Linq-Embed-Mistral / README.md
Tom Aarsen
Integrate Linq-Embed-Mistral with Sentence Transformers
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metadata
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
          - 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
          - 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
          - type: manhattan_precision
            value: 92
          - type: manhattan_recall
            value: 92
          - type: max_accuracy
            value: 99.84158415841584
          - type: max_ap
            value: 96.27527798658713
          - type: max_f1
            value: 92
      - 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
          - 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
          - 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
          - 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
          - 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 and 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 and voyage-large-2-instruct, 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:

For more details, refer to this blog post and this report.

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

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

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 to reproduce evaluation results on the BEIR and MTEB benchmark.

Evaluation Result

MTEB (as of May 29, 2024)

Model Name Retrieval (15) Average (56)
Linq-Embed-Mistral 60.2 68.2
NV-Embed-v1 59.4 69.3
SFR-Embedding-Mistral 59.0 67.6
voyage-large-2-instruct 58.3 68.3
GritLM-7B 57.4 66.8
voyage-lite-02-instruct 56.6 67.1
gte-Qwen1.5-7B-instruct 56.2 67.3
e5-mistral-7b-instruct 56.9 66.6
google-gecko.text-embedding-preview-0409 55.7 66.3
text-embedding-3-large 55.4 64.6
Cohere-embed-english-v3.0 55.0 64.5

Linq Research Team.

Citation

@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/}
}