bge-en-icl / README.md
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metadata
tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  - transformers
  - mteb
license: apache-2.0
model-index:
  - name: bge-en-icl
    results:
      - dataset:
          config: en
          name: MTEB AmazonCounterfactualClassification (en)
          revision: e8379541af4e31359cca9fbcf4b00f2671dba205
          split: test
          type: mteb/amazon_counterfactual
        metrics:
          - type: accuracy
            value: 93.1492537313433
          - type: ap
            value: 72.56132559564212
          - type: f1
            value: 89.71796898040243
          - type: main_score
            value: 93.1492537313433
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB AmazonPolarityClassification
          revision: e2d317d38cd51312af73b3d32a06d1a08b442046
          split: test
          type: mteb/amazon_polarity
        metrics:
          - type: accuracy
            value: 96.98372499999999
          - type: ap
            value: 95.62303091773919
          - type: f1
            value: 96.98308191715637
          - type: main_score
            value: 96.98372499999999
        task:
          type: Classification
      - dataset:
          config: en
          name: MTEB AmazonReviewsClassification (en)
          revision: 1399c76144fd37290681b995c656ef9b2e06e26d
          split: test
          type: mteb/amazon_reviews_multi
        metrics:
          - type: accuracy
            value: 61.461999999999996
          - type: f1
            value: 60.57257766583118
          - type: main_score
            value: 61.461999999999996
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB ArguAna
          revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
          split: test
          type: mteb/arguana
        metrics:
          - type: main_score
            value: 83.07967801208441
          - type: ndcg_at_1
            value: 66.50071123755335
          - type: ndcg_at_3
            value: 80.10869593172173
          - type: ndcg_at_5
            value: 81.89670542467924
          - type: ndcg_at_10
            value: 83.07967801208441
          - type: ndcg_at_100
            value: 83.5991349601075
          - type: ndcg_at_1000
            value: 83.5991349601075
          - type: map_at_1
            value: 66.50071123755335
          - type: map_at_3
            value: 76.83736367946898
          - type: map_at_5
            value: 77.8473210052158
          - type: map_at_10
            value: 78.35472690735851
          - type: map_at_100
            value: 78.47388207611678
          - type: map_at_1000
            value: 78.47388207611678
          - type: precision_at_1
            value: 66.50071123755335
          - type: precision_at_3
            value: 29.848269321953076
          - type: precision_at_5
            value: 18.762446657183045
          - type: precision_at_10
            value: 9.736842105262909
          - type: precision_at_100
            value: 0.9964438122332677
          - type: precision_at_1000
            value: 0.09964438122332549
          - type: recall_at_1
            value: 66.50071123755335
          - type: recall_at_3
            value: 89.5448079658606
          - type: recall_at_5
            value: 93.8122332859175
          - type: recall_at_10
            value: 97.36842105263158
          - type: recall_at_100
            value: 99.6443812233286
          - type: recall_at_1000
            value: 99.6443812233286
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB ArxivClusteringP2P
          revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
          split: test
          type: mteb/arxiv-clustering-p2p
        metrics:
          - type: main_score
            value: 54.43859683357485
          - type: v_measure
            value: 54.43859683357485
          - type: v_measure_std
            value: 14.511128158596337
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB ArxivClusteringS2S
          revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
          split: test
          type: mteb/arxiv-clustering-s2s
        metrics:
          - type: main_score
            value: 49.33365996236564
          - type: v_measure
            value: 49.33365996236564
          - type: v_measure_std
            value: 14.61261944856548
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB AskUbuntuDupQuestions
          revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
          split: test
          type: mteb/askubuntudupquestions-reranking
        metrics:
          - type: main_score
            value: 65.15263966490278
          - type: map
            value: 65.15263966490278
          - type: mrr
            value: 77.90331090885107
        task:
          type: Reranking
      - dataset:
          config: default
          name: MTEB BIOSSES
          revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
          split: test
          type: mteb/biosses-sts
        metrics:
          - type: main_score
            value: 86.47365710792691
          - type: cosine_spearman
            value: 86.47365710792691
          - type: spearman
            value: 86.47365710792691
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB Banking77Classification
          revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
          split: test
          type: mteb/banking77
        metrics:
          - type: accuracy
            value: 91.48701298701299
          - type: f1
            value: 91.4733869423637
          - type: main_score
            value: 91.48701298701299
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB BiorxivClusteringP2P
          revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
          split: test
          type: mteb/biorxiv-clustering-p2p
        metrics:
          - type: main_score
            value: 53.050461108038036
          - type: v_measure
            value: 53.050461108038036
          - type: v_measure_std
            value: 0.9436104839012786
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB BiorxivClusteringS2S
          revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
          split: test
          type: mteb/biorxiv-clustering-s2s
        metrics:
          - type: main_score
            value: 48.38215568371151
          - type: v_measure
            value: 48.38215568371151
          - type: v_measure_std
            value: 0.9104384504649026
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB CQADupstackRetrieval
          revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
          split: test
          type: mteb/cqadupstack
        metrics:
          - type: main_score
            value: 47.308084499970704
          - type: ndcg_at_1
            value: 36.038578730542476
          - type: ndcg_at_3
            value: 41.931365356453036
          - type: ndcg_at_5
            value: 44.479015523894994
          - type: ndcg_at_10
            value: 47.308084499970704
          - type: ndcg_at_100
            value: 52.498062430513606
          - type: ndcg_at_1000
            value: 54.2908789514719
          - type: map_at_1
            value: 30.38821701528966
          - type: map_at_3
            value: 37.974871761903636
          - type: map_at_5
            value: 39.85399878507757
          - type: map_at_10
            value: 41.31456611036795
          - type: map_at_100
            value: 42.62907836655835
          - type: map_at_1000
            value: 42.737235870659845
          - type: precision_at_1
            value: 36.038578730542476
          - type: precision_at_3
            value: 19.39960180094633
          - type: precision_at_5
            value: 13.79264655952497
          - type: precision_at_10
            value: 8.399223517333388
          - type: precision_at_100
            value: 1.2992373779520896
          - type: precision_at_1000
            value: 0.16327170951909567
          - type: recall_at_1
            value: 30.38821701528966
          - type: recall_at_3
            value: 45.51645512564165
          - type: recall_at_5
            value: 52.06077167834868
          - type: recall_at_10
            value: 60.38864106788279
          - type: recall_at_100
            value: 82.76968509918343
          - type: recall_at_1000
            value: 94.84170217080344
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB ClimateFEVER
          revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
          split: test
          type: mteb/climate-fever
        metrics:
          - type: main_score
            value: 45.4272998284769
          - type: ndcg_at_1
            value: 44.36482084690554
          - type: ndcg_at_3
            value: 38.13005747178844
          - type: ndcg_at_5
            value: 40.83474510717123
          - type: ndcg_at_10
            value: 45.4272998284769
          - type: ndcg_at_100
            value: 52.880220707479516
          - type: ndcg_at_1000
            value: 55.364753427333
          - type: map_at_1
            value: 19.200868621064064
          - type: map_at_3
            value: 28.33785740137525
          - type: map_at_5
            value: 31.67162504524064
          - type: map_at_10
            value: 34.417673164090075
          - type: map_at_100
            value: 36.744753097028976
          - type: map_at_1000
            value: 36.91262189016135
          - type: precision_at_1
            value: 44.36482084690554
          - type: precision_at_3
            value: 29.14223669923975
          - type: precision_at_5
            value: 22.410423452768388
          - type: precision_at_10
            value: 14.293159609120309
          - type: precision_at_100
            value: 2.248859934853431
          - type: precision_at_1000
            value: 0.2722475570032542
          - type: recall_at_1
            value: 19.200868621064064
          - type: recall_at_3
            value: 34.132464712269176
          - type: recall_at_5
            value: 42.35613463626491
          - type: recall_at_10
            value: 52.50814332247546
          - type: recall_at_100
            value: 77.16178067318128
          - type: recall_at_1000
            value: 90.59174809989138
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB DBPedia
          revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
          split: test
          type: mteb/dbpedia
        metrics:
          - type: main_score
            value: 51.634197691802754
          - type: ndcg_at_1
            value: 64.375
          - type: ndcg_at_3
            value: 55.677549598242614
          - type: ndcg_at_5
            value: 53.44347199908503
          - type: ndcg_at_10
            value: 51.634197691802754
          - type: ndcg_at_100
            value: 56.202861267183415
          - type: ndcg_at_1000
            value: 63.146019108272576
          - type: map_at_1
            value: 9.789380503780919
          - type: map_at_3
            value: 16.146582195277016
          - type: map_at_5
            value: 19.469695222167193
          - type: map_at_10
            value: 24.163327344766145
          - type: map_at_100
            value: 35.47047690245571
          - type: map_at_1000
            value: 37.5147432331838
          - type: precision_at_1
            value: 76.25
          - type: precision_at_3
            value: 59.08333333333333
          - type: precision_at_5
            value: 52.24999999999997
          - type: precision_at_10
            value: 42.54999999999994
          - type: precision_at_100
            value: 13.460000000000008
          - type: precision_at_1000
            value: 2.4804999999999966
          - type: recall_at_1
            value: 9.789380503780919
          - type: recall_at_3
            value: 17.48487134027656
          - type: recall_at_5
            value: 22.312024269698806
          - type: recall_at_10
            value: 30.305380335237324
          - type: recall_at_100
            value: 62.172868946596424
          - type: recall_at_1000
            value: 85.32410301328747
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB EmotionClassification
          revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
          split: test
          type: mteb/emotion
        metrics:
          - type: accuracy
            value: 93.36
          - type: f1
            value: 89.73665936982262
          - type: main_score
            value: 93.36
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB FEVER
          revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
          split: test
          type: mteb/fever
        metrics:
          - type: main_score
            value: 92.82809814626805
          - type: ndcg_at_1
            value: 88.98889888988899
          - type: ndcg_at_3
            value: 91.82404417747676
          - type: ndcg_at_5
            value: 92.41785792357787
          - type: ndcg_at_10
            value: 92.82809814626805
          - type: ndcg_at_100
            value: 93.31730867509245
          - type: ndcg_at_1000
            value: 93.45171203408582
          - type: map_at_1
            value: 82.64125817343636
          - type: map_at_3
            value: 89.39970782792554
          - type: map_at_5
            value: 89.96799501378695
          - type: map_at_10
            value: 90.27479706587437
          - type: map_at_100
            value: 90.45185655778057
          - type: map_at_1000
            value: 90.46130471574544
          - type: precision_at_1
            value: 88.98889888988899
          - type: precision_at_3
            value: 34.923492349234245
          - type: precision_at_5
            value: 21.524152415244043
          - type: precision_at_10
            value: 11.033603360337315
          - type: precision_at_100
            value: 1.1521152115211895
          - type: precision_at_1000
            value: 0.11765676567657675
          - type: recall_at_1
            value: 82.64125817343636
          - type: recall_at_3
            value: 94.35195900542428
          - type: recall_at_5
            value: 95.9071323799047
          - type: recall_at_10
            value: 97.04234113887586
          - type: recall_at_100
            value: 98.77282371094255
          - type: recall_at_1000
            value: 99.5555567461508
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB FiQA2018
          revision: 27a168819829fe9bcd655c2df245fb19452e8e06
          split: test
          type: mteb/fiqa
        metrics:
          - type: main_score
            value: 59.67151242793314
          - type: ndcg_at_1
            value: 57.407407407407405
          - type: ndcg_at_3
            value: 53.79975378289304
          - type: ndcg_at_5
            value: 56.453379423655406
          - type: ndcg_at_10
            value: 59.67151242793314
          - type: ndcg_at_100
            value: 65.34055762539253
          - type: ndcg_at_1000
            value: 67.07707746043032
          - type: map_at_1
            value: 30.65887045053714
          - type: map_at_3
            value: 44.09107110881799
          - type: map_at_5
            value: 48.18573748068346
          - type: map_at_10
            value: 51.03680979612876
          - type: map_at_100
            value: 53.03165194566928
          - type: map_at_1000
            value: 53.16191096190861
          - type: precision_at_1
            value: 57.407407407407405
          - type: precision_at_3
            value: 35.493827160493886
          - type: precision_at_5
            value: 26.913580246913547
          - type: precision_at_10
            value: 16.435185185185155
          - type: precision_at_100
            value: 2.2685185185184986
          - type: precision_at_1000
            value: 0.25864197530863964
          - type: recall_at_1
            value: 30.65887045053714
          - type: recall_at_3
            value: 48.936723427464194
          - type: recall_at_5
            value: 58.55942925387371
          - type: recall_at_10
            value: 68.45128551147073
          - type: recall_at_100
            value: 88.24599311867836
          - type: recall_at_1000
            value: 98.18121693121691
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB HotpotQA
          revision: ab518f4d6fcca38d87c25209f94beba119d02014
          split: test
          type: mteb/hotpotqa
        metrics:
          - type: main_score
            value: 85.13780800141961
          - type: ndcg_at_1
            value: 89.9392302498312
          - type: ndcg_at_3
            value: 81.2061569376288
          - type: ndcg_at_5
            value: 83.53311592078133
          - type: ndcg_at_10
            value: 85.13780800141961
          - type: ndcg_at_100
            value: 87.02630661625386
          - type: ndcg_at_1000
            value: 87.47294723601075
          - type: map_at_1
            value: 44.9696151249156
          - type: map_at_3
            value: 76.46972766148966
          - type: map_at_5
            value: 78.47749268512187
          - type: map_at_10
            value: 79.49792611170005
          - type: map_at_100
            value: 80.09409086274644
          - type: map_at_1000
            value: 80.11950878917663
          - type: precision_at_1
            value: 89.9392302498312
          - type: precision_at_3
            value: 53.261309925724234
          - type: precision_at_5
            value: 33.79338284942924
          - type: precision_at_10
            value: 17.69750168805041
          - type: precision_at_100
            value: 1.9141120864280805
          - type: precision_at_1000
            value: 0.19721809588118133
          - type: recall_at_1
            value: 44.9696151249156
          - type: recall_at_3
            value: 79.8919648885888
          - type: recall_at_5
            value: 84.48345712356516
          - type: recall_at_10
            value: 88.48750844024308
          - type: recall_at_100
            value: 95.70560432140446
          - type: recall_at_1000
            value: 98.60904794058068
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB ImdbClassification
          revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
          split: test
          type: mteb/imdb
        metrics:
          - type: accuracy
            value: 96.9144
          - type: ap
            value: 95.45276911068486
          - type: f1
            value: 96.91412729455966
          - type: main_score
            value: 96.9144
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB MSMARCO
          revision: c5a29a104738b98a9e76336939199e264163d4a0
          split: dev
          type: mteb/msmarco
        metrics:
          - type: main_score
            value: 46.78865753107054
          - type: ndcg_at_1
            value: 26.63323782234957
          - type: ndcg_at_3
            value: 38.497585804985754
          - type: ndcg_at_5
            value: 42.72761631631636
          - type: ndcg_at_10
            value: 46.78865753107054
          - type: ndcg_at_100
            value: 51.96170786623209
          - type: ndcg_at_1000
            value: 52.82713901970963
          - type: map_at_1
            value: 25.89063992359121
          - type: map_at_3
            value: 35.299466730340654
          - type: map_at_5
            value: 37.68771887933786
          - type: map_at_10
            value: 39.40908074468253
          - type: map_at_100
            value: 40.53444082323405
          - type: map_at_1000
            value: 40.57183037649452
          - type: precision_at_1
            value: 26.63323782234957
          - type: precision_at_3
            value: 16.265520534861793
          - type: precision_at_5
            value: 11.902578796562304
          - type: precision_at_10
            value: 7.262177650430416
          - type: precision_at_100
            value: 0.9819484240687512
          - type: precision_at_1000
            value: 0.10571633237823287
          - type: recall_at_1
            value: 25.89063992359121
          - type: recall_at_3
            value: 46.99737344794652
          - type: recall_at_5
            value: 57.160936007640906
          - type: recall_at_10
            value: 69.43409742120343
          - type: recall_at_100
            value: 92.86413562559697
          - type: recall_at_1000
            value: 99.3230659025788
        task:
          type: Retrieval
      - dataset:
          config: en
          name: MTEB MTOPDomainClassification (en)
          revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
          split: test
          type: mteb/mtop_domain
        metrics:
          - type: accuracy
            value: 98.42225262197901
          - type: f1
            value: 98.31652547061115
          - type: main_score
            value: 98.42225262197901
        task:
          type: Classification
      - dataset:
          config: en
          name: MTEB MTOPIntentClassification (en)
          revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
          split: test
          type: mteb/mtop_intent
        metrics:
          - type: accuracy
            value: 94.00136798905609
          - type: f1
            value: 82.7022316533099
          - type: main_score
            value: 94.00136798905609
        task:
          type: Classification
      - dataset:
          config: en
          name: MTEB MassiveIntentClassification (en)
          revision: 4672e20407010da34463acc759c162ca9734bca6
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 82.92535305985204
          - type: f1
            value: 79.885538231847
          - type: main_score
            value: 82.92535305985204
        task:
          type: Classification
      - dataset:
          config: en
          name: MTEB MassiveScenarioClassification (en)
          revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 85.60188298587758
          - type: f1
            value: 84.87416963499224
          - type: main_score
            value: 85.60188298587758
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB MedrxivClusteringP2P
          revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
          split: test
          type: mteb/medrxiv-clustering-p2p
        metrics:
          - type: main_score
            value: 45.86171497327639
          - type: v_measure
            value: 45.86171497327639
          - type: v_measure_std
            value: 1.551347259003324
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB MedrxivClusteringS2S
          revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
          split: test
          type: mteb/medrxiv-clustering-s2s
        metrics:
          - type: main_score
            value: 44.33336692345644
          - type: v_measure
            value: 44.33336692345644
          - type: v_measure_std
            value: 1.5931408596404715
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB MindSmallReranking
          revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7
          split: test
          type: mteb/mind_small
        metrics:
          - type: main_score
            value: 30.597409734750503
          - type: map
            value: 30.597409734750503
          - type: mrr
            value: 31.397041548018457
        task:
          type: Reranking
      - dataset:
          config: default
          name: MTEB NFCorpus
          revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
          split: test
          type: mteb/nfcorpus
        metrics:
          - type: main_score
            value: 41.850870119787835
          - type: ndcg_at_1
            value: 52.47678018575851
          - type: ndcg_at_3
            value: 47.43993801247414
          - type: ndcg_at_5
            value: 45.08173173082719
          - type: ndcg_at_10
            value: 41.850870119787835
          - type: ndcg_at_100
            value: 37.79284946590978
          - type: ndcg_at_1000
            value: 46.58046062123418
          - type: map_at_1
            value: 6.892464464226138
          - type: map_at_3
            value: 12.113195798233127
          - type: map_at_5
            value: 13.968475602788812
          - type: map_at_10
            value: 16.47564069781326
          - type: map_at_100
            value: 20.671726065190025
          - type: map_at_1000
            value: 22.328875914012006
          - type: precision_at_1
            value: 53.86996904024768
          - type: precision_at_3
            value: 43.96284829721363
          - type: precision_at_5
            value: 38.69969040247682
          - type: precision_at_10
            value: 30.928792569659457
          - type: precision_at_100
            value: 9.507739938080498
          - type: precision_at_1000
            value: 2.25882352941176
          - type: recall_at_1
            value: 6.892464464226138
          - type: recall_at_3
            value: 13.708153358278407
          - type: recall_at_5
            value: 16.651919797359145
          - type: recall_at_10
            value: 21.01801714352559
          - type: recall_at_100
            value: 37.01672102843443
          - type: recall_at_1000
            value: 69.8307270724072
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB NQ
          revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
          split: test
          type: mteb/nq
        metrics:
          - type: main_score
            value: 73.88350836507092
          - type: ndcg_at_1
            value: 57.0683661645423
          - type: ndcg_at_3
            value: 67.89935813080585
          - type: ndcg_at_5
            value: 71.47769719452941
          - type: ndcg_at_10
            value: 73.88350836507092
          - type: ndcg_at_100
            value: 75.76561068060907
          - type: ndcg_at_1000
            value: 75.92437662684215
          - type: map_at_1
            value: 51.00424874468904
          - type: map_at_3
            value: 63.87359984550011
          - type: map_at_5
            value: 66.23696407879494
          - type: map_at_10
            value: 67.42415446608673
          - type: map_at_100
            value: 67.92692839842621
          - type: map_at_1000
            value: 67.93437922640133
          - type: precision_at_1
            value: 57.0683661645423
          - type: precision_at_3
            value: 29.692931633836416
          - type: precision_at_5
            value: 20.046349942062854
          - type: precision_at_10
            value: 10.950173812283
          - type: precision_at_100
            value: 1.1995944380069687
          - type: precision_at_1000
            value: 0.12146581691772171
          - type: recall_at_1
            value: 51.00424874468904
          - type: recall_at_3
            value: 75.93665507918116
          - type: recall_at_5
            value: 83.95133256083433
          - type: recall_at_10
            value: 90.78794901506375
          - type: recall_at_100
            value: 98.61915797605253
          - type: recall_at_1000
            value: 99.7827346465817
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB QuoraRetrieval
          revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
          split: test
          type: mteb/quora
        metrics:
          - type: main_score
            value: 90.95410848372035
          - type: ndcg_at_1
            value: 84.61999999999999
          - type: ndcg_at_3
            value: 88.57366734033212
          - type: ndcg_at_5
            value: 89.89804048972175
          - type: ndcg_at_10
            value: 90.95410848372035
          - type: ndcg_at_100
            value: 91.83227134455773
          - type: ndcg_at_1000
            value: 91.88368412611601
          - type: map_at_1
            value: 73.4670089207039
          - type: map_at_3
            value: 84.87862925508942
          - type: map_at_5
            value: 86.68002324701408
          - type: map_at_10
            value: 87.7165466015312
          - type: map_at_100
            value: 88.28718809614146
          - type: map_at_1000
            value: 88.29877148480672
          - type: precision_at_1
            value: 84.61999999999999
          - type: precision_at_3
            value: 38.82333333333838
          - type: precision_at_5
            value: 25.423999999998642
          - type: precision_at_10
            value: 13.787999999998583
          - type: precision_at_100
            value: 1.5442999999999767
          - type: precision_at_1000
            value: 0.15672999999997972
          - type: recall_at_1
            value: 73.4670089207039
          - type: recall_at_3
            value: 89.98389854832143
          - type: recall_at_5
            value: 93.88541046010576
          - type: recall_at_10
            value: 96.99779417520634
          - type: recall_at_100
            value: 99.80318763957743
          - type: recall_at_1000
            value: 99.99638888888889
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB RedditClustering
          revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
          split: test
          type: mteb/reddit-clustering
        metrics:
          - type: main_score
            value: 72.33008348681277
          - type: v_measure
            value: 72.33008348681277
          - type: v_measure_std
            value: 2.9203215463933008
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB RedditClusteringP2P
          revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
          split: test
          type: mteb/reddit-clustering-p2p
        metrics:
          - type: main_score
            value: 72.72079657828903
          - type: v_measure
            value: 72.72079657828903
          - type: v_measure_std
            value: 11.930271663428735
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB SCIDOCS
          revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
          split: test
          type: mteb/scidocs
        metrics:
          - type: main_score
            value: 25.25865384510787
          - type: ndcg_at_1
            value: 28.7
          - type: ndcg_at_3
            value: 23.61736427940938
          - type: ndcg_at_5
            value: 20.845690325673885
          - type: ndcg_at_10
            value: 25.25865384510787
          - type: ndcg_at_100
            value: 36.18596641088721
          - type: ndcg_at_1000
            value: 41.7166868935345
          - type: map_at_1
            value: 5.828333333333361
          - type: map_at_3
            value: 10.689166666666676
          - type: map_at_5
            value: 13.069916666666668
          - type: map_at_10
            value: 15.4901164021164
          - type: map_at_100
            value: 18.61493245565425
          - type: map_at_1000
            value: 18.99943478016456
          - type: precision_at_1
            value: 28.7
          - type: precision_at_3
            value: 22.30000000000006
          - type: precision_at_5
            value: 18.55999999999997
          - type: precision_at_10
            value: 13.289999999999946
          - type: precision_at_100
            value: 2.905000000000005
          - type: precision_at_1000
            value: 0.4218999999999946
          - type: recall_at_1
            value: 5.828333333333361
          - type: recall_at_3
            value: 13.548333333333387
          - type: recall_at_5
            value: 18.778333333333308
          - type: recall_at_10
            value: 26.939999999999902
          - type: recall_at_100
            value: 58.91333333333344
          - type: recall_at_1000
            value: 85.57499999999972
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB SICK-R
          revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
          split: test
          type: mteb/sickr-sts
        metrics:
          - type: main_score
            value: 83.86733787791422
          - type: cosine_spearman
            value: 83.86733787791422
          - type: spearman
            value: 83.86733787791422
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB STS12
          revision: a0d554a64d88156834ff5ae9920b964011b16384
          split: test
          type: mteb/sts12-sts
        metrics:
          - type: main_score
            value: 78.14269330480724
          - type: cosine_spearman
            value: 78.14269330480724
          - type: spearman
            value: 78.14269330480724
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB STS13
          revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
          split: test
          type: mteb/sts13-sts
        metrics:
          - type: main_score
            value: 86.58640009300751
          - type: cosine_spearman
            value: 86.58640009300751
          - type: spearman
            value: 86.58640009300751
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB STS14
          revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
          split: test
          type: mteb/sts14-sts
        metrics:
          - type: main_score
            value: 82.8292579957437
          - type: cosine_spearman
            value: 82.8292579957437
          - type: spearman
            value: 82.8292579957437
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB STS15
          revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
          split: test
          type: mteb/sts15-sts
        metrics:
          - type: main_score
            value: 87.77203714228862
          - type: cosine_spearman
            value: 87.77203714228862
          - type: spearman
            value: 87.77203714228862
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB STS16
          revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
          split: test
          type: mteb/sts16-sts
        metrics:
          - type: main_score
            value: 87.0439304006969
          - type: cosine_spearman
            value: 87.0439304006969
          - type: spearman
            value: 87.0439304006969
        task:
          type: STS
      - dataset:
          config: en-en
          name: MTEB STS17 (en-en)
          revision: faeb762787bd10488a50c8b5be4a3b82e411949c
          split: test
          type: mteb/sts17-crosslingual-sts
        metrics:
          - type: main_score
            value: 91.24736138013424
          - type: cosine_spearman
            value: 91.24736138013424
          - type: spearman
            value: 91.24736138013424
        task:
          type: STS
      - dataset:
          config: en
          name: MTEB STS22 (en)
          revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
          split: test
          type: mteb/sts22-crosslingual-sts
        metrics:
          - type: main_score
            value: 70.07326214706
          - type: cosine_spearman
            value: 70.07326214706
          - type: spearman
            value: 70.07326214706
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB STSBenchmark
          revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
          split: test
          type: mteb/stsbenchmark-sts
        metrics:
          - type: main_score
            value: 88.42076443255168
          - type: cosine_spearman
            value: 88.42076443255168
          - type: spearman
            value: 88.42076443255168
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB SciDocsRR
          revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
          split: test
          type: mteb/scidocs-reranking
        metrics:
          - type: main_score
            value: 86.9584489124583
          - type: map
            value: 86.9584489124583
          - type: mrr
            value: 96.59475328592976
        task:
          type: Reranking
      - dataset:
          config: default
          name: MTEB SciFact
          revision: 0228b52cf27578f30900b9e5271d331663a030d7
          split: test
          type: mteb/scifact
        metrics:
          - type: main_score
            value: 79.09159079425369
          - type: ndcg_at_1
            value: 66
          - type: ndcg_at_3
            value: 74.98853481223065
          - type: ndcg_at_5
            value: 77.29382051205019
          - type: ndcg_at_10
            value: 79.09159079425369
          - type: ndcg_at_100
            value: 80.29692802526776
          - type: ndcg_at_1000
            value: 80.55210036585547
          - type: map_at_1
            value: 62.994444444444454
          - type: map_at_3
            value: 71.7425925925926
          - type: map_at_5
            value: 73.6200925925926
          - type: map_at_10
            value: 74.50223544973547
          - type: map_at_100
            value: 74.82438594015447
          - type: map_at_1000
            value: 74.83420474892468
          - type: precision_at_1
            value: 66
          - type: precision_at_3
            value: 29.44444444444439
          - type: precision_at_5
            value: 19.40000000000008
          - type: precision_at_10
            value: 10.366666666666715
          - type: precision_at_100
            value: 1.0999999999999928
          - type: precision_at_1000
            value: 0.11200000000000007
          - type: recall_at_1
            value: 62.994444444444454
          - type: recall_at_3
            value: 80.89999999999998
          - type: recall_at_5
            value: 86.72777777777779
          - type: recall_at_10
            value: 91.88888888888887
          - type: recall_at_100
            value: 97
          - type: recall_at_1000
            value: 99
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB SprintDuplicateQuestions
          revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
          split: test
          type: mteb/sprintduplicatequestions-pairclassification
        metrics:
          - type: main_score
            value: 97.26819027722253
          - type: cos_sim_accuracy
            value: 99.88019801980198
          - type: cos_sim_accuracy_threshold
            value: 76.67685151100159
          - type: cos_sim_ap
            value: 97.23260568085786
          - type: cos_sim_f1
            value: 93.91824526420737
          - type: cos_sim_f1_threshold
            value: 75.82710981369019
          - type: cos_sim_precision
            value: 93.63817097415506
          - type: cos_sim_recall
            value: 94.19999999999999
          - type: dot_accuracy
            value: 99.88019801980198
          - type: dot_accuracy_threshold
            value: 76.67686343193054
          - type: dot_ap
            value: 97.23260568085786
          - type: dot_f1
            value: 93.91824526420737
          - type: dot_f1_threshold
            value: 75.8271336555481
          - type: dot_precision
            value: 93.63817097415506
          - type: dot_recall
            value: 94.19999999999999
          - type: euclidean_accuracy
            value: 99.88019801980198
          - type: euclidean_accuracy_threshold
            value: 68.29807758331299
          - type: euclidean_ap
            value: 97.23259982599497
          - type: euclidean_f1
            value: 93.91824526420737
          - type: euclidean_f1_threshold
            value: 69.53110694885254
          - type: euclidean_precision
            value: 93.63817097415506
          - type: euclidean_recall
            value: 94.19999999999999
          - type: manhattan_accuracy
            value: 99.87821782178217
          - type: manhattan_accuracy_threshold
            value: 3482.6908111572266
          - type: manhattan_ap
            value: 97.26819027722253
          - type: manhattan_f1
            value: 93.92592592592592
          - type: manhattan_f1_threshold
            value: 3555.5641174316406
          - type: manhattan_precision
            value: 92.78048780487805
          - type: manhattan_recall
            value: 95.1
          - type: max_accuracy
            value: 99.88019801980198
          - type: max_ap
            value: 97.26819027722253
          - type: max_f1
            value: 93.92592592592592
        task:
          type: PairClassification
      - dataset:
          config: default
          name: MTEB StackExchangeClustering
          revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
          split: test
          type: mteb/stackexchange-clustering
        metrics:
          - type: main_score
            value: 81.32419328350603
          - type: v_measure
            value: 81.32419328350603
          - type: v_measure_std
            value: 2.666861121694755
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB StackExchangeClusteringP2P
          revision: 815ca46b2622cec33ccafc3735d572c266efdb44
          split: test
          type: mteb/stackexchange-clustering-p2p
        metrics:
          - type: main_score
            value: 46.048387963107565
          - type: v_measure
            value: 46.048387963107565
          - type: v_measure_std
            value: 1.4102848576321703
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB StackOverflowDupQuestions
          revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
          split: test
          type: mteb/stackoverflowdupquestions-reranking
        metrics:
          - type: main_score
            value: 56.70574900554072
          - type: map
            value: 56.70574900554072
          - type: mrr
            value: 57.517109116373824
        task:
          type: Reranking
      - dataset:
          config: default
          name: MTEB SummEval
          revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
          split: test
          type: mteb/summeval
        metrics:
          - type: main_score
            value: 30.76932903185174
          - type: cosine_spearman
            value: 30.76932903185174
          - type: spearman
            value: 30.76932903185174
        task:
          type: Summarization
      - dataset:
          config: default
          name: MTEB TRECCOVID
          revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
          split: test
          type: mteb/trec-covid
        metrics:
          - type: main_score
            value: 79.07987651251462
          - type: ndcg_at_1
            value: 83
          - type: ndcg_at_3
            value: 79.86598407528447
          - type: ndcg_at_5
            value: 79.27684428714952
          - type: ndcg_at_10
            value: 79.07987651251462
          - type: ndcg_at_100
            value: 64.55029164391163
          - type: ndcg_at_1000
            value: 59.42333857860492
          - type: map_at_1
            value: 0.226053732680979
          - type: map_at_3
            value: 0.644034626013194
          - type: map_at_5
            value: 1.045196967937728
          - type: map_at_10
            value: 2.0197496659905085
          - type: map_at_100
            value: 13.316018005224159
          - type: map_at_1000
            value: 33.784766957424104
          - type: precision_at_1
            value: 88
          - type: precision_at_3
            value: 86.66666666666667
          - type: precision_at_5
            value: 85.20000000000002
          - type: precision_at_10
            value: 84.19999999999997
          - type: precision_at_100
            value: 67.88000000000001
          - type: precision_at_1000
            value: 26.573999999999998
          - type: recall_at_1
            value: 0.226053732680979
          - type: recall_at_3
            value: 0.6754273711472734
          - type: recall_at_5
            value: 1.1168649828059245
          - type: recall_at_10
            value: 2.2215081031265207
          - type: recall_at_100
            value: 16.694165236664727
          - type: recall_at_1000
            value: 56.7022214857503
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB Touche2020
          revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
          split: test
          type: mteb/touche2020
        metrics:
          - type: main_score
            value: 30.47934263207554
          - type: ndcg_at_1
            value: 33.6734693877551
          - type: ndcg_at_3
            value: 34.36843900446739
          - type: ndcg_at_5
            value: 32.21323786731918
          - type: ndcg_at_10
            value: 30.47934263207554
          - type: ndcg_at_100
            value: 41.49598869753928
          - type: ndcg_at_1000
            value: 52.32963949183662
          - type: map_at_1
            value: 3.0159801678718168
          - type: map_at_3
            value: 7.13837927642557
          - type: map_at_5
            value: 9.274004610363466
          - type: map_at_10
            value: 12.957368366814324
          - type: map_at_100
            value: 19.3070585127604
          - type: map_at_1000
            value: 20.809777161133532
          - type: precision_at_1
            value: 34.69387755102041
          - type: precision_at_3
            value: 36.054421768707485
          - type: precision_at_5
            value: 32.24489795918368
          - type: precision_at_10
            value: 27.142857142857146
          - type: precision_at_100
            value: 8.326530612244898
          - type: precision_at_1000
            value: 1.5755102040816336
          - type: recall_at_1
            value: 3.0159801678718168
          - type: recall_at_3
            value: 8.321771388428257
          - type: recall_at_5
            value: 11.737532394366069
          - type: recall_at_10
            value: 19.49315139822179
          - type: recall_at_100
            value: 50.937064145519685
          - type: recall_at_1000
            value: 83.4358283484675
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB ToxicConversationsClassification
          revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
          split: test
          type: mteb/toxic_conversations_50k
        metrics:
          - type: accuracy
            value: 93.173828125
          - type: ap
            value: 46.040184641424396
          - type: f1
            value: 80.77280549412752
          - type: main_score
            value: 93.173828125
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB TweetSentimentExtractionClassification
          revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
          split: test
          type: mteb/tweet_sentiment_extraction
        metrics:
          - type: accuracy
            value: 79.9320882852292
          - type: f1
            value: 80.22638685975485
          - type: main_score
            value: 79.9320882852292
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB TwentyNewsgroupsClustering
          revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
          split: test
          type: mteb/twentynewsgroups-clustering
        metrics:
          - type: main_score
            value: 68.98152919711418
          - type: v_measure
            value: 68.98152919711418
          - type: v_measure_std
            value: 1.2519720970652428
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB TwitterSemEval2015
          revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
          split: test
          type: mteb/twittersemeval2015-pairclassification
        metrics:
          - type: main_score
            value: 79.34189681158234
          - type: cos_sim_accuracy
            value: 87.68552184538356
          - type: cos_sim_accuracy_threshold
            value: 76.06316804885864
          - type: cos_sim_ap
            value: 79.34189149773933
          - type: cos_sim_f1
            value: 72.16386554621849
          - type: cos_sim_f1_threshold
            value: 73.62890243530273
          - type: cos_sim_precision
            value: 71.82435964453737
          - type: cos_sim_recall
            value: 72.5065963060686
          - type: dot_accuracy
            value: 87.68552184538356
          - type: dot_accuracy_threshold
            value: 76.06316208839417
          - type: dot_ap
            value: 79.34189231911259
          - type: dot_f1
            value: 72.16386554621849
          - type: dot_f1_threshold
            value: 73.62889647483826
          - type: dot_precision
            value: 71.82435964453737
          - type: dot_recall
            value: 72.5065963060686
          - type: euclidean_accuracy
            value: 87.68552184538356
          - type: euclidean_accuracy_threshold
            value: 69.19080018997192
          - type: euclidean_ap
            value: 79.34189681158234
          - type: euclidean_f1
            value: 72.16386554621849
          - type: euclidean_f1_threshold
            value: 72.62383103370667
          - type: euclidean_precision
            value: 71.82435964453737
          - type: euclidean_recall
            value: 72.5065963060686
          - type: manhattan_accuracy
            value: 87.661679680515
          - type: manhattan_accuracy_threshold
            value: 3408.807373046875
          - type: manhattan_ap
            value: 79.29617544165136
          - type: manhattan_f1
            value: 72.1957671957672
          - type: manhattan_f1_threshold
            value: 3597.7684020996094
          - type: manhattan_precision
            value: 72.38726790450929
          - type: manhattan_recall
            value: 72.00527704485488
          - type: max_accuracy
            value: 87.68552184538356
          - type: max_ap
            value: 79.34189681158234
          - type: max_f1
            value: 72.1957671957672
        task:
          type: PairClassification
      - dataset:
          config: default
          name: MTEB TwitterURLCorpus
          revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
          split: test
          type: mteb/twitterurlcorpus-pairclassification
        metrics:
          - type: main_score
            value: 87.8635519535718
          - type: cos_sim_accuracy
            value: 89.80672953778088
          - type: cos_sim_accuracy_threshold
            value: 73.09532165527344
          - type: cos_sim_ap
            value: 87.84251379545145
          - type: cos_sim_f1
            value: 80.25858884373845
          - type: cos_sim_f1_threshold
            value: 70.57080268859863
          - type: cos_sim_precision
            value: 77.14103110353643
          - type: cos_sim_recall
            value: 83.63874345549738
          - type: dot_accuracy
            value: 89.80672953778088
          - type: dot_accuracy_threshold
            value: 73.09532761573792
          - type: dot_ap
            value: 87.84251881260793
          - type: dot_f1
            value: 80.25858884373845
          - type: dot_f1_threshold
            value: 70.57079076766968
          - type: dot_precision
            value: 77.14103110353643
          - type: dot_recall
            value: 83.63874345549738
          - type: euclidean_accuracy
            value: 89.80672953778088
          - type: euclidean_accuracy_threshold
            value: 73.3548641204834
          - type: euclidean_ap
            value: 87.84251335039049
          - type: euclidean_f1
            value: 80.25858884373845
          - type: euclidean_f1_threshold
            value: 76.71923041343689
          - type: euclidean_precision
            value: 77.14103110353643
          - type: euclidean_recall
            value: 83.63874345549738
          - type: manhattan_accuracy
            value: 89.78150347343501
          - type: manhattan_accuracy_threshold
            value: 3702.7603149414062
          - type: manhattan_ap
            value: 87.8635519535718
          - type: manhattan_f1
            value: 80.27105660516332
          - type: manhattan_f1_threshold
            value: 3843.5962677001953
          - type: manhattan_precision
            value: 76.9361101306036
          - type: manhattan_recall
            value: 83.90822297505389
          - type: max_accuracy
            value: 89.80672953778088
          - type: max_ap
            value: 87.8635519535718
          - type: max_f1
            value: 80.27105660516332
        task:
          type: PairClassification

FlagEmbedding

For more details please refer to our Github: FlagEmbedding.

BGE-EN-ICL primarily demonstrates the following capabilities:

  • In-context learning ability: By providing few-shot examples in the query, it can significantly enhance the model's ability to handle new tasks.
  • Outstanding performance: The model has achieved state-of-the-art (SOTA) performance on both BEIR and AIR-Bench.

📑 Open-source Plan

  • Checkpoint
  • Training Data
  • Technical Report
  • Evaluation Pipeline

The technical report for BGE-EN-ICL can be found in Making Text Embedders Few-Shot Learners

Data List

Data Introduction
public-data Public data identical to e5-mistral
full-data The full dataset we used for training

Usage

Using FlagEmbedding

git clone https://github.com/FlagOpen/FlagEmbedding.git
cd FlagEmbedding
pip install -e .
from FlagEmbedding import FlagICLModel
queries = ["how much protein should a female eat", "summit define"]
documents = [
    "As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
    "Definition of summit for English Language Learners. : 1  the highest point of a mountain : the top of a mountain. : 2  the highest level. : 3  a meeting or series of meetings between the leaders of two or more governments."
]
examples = [
  {'instruct': 'Given a web search query, retrieve relevant passages that answer the query.',
   'query': 'what is a virtual interface',
   'response': "A virtual interface is a software-defined abstraction that mimics the behavior and characteristics of a physical network interface. It allows multiple logical network connections to share the same physical network interface, enabling efficient utilization of network resources. Virtual interfaces are commonly used in virtualization technologies such as virtual machines and containers to provide network connectivity without requiring dedicated hardware. They facilitate flexible network configurations and help in isolating network traffic for security and management purposes."},
  {'instruct': 'Given a web search query, retrieve relevant passages that answer the query.',
   'query': 'causes of back pain in female for a week',
   'response': "Back pain in females lasting a week can stem from various factors. Common causes include muscle strain due to lifting heavy objects or improper posture, spinal issues like herniated discs or osteoporosis, menstrual cramps causing referred pain, urinary tract infections, or pelvic inflammatory disease. Pregnancy-related changes can also contribute. Stress and lack of physical activity may exacerbate symptoms. Proper diagnosis by a healthcare professional is crucial for effective treatment and management."}
]
model = FlagICLModel('BAAI/bge-en-icl', 
                     query_instruction_for_retrieval="Given a web search query, retrieve relevant passages that answer the query.",
                     examples_for_task=examples,  # set `examples_for_task=None` to use model without examples
                     use_fp16=True) # Setting use_fp16 to True speeds up computation with a slight performance degradation
embeddings_1 = model.encode_queries(queries)
embeddings_2 = model.encode_corpus(documents)
similarity = embeddings_1 @ embeddings_2.T
print(similarity)

By default, FlagICLModel will use all available GPUs when encoding. Please set os.environ["CUDA_VISIBLE_DEVICES"] to select specific GPUs. You also can set os.environ["CUDA_VISIBLE_DEVICES"]="" to make all GPUs unavailable.

Using HuggingFace Transformers

With the transformers package, you can use the model like this: First, you pass your input through the transformer model, then you select the last hidden state of the first token (i.e., [CLS]) as the sentence embedding.

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}\n<query>{query}'

def get_detailed_example(task_description: str, query: str, response: str) -> str:
    return f'<instruct>{task_description}\n<query>{query}\n<response>{response}'

def get_new_queries(queries, query_max_len, examples_prefix, tokenizer):
    inputs = tokenizer(
        queries,
        max_length=query_max_len - len(tokenizer('<s>', add_special_tokens=False)['input_ids']) - len(
            tokenizer('\n<response></s>', add_special_tokens=False)['input_ids']),
        return_token_type_ids=False,
        truncation=True,
        return_tensors=None,
        add_special_tokens=False
    )
    prefix_ids = tokenizer(examples_prefix, add_special_tokens=False)['input_ids']
    suffix_ids = tokenizer('\n<response>', add_special_tokens=False)['input_ids']
    new_max_length = (len(prefix_ids) + len(suffix_ids) + query_max_len + 8) // 8 * 8 + 8
    new_queries = tokenizer.batch_decode(inputs['input_ids'])
    for i in range(len(new_queries)):
        new_queries[i] = examples_prefix + new_queries[i] + '\n<response>'
    return new_max_length, new_queries

task = 'Given a web search query, retrieve relevant passages that answer the query.'
examples = [
  {'instruct': 'Given a web search query, retrieve relevant passages that answer the query.',
   'query': 'what is a virtual interface',
   'response': "A virtual interface is a software-defined abstraction that mimics the behavior and characteristics of a physical network interface. It allows multiple logical network connections to share the same physical network interface, enabling efficient utilization of network resources. Virtual interfaces are commonly used in virtualization technologies such as virtual machines and containers to provide network connectivity without requiring dedicated hardware. They facilitate flexible network configurations and help in isolating network traffic for security and management purposes."},
  {'instruct': 'Given a web search query, retrieve relevant passages that answer the query.',
   'query': 'causes of back pain in female for a week',
   'response': "Back pain in females lasting a week can stem from various factors. Common causes include muscle strain due to lifting heavy objects or improper posture, spinal issues like herniated discs or osteoporosis, menstrual cramps causing referred pain, urinary tract infections, or pelvic inflammatory disease. Pregnancy-related changes can also contribute. Stress and lack of physical activity may exacerbate symptoms. Proper diagnosis by a healthcare professional is crucial for effective treatment and management."}
]
examples = [get_detailed_example(e['instruct'], e['query'], e['response']) for e in examples]
examples_prefix = '\n\n'.join(examples) + '\n\n' # if there not exists any examples, just set examples_prefix = ''
queries = [
    get_detailed_instruct(task, 'how much protein should a female eat'),
    get_detailed_instruct(task, 'summit define')
]
documents = [
    "As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
    "Definition of summit for English Language Learners. : 1  the highest point of a mountain : the top of a mountain. : 2  the highest level. : 3  a meeting or series of meetings between the leaders of two or more governments."
]
query_max_len, doc_max_len = 512, 512

tokenizer = AutoTokenizer.from_pretrained('BAAI/bge-en-icl')
model = AutoModel.from_pretrained('BAAI/bge-en-icl')
model.eval()

new_query_max_len, new_queries = get_new_queries(queries, query_max_len, examples_prefix, tokenizer)

query_batch_dict = tokenizer(new_queries, max_length=new_query_max_len, padding=True, truncation=True, return_tensors='pt')
doc_batch_dict = tokenizer(documents, max_length=doc_max_len, padding=True, truncation=True, return_tensors='pt')

with torch.no_grad():
    query_outputs = model(**query_batch_dict)
    query_embeddings = last_token_pool(query_outputs.last_hidden_state, query_batch_dict['attention_mask'])
    doc_outputs = model(**doc_batch_dict)
    doc_embeddings = last_token_pool(doc_outputs.last_hidden_state, doc_batch_dict['attention_mask'])
    
# normalize embeddings
query_embeddings = F.normalize(query_embeddings, p=2, dim=1)
doc_embeddings = F.normalize(doc_embeddings, p=2, dim=1)
scores = (query_embeddings @ doc_embeddings.T) * 100
print(scores.tolist())

Evaluation

bge-en-icl achieve state-of-the-art performance on both MTEB and AIR-Bench leaderboard!

BEIR

BEIR

QA (en, nDCG@10):

AIR-Bench_24.04 wiki web news healthcare law finance arxiv msmarco ALL (8)
e5-mistral-7b-instruct 61.67 44.41 48.18 56.32 19.32 54.79 44.78 59.03 48.56
SFR-Embedding-Mistral 63.46 51.27 52.21 58.76 23.27 56.94 47.75 58.99 51.58
NV-Embed-v1 62.84 50.42 51.46 58.53 20.65 49.89 46.10 60.27 50.02
Linq-Embed-Mistral 61.04 48.41 49.44 60.18 20.34 50.04 47.56 60.50 49.69
gte-Qwen2-7B-instruct 63.46 51.20 54.07 54.20 22.31 58.20 40.27 58.39 50.26
stella_en_1.5B_v5 61.99 50.88 53.87 58.81 23.22 57.26 44.81 61.38 51.53
bge-en-icl zero-shot 64.61 54.40 55.11 57.25 25.10 54.81 48.46 63.71 52.93
bge-en-icl few-shot 64.94 55.11 56.02 58.85 28.29 57.16 50.04 64.50 54.36

Long-Doc (en, Recall@10):

AIR-Bench_24.04 arxiv (4) book (2) healthcare (5) law (4) ALL (15)
text-embedding-3-large 74.53 73.16 65.83 64.47 68.77
e5-mistral-7b-instruct 72.14 72.44 68.44 62.92 68.49
SFR-Embedding-Mistral 72.79 72.41 67.94 64.83 69.00
NV-Embed-v1 77.65 75.49 72.38 69.55 73.45
Linq-Embed-Mistral 75.46 73.81 71.58 68.58 72.11
gte-Qwen2-7B-instruct 63.93 68.51 65.59 65.26 65.45
stella_en_1.5B_v5 73.17 74.38 70.02 69.32 71.25
bge-en-icl zero-shot 78.30 78.21 73.65 67.09 73.75
bge-en-icl few-shot 79.63 79.36 74.80 67.79 74.83

Model List

bge is short for BAAI general embedding.

Model Language Description query instruction for retrieval [1]
BAAI/bge-en-icl English - A LLM-based embedding model with in-context learning capabilities, which can fully leverage the model's potential based on a few shot examples Provide instructions and few-shot examples freely based on the given task.
BAAI/bge-m3 Multilingual Inference Fine-tune Multi-Functionality(dense retrieval, sparse retrieval, multi-vector(colbert)), Multi-Linguality, and Multi-Granularity(8192 tokens)
BAAI/llm-embedder English Inference Fine-tune a unified embedding model to support diverse retrieval augmentation needs for LLMs See README
BAAI/bge-reranker-large Chinese and English Inference Fine-tune a cross-encoder model which is more accurate but less efficient [2]
BAAI/bge-reranker-base Chinese and English Inference Fine-tune a cross-encoder model which is more accurate but less efficient [2]
BAAI/bge-large-en-v1.5 English Inference Fine-tune version 1.5 with more reasonable similarity distribution Represent this sentence for searching relevant passages:
BAAI/bge-base-en-v1.5 English Inference Fine-tune version 1.5 with more reasonable similarity distribution Represent this sentence for searching relevant passages:
BAAI/bge-small-en-v1.5 English Inference Fine-tune version 1.5 with more reasonable similarity distribution Represent this sentence for searching relevant passages:
BAAI/bge-large-zh-v1.5 Chinese Inference Fine-tune version 1.5 with more reasonable similarity distribution 为这个句子生成表示以用于检索相关文章:
BAAI/bge-base-zh-v1.5 Chinese Inference Fine-tune version 1.5 with more reasonable similarity distribution 为这个句子生成表示以用于检索相关文章:
BAAI/bge-small-zh-v1.5 Chinese Inference Fine-tune version 1.5 with more reasonable similarity distribution 为这个句子生成表示以用于检索相关文章:
BAAI/bge-large-en English Inference Fine-tune :trophy: rank 1st in MTEB leaderboard Represent this sentence for searching relevant passages:
BAAI/bge-base-en English Inference Fine-tune a base-scale model but with similar ability to bge-large-en Represent this sentence for searching relevant passages:
BAAI/bge-small-en English Inference Fine-tune a small-scale model but with competitive performance Represent this sentence for searching relevant passages:
BAAI/bge-large-zh Chinese Inference Fine-tune :trophy: rank 1st in C-MTEB benchmark 为这个句子生成表示以用于检索相关文章:
BAAI/bge-base-zh Chinese Inference Fine-tune a base-scale model but with similar ability to bge-large-zh 为这个句子生成表示以用于检索相关文章:
BAAI/bge-small-zh Chinese Inference Fine-tune a small-scale model but with competitive performance 为这个句子生成表示以用于检索相关文章:

Citation

If you find this repository useful, please consider giving a star :star: and citation

@misc{li2024makingtextembeddersfewshot,
      title={Making Text Embedders Few-Shot Learners}, 
      author={Chaofan Li and MingHao Qin and Shitao Xiao and Jianlyu Chen and Kun Luo and Yingxia Shao and Defu Lian and Zheng Liu},
      year={2024},
      eprint={2409.15700},
      archivePrefix={arXiv},
      primaryClass={cs.IR},
      url={https://arxiv.org/abs/2409.15700}, 
}
@misc{bge_embedding,
      title={C-Pack: Packaged Resources To Advance General Chinese Embedding}, 
      author={Shitao Xiao and Zheng Liu and Peitian Zhang and Niklas Muennighoff},
      year={2023},
      eprint={2309.07597},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

License

FlagEmbedding is licensed under the MIT License.