f1
value: 79.70626515721662
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 85.01344989912575
- type: f1
value: 84.45181022816965
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure
value: 37.843426126777295
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure
value: 36.651728547241476
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map
value: 32.05750522793288
- type: mrr
value: 33.28067556869468
- task:
type: Retrieval
dataset:
type: mteb/nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
metrics:
- type: map_at_1
value: 6.744
- type: map_at_10
value: 16.235
- type: map_at_100
value: 20.767
- type: map_at_1000
value: 22.469
- type: map_at_3
value: 11.708
- type: map_at_5
value: 13.924
- type: mrr_at_1
value: 55.728
- type: mrr_at_10
value: 63.869
- type: mrr_at_100
value: 64.322
- type: mrr_at_1000
value: 64.342
- type: mrr_at_3
value: 62.022999999999996
- type: mrr_at_5
value: 63.105999999999995
- type: ndcg_at_1
value: 53.096
- type: ndcg_at_10
value: 41.618
- type: ndcg_at_100
value: 38.562999999999995
- type: ndcg_at_1000
value: 47.006
- type: ndcg_at_3
value: 47.657
- type: ndcg_at_5
value: 45.562999999999995
- type: precision_at_1
value: 55.108000000000004
- type: precision_at_10
value: 30.464000000000002
- type: precision_at_100
value: 9.737
- type: precision_at_1000
value: 2.2720000000000002
- type: precision_at_3
value: 44.376
- type: precision_at_5
value: 39.505
- type: recall_at_1
value: 6.744
- type: recall_at_10
value: 21.11
- type: recall_at_100
value: 39.69
- type: recall_at_1000
value: 70.44
- type: recall_at_3
value: 13.120000000000001
- type: recall_at_5
value: 16.669
- task:
type: Retrieval
dataset:
type: mteb/nq
name: MTEB NQ
config: default
split: test
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
metrics:
- type: map_at_1
value: 46.263
- type: map_at_10
value: 63.525
- type: map_at_100
value: 64.142
- type: map_at_1000
value: 64.14800000000001
- type: map_at_3
value: 59.653
- type: map_at_5
value: 62.244
- type: mrr_at_1
value: 51.796
- type: mrr_at_10
value: 65.764
- type: mrr_at_100
value: 66.155
- type: mrr_at_1000
value: 66.158
- type: mrr_at_3
value: 63.05500000000001
- type: mrr_at_5
value: 64.924
- type: ndcg_at_1
value: 51.766999999999996
- type: ndcg_at_10
value: 70.626
- type: ndcg_at_100
value: 72.905
- type: ndcg_at_1000
value: 73.021
- type: ndcg_at_3
value: 63.937999999999995
- type: ndcg_at_5
value: 68.00699999999999
- type: precision_at_1
value: 51.766999999999996
- type: precision_at_10
value: 10.768
- type: precision_at_100
value: 1.203
- type: precision_at_1000
value: 0.121
- type: precision_at_3
value: 28.409000000000002
- type: precision_at_5
value: 19.502
- type: recall_at_1
value: 46.263
- type: recall_at_10
value: 89.554
- type: recall_at_100
value: 98.914
- type: recall_at_1000
value: 99.754
- type: recall_at_3
value: 72.89999999999999
- type: recall_at_5
value: 82.1
- task:
type: Retrieval
dataset:
type: mteb/quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
metrics:
- type: map_at_1
value: 72.748
- type: map_at_10
value: 86.87700000000001
- type: map_at_100
value: 87.46199999999999
- type: map_at_1000
value: 87.47399999999999
- type: map_at_3
value: 83.95700000000001
- type: map_at_5
value: 85.82300000000001
- type: mrr_at_1
value: 83.62
- type: mrr_at_10
value: 89.415
- type: mrr_at_100
value: 89.484
- type: mrr_at_1000
value: 89.484
- type: mrr_at_3
value: 88.633
- type: mrr_at_5
value: 89.176
- type: ndcg_at_1
value: 83.62
- type: ndcg_at_10
value: 90.27
- type: ndcg_at_100
value: 91.23599999999999
- type: ndcg_at_1000
value: 91.293
- type: ndcg_at_3
value: 87.69500000000001
- type: ndcg_at_5
value: 89.171
- type: precision_at_1
value: 83.62
- type: precision_at_10
value: 13.683
- type: precision_at_100
value: 1.542
- type: precision_at_1000
value: 0.157
- type: precision_at_3
value: 38.363
- type: precision_at_5
value: 25.196
- type: recall_at_1
value: 72.748
- type: recall_at_10
value: 96.61699999999999
- type: recall_at_100
value: 99.789
- type: recall_at_1000
value: 99.997
- type: recall_at_3
value: 89.21
- type: recall_at_5
value: 93.418
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure
value: 61.51909029379199
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
metrics:
- type: v_measure
value: 68.24483162045645
- task:
type: Retrieval
dataset:
type: mteb/scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
metrics:
- type: map_at_1
value: 4.793
- type: map_at_10
value: 13.092
- type: map_at_100
value: 15.434000000000001
- type: map_at_1000
value: 15.748999999999999
- type: map_at_3
value: 9.139
- type: map_at_5
value: 11.033
- type: mrr_at_1
value: 23.599999999999998
- type: mrr_at_10
value: 35.892
- type: mrr_at_100
value: 36.962
- type: mrr_at_1000
value: 37.009
- type: mrr_at_3
value: 32.550000000000004
- type: mrr_at_5
value: 34.415
- type: ndcg_at_1
value: 23.599999999999998
- type: ndcg_at_10
value: 21.932
- type: ndcg_at_100
value: 30.433
- type: ndcg_at_1000
value: 35.668
- type: ndcg_at_3
value: 20.483999999999998
- type: ndcg_at_5
value: 17.964
- type: precision_at_1
value: 23.599999999999998
- type: precision_at_10
value: 11.63
- type: precision_at_100
value: 2.383
- type: precision_at_1000
value: 0.363
- type: precision_at_3
value: 19.567
- type: precision_at_5
value: 16.06
- type: recall_at_1
value: 4.793
- type: recall_at_10
value: 23.558
- type: recall_at_100
value: 48.376999999999995
- type: recall_at_1000
value: 73.75699999999999
- type: recall_at_3
value: 11.903
- type: recall_at_5
value: 16.278000000000002
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
- type: cos_sim_pearson
value: 87.31937967632581
- type: cos_sim_spearman
value: 84.30523596401186
- type: euclidean_pearson
value: 84.19537987069458
- type: euclidean_spearman
value: 84.30522052876
- type: manhattan_pearson
value: 84.16420807244911
- type: manhattan_spearman
value: 84.28515410219309
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson
value: 86.17180810119646
- type: cos_sim_spearman
value: 78.44413657529002
- type: euclidean_pearson
value: 81.69054139101816
- type: euclidean_spearman
value: 78.44412412142488
- type: manhattan_pearson
value: 82.04975789626462
- type: manhattan_spearman
value: 78.78390856857253
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson
value: 88.35737871089687
- type: cos_sim_spearman
value: 88.26850223126127
- type: euclidean_pearson
value: 87.44100858335746
- type: euclidean_spearman
value: 88.26850223126127
- type: manhattan_pearson
value: 87.61572015772133
- type: manhattan_spearman
value: 88.56229552813319
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson
value: 86.8395966764906
- type: cos_sim_spearman
value: 84.49441798385489
- type: euclidean_pearson
value: 85.3259176121388
- type: euclidean_spearman
value: 84.49442124804686
- type: manhattan_pearson
value: 85.35153862806513
- type: manhattan_spearman
value: 84.60094577432503
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson
value: 90.14048269057345
- type: cos_sim_spearman
value: 90.27866978947013
- type: euclidean_pearson
value: 89.35308361940393
- type: euclidean_spearman
value: 90.27866978947013
- type: manhattan_pearson
value: 89.37601244066997
- type: manhattan_spearman
value: 90.42707449698062
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson
value: 86.8522678865688
- type: cos_sim_spearman
value: 87.37396401580446
- type: euclidean_pearson
value: 86.37219665505377
- type: euclidean_spearman
value: 87.37396385867791
- type: manhattan_pearson
value: 86.44628823799896
- type: manhattan_spearman
value: 87.49116026788859
- 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: 92.94248481968916
- type: cos_sim_spearman
value: 92.68185242943188
- type: euclidean_pearson
value: 92.33802342092979
- type: euclidean_spearman
value: 92.68185242943188
- type: manhattan_pearson
value: 92.2011323340474
- type: manhattan_spearman
value: 92.43364757640346
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (en)
config: en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson
value: 70.2918782293091
- type: cos_sim_spearman
value: 68.61986257003369
- type: euclidean_pearson
value: 70.51920905899138
- type: euclidean_spearman
value: 68.61986257003369
- type: manhattan_pearson
value: 70.64673843811433
- type: manhattan_spearman
value: 68.86711466517345
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson
value: 88.62956838105524
- type: cos_sim_spearman
value: 88.80650007123052
- type: euclidean_pearson
value: 88.37976252122822
- type: euclidean_spearman
value: 88.80650007123052
- type: manhattan_pearson
value: 88.49866938476616
- type: manhattan_spearman
value: 89.02489665452616
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map
value: 86.40175229911527
- type: mrr
value: 96.61958230585682
- task:
type: Retrieval
dataset:
type: mteb/scifact
name: MTEB SciFact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: map_at_1
value: 63.05
- type: map_at_10
value: 73.844
- type: map_at_100
value: 74.313
- type: map_at_1000
value: 74.321
- type: map_at_3
value: 71.17999999999999
- type: map_at_5
value: 72.842
- type: mrr_at_1
value: 65.667
- type: mrr_at_10
value: 74.772
- type: mrr_at_100
value: 75.087
- type: mrr_at_1000
value: 75.095
- type: mrr_at_3
value: 72.944
- type: mrr_at_5
value: 74.078
- type: ndcg_at_1
value: 65.667
- type: ndcg_at_10
value: 78.31700000000001
- type: ndcg_at_100
value: 79.969
- type: ndcg_at_1000
value: 80.25
- type: ndcg_at_3
value: 74.099
- type: ndcg_at_5
value: 76.338
- type: precision_at_1
value: 65.667
- type: precision_at_10
value: 10.233
- type: precision_at_100
value: 1.107
- type: precision_at_1000
value: 0.11299999999999999
- type: precision_at_3
value: 28.889
- type: precision_at_5
value: 19.0
- type: recall_at_1
value: 63.05
- type: recall_at_10
value: 90.822
- type: recall_at_100
value: 97.667
- type: recall_at_1000
value: 100.0
- type: recall_at_3
value: 79.489
- type: recall_at_5
value: 85.161
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy
value: 99.83564356435643
- type: cos_sim_ap
value: 96.10619363017767
- type: cos_sim_f1
value: 91.61225514816677
- type: cos_sim_precision
value: 92.02825428859738
- type: cos_sim_recall
value: 91.2
- type: dot_accuracy
value: 99.83564356435643
- type: dot_ap
value: 96.10619363017767
- type: dot_f1
value: 91.61225514816677
- type: dot_precision
value: 92.02825428859738
- type: dot_recall
value: 91.2
- type: euclidean_accuracy
value: 99.83564356435643
- type: euclidean_ap
value: 96.10619363017769
- type: euclidean_f1
value: 91.61225514816677
- type: euclidean_precision
value: 92.02825428859738
- type: euclidean_recall
value: 91.2
- type: manhattan_accuracy
value: 99.84158415841584
- type: manhattan_ap
value: 96.27527798658713
- type: manhattan_f1
value: 92.0
- type: manhattan_precision
value: 92.0
- type: manhattan_recall
value: 92.0
- type: max_accuracy
value: 99.84158415841584
- type: max_ap
value: 96.27527798658713
- type: max_f1
value: 92.0
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure
value: 76.93753872885304
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure
value: 46.044085080870126
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map
value: 55.885129730227256
- type: mrr
value: 56.95062494694848
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson
value: 31.202047940935508
- type: cos_sim_spearman
value: 30.984832035722228
- type: dot_pearson
value: 31.20204247226978
- type: dot_spearman
value: 30.984832035722228
- task:
type: Retrieval
dataset:
type: mteb/trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
metrics:
- type: map_at_1
value: 0.245
- type: map_at_10
value: 2.249
- type: map_at_100
value: 14.85
- type: map_at_1000
value: 36.596000000000004
- type: map_at_3
value: 0.717
- type: map_at_5
value: 1.18
- type: mrr_at_1
value: 94.0
- type: mrr_at_10
value: 96.167
- type: mrr_at_100
value: 96.167
- type: mrr_at_1000
value: 96.167
- type: mrr_at_3
value: 95.667
- type: mrr_at_5
value: 96.167
- type: ndcg_at_1
value: 91.0
- type: ndcg_at_10
value: 87.09700000000001
- type: ndcg_at_100
value: 69.637
- type: ndcg_at_1000
value: 62.257
- type: ndcg_at_3
value: 90.235
- type: ndcg_at_5
value: 89.51400000000001
- type: precision_at_1
value: 94.0
- type: precision_at_10
value: 90.60000000000001
- type: precision_at_100
value: 71.38
- type: precision_at_1000
value: 27.400000000000002
- type: precision_at_3
value: 94.0
- type: precision_at_5
value: 93.2
- type: recall_at_1
value: 0.245
- type: recall_at_10
value: 2.366
- type: recall_at_100
value: 17.491
- type: recall_at_1000
value: 58.772999999999996
- type: recall_at_3
value: 0.7270000000000001
- type: recall_at_5
value: 1.221
- task:
type: Retrieval
dataset:
type: mteb/touche2020
name: MTEB Touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
metrics:
- type: map_at_1
value: 3.435
- type: map_at_10
value: 12.147
- type: map_at_100
value: 18.724
- type: map_at_1000
value: 20.426
- type: map_at_3
value: 6.526999999999999
- type: map_at_5
value: 9.198
- type: mrr_at_1
value: 48.980000000000004
- type: mrr_at_10
value: 62.970000000000006
- type: mrr_at_100
value: 63.288999999999994
- type: mrr_at_1000
value: 63.288999999999994
- type: mrr_at_3
value: 59.184000000000005
- type: mrr_at_5
value: 61.224000000000004
- type: ndcg_at_1
value: 46.939
- type: ndcg_at_10
value: 30.61
- type: ndcg_at_100
value: 41.683
- type: ndcg_at_1000
value: 53.144000000000005
- type: ndcg_at_3
value: 36.284
- type: ndcg_at_5
value: 34.345
- type: precision_at_1
value: 48.980000000000004
- type: precision_at_10
value: 26.122
- type: precision_at_100
value: 8.204
- type: precision_at_1000
value: 1.6019999999999999
- type: precision_at_3
value: 35.374
- type: precision_at_5
value: 32.653
- type: recall_at_1
value: 3.435
- type: recall_at_10
value: 18.953
- type: recall_at_100
value: 50.775000000000006
- type: recall_at_1000
value: 85.858
- type: recall_at_3
value: 7.813000000000001
- type: recall_at_5
value: 11.952
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy
value: 71.2938
- type: ap
value: 15.090139095602268
- type: f1
value: 55.23862650598296
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy
value: 64.7623089983022
- type: f1
value: 65.07617131099336
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure
value: 57.2988222684939
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy
value: 88.6034451928235
- type: cos_sim_ap
value: 81.51815279166863
- type: cos_sim_f1
value: 74.43794671864849
- type: cos_sim_precision
value: 73.34186939820742
- type: cos_sim_recall
value: 75.56728232189973
- type: dot_accuracy
value: 88.6034451928235
- type: dot_ap
value: 81.51816956866841
- type: dot_f1
value: 74.43794671864849
- type: dot_precision
value: 73.34186939820742
- type: dot_recall
value: 75.56728232189973
- type: euclidean_accuracy
value: 88.6034451928235
- type: euclidean_ap
value: 81.51817015121485
- type: euclidean_f1
value: 74.43794671864849
- type: euclidean_precision
value: 73.34186939820742
- type: euclidean_recall
value: 75.56728232189973
- type: manhattan_accuracy
value: 88.5736424867378
- type: manhattan_ap
value: 81.37610101292196
- type: manhattan_f1
value: 74.2504182215931
- type: manhattan_precision
value: 72.46922883697563
- type: manhattan_recall
value: 76.12137203166228
- type: max_accuracy
value: 88.6034451928235
- type: max_ap
value: 81.51817015121485
- type: max_f1
value: 74.43794671864849
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy
value: 89.53118329646446
- type: cos_sim_ap
value: 87.41972033060013
- type: cos_sim_f1
value: 79.4392523364486
- type: cos_sim_precision
value: 75.53457372951958
- type: cos_sim_recall
value: 83.7696335078534
- type: dot_accuracy
value: 89.53118329646446
- type: dot_ap
value: 87.41971646088945
- type: dot_f1
value: 79.4392523364486
- type: dot_precision
value: 75.53457372951958
- type: dot_recall
value: 83.7696335078534
- type: euclidean_accuracy
value: 89.53118329646446
- type: euclidean_ap
value: 87.41972415605997
- type: euclidean_f1
value: 79.4392523364486
- type: euclidean_precision
value: 75.53457372951958
- type: euclidean_recall
value: 83.7696335078534
- type: manhattan_accuracy
value: 89.5855163581325
- type: manhattan_ap
value: 87.51158697451964
- type: manhattan_f1
value: 79.54455087655883
- type: manhattan_precision
value: 74.96763643796416
- type: manhattan_recall
value: 84.71666153372344
- type: max_accuracy
value: 89.5855163581325
- type: max_ap
value: 87.51158697451964
- type: max_f1
value: 79.54455087655883
language:
- en
license: cc-by-nc-4.0
---
Linq-AI-Research/Linq-Embed-Mistral
**Linq-Embed-Mistral**
Linq-Embed-Mistral has been developed by building upon the foundations of the [E5-mistral-7b-instruct](https://huggingface.co/intfloat/e5-mistral-7b-instruct) and [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) models. We focus on improving text retrieval using advanced data refinement methods, including sophisticated data crafting, data filtering, and negative mining guided by teacher models, which are highly tailored to each task, to improve the quality of the synthetic data generated by LLM. These methods are applied to both existing benchmark dataset and highly tailored synthetic dataset generated via LLMs. Our efforts primarily aim to create high-quality triplet datasets (query, positive example, negative example), significantly improving text retrieval performance.
Linq-Embed-Mistral performs well in the MTEB benchmarks (as of May 29, 2024). The model excels in retrieval tasks, ranking **`1st`** among all models listed on the MTEB leaderboard with a performance score of **`60.2`**. This outstanding performance underscores its superior capability in enhancing search precision and reliability. The model achieves an average score of **`68.2`** across 56 datasets in the MTEB benchmarks, making it the highest-ranking publicly accessible model and third overall. (Please note that [NV-Emb-v1](https://huggingface.co/nvidia/NV-Embed-v1) and [voyage-large-2-instruct](https://docs.voyageai.com/embeddings/), ranked 1st and 2nd on the leaderboard as of May 29, reported their performance without releasing their models.)
This project is for research purposes only. Third-party datasets may be subject to additional terms and conditions under their associated licenses. Please refer to specific papers for more details:
- [MTEB benchmark](https://arxiv.org/abs/2210.07316)
- [Mistral](https://arxiv.org/abs/2310.06825)
- [E5-mistral-7b-instruct](https://arxiv.org/pdf/2401.00368.pdf)
For more details, refer to [this blog post](https://getlinq.com/blog/linq-embed-mistral/) and [this report](https://huggingface.co/Linq-AI-Research/Linq-Embed-Mistral/blob/main/LinqAIResearch2024_Linq-Embed-Mistral.pdf).
## How to use
Here is an example of how to encode queries and passages from the Mr.TyDi training dataset, both with Sentence Transformers or Transformers directly.
### Sentence Transformers
```python
from sentence_transformers import SentenceTransformer
# Load the model
model = SentenceTransformer("Linq-AI-Research/Linq-Embed-Mistral")
# Each query must come with a one-sentence instruction that describes the task
task = 'Given a question, retrieve Wikipedia passages that answer the question'
prompt = f"Instruct: {task}\nQuery: "
queries = [
"최초의 원자력 발전소는 무엇인가?",
"Who invented Hangul?"
]
passages = [
"현재 사용되는 핵분열 방식을 이용한 전력생산은 1948년 9월 미국 테네시주 오크리지에 설치된 X-10 흑연원자로에서 전구의 불을 밝히는 데 사용되면서 시작되었다. 그리고 1954년 6월에 구소련의 오브닌스크에 건설된 흑연감속 비등경수 압력관형 원자로를 사용한 오브닌스크 원자력 발전소가 시험적으로 전력생산을 시작하였고, 최초의 상업용 원자력 엉더이로를 사용한 영국 셀라필드 원자력 단지에 위치한 콜더 홀(Calder Hall) 원자력 발전소로, 1956년 10월 17일 상업 운전을 시작하였다.",
"Hangul was personally created and promulgated by the fourth king of the Joseon dynasty, Sejong the Great.[1][2] Sejong's scholarly institute, the Hall of Worthies, is often credited with the work, and at least one of its scholars was heavily involved in its creation, but it appears to have also been a personal project of Sejong."
]
# Encode the queries and passages. We only use the prompt for the queries
query_embeddings = model.encode(queries, prompt=prompt)
passage_embeddings = model.encode(passages)
# Compute the (cosine) similarity scores
scores = model.similarity(query_embeddings, passage_embeddings) * 100
print(scores.tolist())
# [[73.72908782958984, 30.122787475585938], [29.15508460998535, 79.25375366210938]]
```
### Transformers
```python
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 question, retrieve Wikipedia passages that answer the question'
queries = [
get_detailed_instruct(task, '최초의 원자력 발전소는 무엇인가?'),
get_detailed_instruct(task, 'Who invented Hangul?')
]
# No need to add instruction for retrieval documents
passages = [
"현재 사용되는 핵분열 방식을 이용한 전력생산은 1948년 9월 미국 테네시주 오크리지에 설치된 X-10 흑연원자로에서 전구의 불을 밝히는 데 사용되면서 시작되었다. 그리고 1954년 6월에 구소련의 오브닌스크에 건설된 흑연감속 비등경수 압력관형 원자로를 사용한 오브닌스크 원자력 발전소가 시험적으로 전력생산을 시작하였고, 최초의 상업용 원자력 엉더이로를 사용한 영국 셀라필드 원자력 단지에 위치한 콜더 홀(Calder Hall) 원자력 발전소로, 1956년 10월 17일 상업 운전을 시작하였다.",
"Hangul was personally created and promulgated by the fourth king of the Joseon dynasty, Sejong the Great.[1][2] Sejong's scholarly institute, the Hall of Worthies, is often credited with the work, and at least one of its scholars was heavily involved in its creation, but it appears to have also been a personal project of Sejong."
]
# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained('Linq-AI-Research/Linq-Embed-Mistral')
model = AutoModel.from_pretrained('Linq-AI-Research/Linq-Embed-Mistral')
max_length = 4096
input_texts = [*queries, *passages]
# 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())
# [[73.72909545898438, 30.122783660888672], [29.155078887939453, 79.25374603271484]]
```
### MTEB Benchmark Evaluation
Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB](https://arxiv.org/abs/2210.07316) benchmark.
## Evaluation Result
### MTEB (as of May 29, 2024)
| Model Name | Retrieval (15) | Average (56) |
| :------------------------------------------------------------------------------: | :------------: | :----------: |
| [Linq-Embed-Mistral](https://huggingface.co/Linq-AI-Research/Linq-Embed-Mistral) | 60.2 | 68.2 |
| [NV-Embed-v1](https://huggingface.co/nvidia/NV-Embed-v1) | 59.4 | 69.3 |
| [SFR-Embedding-Mistral](https://huggingface.co/Salesforce/SFR-Embedding-Mistral) | 59.0 | 67.6 |
| [voyage-large-2-instruct](https://docs.voyageai.com/docs/embeddings) | 58.3 | 68.3 |
| [GritLM-7B](https://huggingface.co/GritLM/GritLM-7B) | 57.4 | 66.8 |
| [voyage-lite-02-instruct](https://docs.voyageai.com/docs/embeddings) | 56.6 | 67.1 |
|[gte-Qwen1.5-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct)| 56.2 | 67.3 |
| [e5-mistral-7b-instruct](https://huggingface.co/intfloat/e5-mistral-7b-instruct) | 56.9 | 66.6 |
|[google-gecko.text-embedding-preview-0409](https://cloud.google.com/vertex-ai/generative-ai/docs/embeddings/get-text-embeddings?hl=ko#latest_models)| 55.7 | 66.3 |
|[text-embedding-3-large](https://openai.com/index/new-embedding-models-and-api-updates/)| 55.4 | 64.6 |
|[Cohere-embed-english-v3.0](https://huggingface.co/Cohere/Cohere-embed-english-v3.0)| 55.0 | 64.5 |
# Linq Research Team.
- [Junseong Kim](https://huggingface.co/Junseong)
- [Seolhwa Lee](https://huggingface.co/Seolhwa)
- [Jihoon Kwon](https://huggingface.co/Mayfull)
- [Sangmo Gu](https://huggingface.co/karma-os)
- Yejin Kim
- Minkyung Cho
- [Jy-yong Sohn](https://itml.yonsei.ac.kr/professor)
- [Chanyeol Choi](https://www.linkedin.com/in/chanyeolchoi)
# Citation
```bibtex
@misc{LinqAIResearch2024,
title={Linq-Embed-Mistral:Elevating Text Retrieval with Improved GPT Data Through Task-Specific Control and Quality Refinement},
author={Junseong Kim, Seolhwa Lee, Jihoon Kwon, Sangmo Gu, Yejin Kim, Minkyung Cho, Jy-yong Sohn, Chanyeol Choi},
howpublished={Linq AI Research Blog},
year={2024},
url={https://getlinq.com/blog/linq-embed-mistral/}
}
```