jinaai_jina-embeddings-v2-small-en

---

tags:

• sentence-transformers
• feature-extraction
• sentence-similarity
• mteb datasets:
• jinaai/negation-dataset language: en inference: false license: apache-2.0 model-index:
• name: jina-embedding-s-en-v2 results:
  • task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics:
    • type: accuracy value: 71.35820895522387
    • type: ap value: 33.99931933598115
    • type: f1 value: 65.3853685535555
  • task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics:
    • type: accuracy value: 82.90140000000001
    • type: ap value: 78.01434597815617
    • type: f1 value: 82.83357802722676
  • task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics:
    • type: accuracy value: 40.88999999999999
    • type: f1 value: 39.209432767163456
  • task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics:
    • type: map_at_1 value: 23.257
    • type: map_at_10 value: 37.946000000000005
    • type: map_at_100 value: 39.17
    • type: map_at_1000 value: 39.181
    • type: map_at_3 value: 32.99
    • type: map_at_5 value: 35.467999999999996
    • type: mrr_at_1 value: 23.541999999999998
    • type: mrr_at_10 value: 38.057
    • type: mrr_at_100 value: 39.289
    • type: mrr_at_1000 value: 39.299
    • type: mrr_at_3 value: 33.096
    • type: mrr_at_5 value: 35.628
    • type: ndcg_at_1 value: 23.257
    • type: ndcg_at_10 value: 46.729
    • type: ndcg_at_100 value: 51.900999999999996
    • type: ndcg_at_1000 value: 52.16
    • type: ndcg_at_3 value: 36.323
    • type: ndcg_at_5 value: 40.766999999999996
    • type: precision_at_1 value: 23.257
    • type: precision_at_10 value: 7.510999999999999
    • type: precision_at_100 value: 0.976
    • type: precision_at_1000 value: 0.1
    • type: precision_at_3 value: 15.339
    • type: precision_at_5 value: 11.350999999999999
    • type: recall_at_1 value: 23.257
    • type: recall_at_10 value: 75.107
    • type: recall_at_100 value: 97.58200000000001
    • type: recall_at_1000 value: 99.57300000000001
    • type: recall_at_3 value: 46.017
    • type: recall_at_5 value: 56.757000000000005
  • task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics:
    • type: v_measure value: 44.02420878391967
  • task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics:
    • type: v_measure value: 35.16136856000258
  • task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics:
    • type: map value: 59.61809790513646
    • type: mrr value: 73.07215406938397
  • task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics:
    • type: cos_sim_pearson value: 82.0167350090749
    • type: cos_sim_spearman value: 80.51569002630401
    • type: euclidean_pearson value: 81.46820525099726
    • type: euclidean_spearman value: 80.51569002630401
    • type: manhattan_pearson value: 81.35596555056757
    • type: manhattan_spearman value: 80.12592210903303
  • task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics:
    • type: accuracy value: 78.25
    • type: f1 value: 77.34950913540605
  • task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics:
    • type: v_measure value: 35.57238596005698
  • task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics:
    • type: v_measure value: 29.066444306196683
  • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics:
    • type: map_at_1 value: 31.891000000000002
    • type: map_at_10 value: 42.772
    • type: map_at_100 value: 44.108999999999995
    • type: map_at_1000 value: 44.236
    • type: map_at_3 value: 39.289
    • type: map_at_5 value: 41.113
    • type: mrr_at_1 value: 39.342
    • type: mrr_at_10 value: 48.852000000000004
    • type: mrr_at_100 value: 49.534
    • type: mrr_at_1000 value: 49.582
    • type: mrr_at_3 value: 46.089999999999996
    • type: mrr_at_5 value: 47.685
    • type: ndcg_at_1 value: 39.342
    • type: ndcg_at_10 value: 48.988
    • type: ndcg_at_100 value: 53.854
    • type: ndcg_at_1000 value: 55.955
    • type: ndcg_at_3 value: 43.877
    • type: ndcg_at_5 value: 46.027
    • type: precision_at_1 value: 39.342
    • type: precision_at_10 value: 9.285
    • type: precision_at_100 value: 1.488
    • type: precision_at_1000 value: 0.194
    • type: precision_at_3 value: 20.696
    • type: precision_at_5 value: 14.878
    • type: recall_at_1 value: 31.891000000000002
    • type: recall_at_10 value: 60.608
    • type: recall_at_100 value: 81.025
    • type: recall_at_1000 value: 94.883
    • type: recall_at_3 value: 45.694
    • type: recall_at_5 value: 51.684
  • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics:
    • type: map_at_1 value: 28.778
    • type: map_at_10 value: 37.632
    • type: map_at_100 value: 38.800000000000004
    • type: map_at_1000 value: 38.934999999999995
    • type: map_at_3 value: 35.293
    • type: map_at_5 value: 36.547000000000004
    • type: mrr_at_1 value: 35.35
    • type: mrr_at_10 value: 42.936
    • type: mrr_at_100 value: 43.69
    • type: mrr_at_1000 value: 43.739
    • type: mrr_at_3 value: 41.062
    • type: mrr_at_5 value: 42.097
    • type: ndcg_at_1 value: 35.35
    • type: ndcg_at_10 value: 42.528
    • type: ndcg_at_100 value: 46.983000000000004
    • type: ndcg_at_1000 value: 49.187999999999995
    • type: ndcg_at_3 value: 39.271
    • type: ndcg_at_5 value: 40.654
    • type: precision_at_1 value: 35.35
    • type: precision_at_10 value: 7.828
    • type: precision_at_100 value: 1.3010000000000002
    • type: precision_at_1000 value: 0.17700000000000002
    • type: precision_at_3 value: 18.96
    • type: precision_at_5 value: 13.120999999999999
    • type: recall_at_1 value: 28.778
    • type: recall_at_10 value: 50.775000000000006
    • type: recall_at_100 value: 69.66799999999999
    • type: recall_at_1000 value: 83.638
    • type: recall_at_3 value: 40.757
    • type: recall_at_5 value: 44.86
  • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics:
    • type: map_at_1 value: 37.584
    • type: map_at_10 value: 49.69
    • type: map_at_100 value: 50.639
    • type: map_at_1000 value: 50.702999999999996
    • type: map_at_3 value: 46.61
    • type: map_at_5 value: 48.486000000000004
    • type: mrr_at_1 value: 43.009
    • type: mrr_at_10 value: 52.949999999999996
    • type: mrr_at_100 value: 53.618
    • type: mrr_at_1000 value: 53.65299999999999
    • type: mrr_at_3 value: 50.605999999999995
    • type: mrr_at_5 value: 52.095
    • type: ndcg_at_1 value: 43.009
    • type: ndcg_at_10 value: 55.278000000000006
    • type: ndcg_at_100 value: 59.134
    • type: ndcg_at_1000 value: 60.528999999999996
    • type: ndcg_at_3 value: 50.184
    • type: ndcg_at_5 value: 52.919000000000004
    • type: precision_at_1 value: 43.009
    • type: precision_at_10 value: 8.821
    • type: precision_at_100 value: 1.161
    • type: precision_at_1000 value: 0.133
    • type: precision_at_3 value: 22.424
    • type: precision_at_5 value: 15.436
    • type: recall_at_1 value: 37.584
    • type: recall_at_10 value: 68.514
    • type: recall_at_100 value: 85.099
    • type: recall_at_1000 value: 95.123
    • type: recall_at_3 value: 55.007
    • type: recall_at_5 value: 61.714999999999996
  • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics:
    • type: map_at_1 value: 24.7
    • type: map_at_10 value: 32.804
    • type: map_at_100 value: 33.738
    • type: map_at_1000 value: 33.825
    • type: map_at_3 value: 30.639
    • type: map_at_5 value: 31.781
    • type: mrr_at_1 value: 26.328000000000003
    • type: mrr_at_10 value: 34.679
    • type: mrr_at_100 value: 35.510000000000005
    • type: mrr_at_1000 value: 35.577999999999996
    • type: mrr_at_3 value: 32.58
    • type: mrr_at_5 value: 33.687
    • type: ndcg_at_1 value: 26.328000000000003
    • type: ndcg_at_10 value: 37.313
    • type: ndcg_at_100 value: 42.004000000000005
    • type: ndcg_at_1000 value: 44.232
    • type: ndcg_at_3 value: 33.076
    • type: ndcg_at_5 value: 34.966
    • type: precision_at_1 value: 26.328000000000003
    • type: precision_at_10 value: 5.627
    • type: precision_at_100 value: 0.8410000000000001
    • type: precision_at_1000 value: 0.106
    • type: precision_at_3 value: 14.011000000000001
    • type: precision_at_5 value: 9.582
    • type: recall_at_1 value: 24.7
    • type: recall_at_10 value: 49.324
    • type: recall_at_100 value: 71.018
    • type: recall_at_1000 value: 87.905
    • type: recall_at_3 value: 37.7
    • type: recall_at_5 value: 42.281
  • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics:
    • type: map_at_1 value: 14.350999999999999
    • type: map_at_10 value: 21.745
    • type: map_at_100 value: 22.731
    • type: map_at_1000 value: 22.852
    • type: map_at_3 value: 19.245
    • type: map_at_5 value: 20.788
    • type: mrr_at_1 value: 18.159
    • type: mrr_at_10 value: 25.833000000000002
    • type: mrr_at_100 value: 26.728
    • type: mrr_at_1000 value: 26.802
    • type: mrr_at_3 value: 23.383000000000003
    • type: mrr_at_5 value: 24.887999999999998
    • type: ndcg_at_1 value: 18.159
    • type: ndcg_at_10 value: 26.518000000000004
    • type: ndcg_at_100 value: 31.473000000000003
    • type: ndcg_at_1000 value: 34.576
    • type: ndcg_at_3 value: 21.907
    • type: ndcg_at_5 value: 24.39
    • type: precision_at_1 value: 18.159
    • type: precision_at_10 value: 4.938
    • type: precision_at_100 value: 0.853
    • type: precision_at_1000 value: 0.125
    • type: precision_at_3 value: 10.655000000000001
    • type: precision_at_5 value: 7.985
    • type: recall_at_1 value: 14.350999999999999
    • type: recall_at_10 value: 37.284
    • type: recall_at_100 value: 59.11300000000001
    • type: recall_at_1000 value: 81.634
    • type: recall_at_3 value: 24.753
    • type: recall_at_5 value: 30.979
  • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics:
    • type: map_at_1 value: 26.978
    • type: map_at_10 value: 36.276
    • type: map_at_100 value: 37.547000000000004
    • type: map_at_1000 value: 37.678
    • type: map_at_3 value: 33.674
    • type: map_at_5 value: 35.119
    • type: mrr_at_1 value: 32.916000000000004
    • type: mrr_at_10 value: 41.798
    • type: mrr_at_100 value: 42.72
    • type: mrr_at_1000 value: 42.778
    • type: mrr_at_3 value: 39.493
    • type: mrr_at_5 value: 40.927
    • type: ndcg_at_1 value: 32.916000000000004
    • type: ndcg_at_10 value: 41.81
    • type: ndcg_at_100 value: 47.284
    • type: ndcg_at_1000 value: 49.702
    • type: ndcg_at_3 value: 37.486999999999995
    • type: ndcg_at_5 value: 39.597
    • type: precision_at_1 value: 32.916000000000004
    • type: precision_at_10 value: 7.411
    • type: precision_at_100 value: 1.189
    • type: precision_at_1000 value: 0.158
    • type: precision_at_3 value: 17.581
    • type: precision_at_5 value: 12.397
    • type: recall_at_1 value: 26.978
    • type: recall_at_10 value: 52.869
    • type: recall_at_100 value: 75.78399999999999
    • type: recall_at_1000 value: 91.545
    • type: recall_at_3 value: 40.717
    • type: recall_at_5 value: 46.168
  • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics:
    • type: map_at_1 value: 24.641
    • type: map_at_10 value: 32.916000000000004
    • type: map_at_100 value: 34.165
    • type: map_at_1000 value: 34.286
    • type: map_at_3 value: 30.335
    • type: map_at_5 value: 31.569000000000003
    • type: mrr_at_1 value: 30.593999999999998
    • type: mrr_at_10 value: 38.448
    • type: mrr_at_100 value: 39.299
    • type: mrr_at_1000 value: 39.362
    • type: mrr_at_3 value: 36.244
    • type: mrr_at_5 value: 37.232
    • type: ndcg_at_1 value: 30.593999999999998
    • type: ndcg_at_10 value: 38.2
    • type: ndcg_at_100 value: 43.742
    • type: ndcg_at_1000 value: 46.217000000000006
    • type: ndcg_at_3 value: 33.925
    • type: ndcg_at_5 value: 35.394
    • type: precision_at_1 value: 30.593999999999998
    • type: precision_at_10 value: 6.895
    • type: precision_at_100 value: 1.1320000000000001
    • type: precision_at_1000 value: 0.153
    • type: precision_at_3 value: 16.096
    • type: precision_at_5 value: 11.05
    • type: recall_at_1 value: 24.641
    • type: recall_at_10 value: 48.588
    • type: recall_at_100 value: 72.841
    • type: recall_at_1000 value: 89.535
    • type: recall_at_3 value: 36.087
    • type: recall_at_5 value: 40.346
  • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics:
    • type: map_at_1 value: 24.79425
    • type: map_at_10 value: 33.12033333333333
    • type: map_at_100 value: 34.221333333333334
    • type: map_at_1000 value: 34.3435
    • type: map_at_3 value: 30.636583333333338
    • type: map_at_5 value: 31.974083333333326
    • type: mrr_at_1 value: 29.242416666666664
    • type: mrr_at_10 value: 37.11675
    • type: mrr_at_100 value: 37.93783333333334
    • type: mrr_at_1000 value: 38.003083333333336
    • type: mrr_at_3 value: 34.904666666666664
    • type: mrr_at_5 value: 36.12916666666667
    • type: ndcg_at_1 value: 29.242416666666664
    • type: ndcg_at_10 value: 38.03416666666667
    • type: ndcg_at_100 value: 42.86674999999999
    • type: ndcg_at_1000 value: 45.34550000000001
    • type: ndcg_at_3 value: 33.76466666666666
    • type: ndcg_at_5 value: 35.668666666666674
    • type: precision_at_1 value: 29.242416666666664
    • type: precision_at_10 value: 6.589833333333334
    • type: precision_at_100 value: 1.0693333333333332
    • type: precision_at_1000 value: 0.14641666666666667
    • type: precision_at_3 value: 15.430749999999998
    • type: precision_at_5 value: 10.833833333333333
    • type: recall_at_1 value: 24.79425
    • type: recall_at_10 value: 48.582916666666655
    • type: recall_at_100 value: 69.88499999999999
    • type: recall_at_1000 value: 87.211
    • type: recall_at_3 value: 36.625499999999995
    • type: recall_at_5 value: 41.553999999999995
  • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics:
    • type: map_at_1 value: 22.767
    • type: map_at_10 value: 28.450999999999997
    • type: map_at_100 value: 29.332
    • type: map_at_1000 value: 29.426000000000002
    • type: map_at_3 value: 26.379
    • type: map_at_5 value: 27.584999999999997
    • type: mrr_at_1 value: 25.46
    • type: mrr_at_10 value: 30.974
    • type: mrr_at_100 value: 31.784000000000002
    • type: mrr_at_1000 value: 31.857999999999997
    • type: mrr_at_3 value: 28.962
    • type: mrr_at_5 value: 30.066
    • type: ndcg_at_1 value: 25.46
    • type: ndcg_at_10 value: 32.041
    • type: ndcg_at_100 value: 36.522
    • type: ndcg_at_1000 value: 39.101
    • type: ndcg_at_3 value: 28.152
    • type: ndcg_at_5 value: 30.03
    • type: precision_at_1 value: 25.46
    • type: precision_at_10 value: 4.893
    • type: precision_at_100 value: 0.77
    • type: precision_at_1000 value: 0.107
    • type: precision_at_3 value: 11.605
    • type: precision_at_5 value: 8.19
    • type: recall_at_1 value: 22.767
    • type: recall_at_10 value: 40.71
    • type: recall_at_100 value: 61.334999999999994
    • type: recall_at_1000 value: 80.567
    • type: recall_at_3 value: 30.198000000000004
    • type: recall_at_5 value: 34.803
  • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics:
    • type: map_at_1 value: 16.722
    • type: map_at_10 value: 22.794
    • type: map_at_100 value: 23.7
    • type: map_at_1000 value: 23.822
    • type: map_at_3 value: 20.781
    • type: map_at_5 value: 22.024
    • type: mrr_at_1 value: 20.061999999999998
    • type: mrr_at_10 value: 26.346999999999998
    • type: mrr_at_100 value: 27.153
    • type: mrr_at_1000 value: 27.233
    • type: mrr_at_3 value: 24.375
    • type: mrr_at_5 value: 25.593
    • type: ndcg_at_1 value: 20.061999999999998
    • type: ndcg_at_10 value: 26.785999999999998
    • type: ndcg_at_100 value: 31.319999999999997
    • type: ndcg_at_1000 value: 34.346
    • type: ndcg_at_3 value: 23.219
    • type: ndcg_at_5 value: 25.107000000000003
    • type: precision_at_1 value: 20.061999999999998
    • type: precision_at_10 value: 4.78
    • type: precision_at_100 value: 0.83
    • type: precision_at_1000 value: 0.125
    • type: precision_at_3 value: 10.874
    • type: precision_at_5 value: 7.956
    • type: recall_at_1 value: 16.722
    • type: recall_at_10 value: 35.204
    • type: recall_at_100 value: 55.797
    • type: recall_at_1000 value: 77.689
    • type: recall_at_3 value: 25.245
    • type: recall_at_5 value: 30.115
  • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics:
    • type: map_at_1 value: 24.842
    • type: map_at_10 value: 32.917
    • type: map_at_100 value: 33.961000000000006
    • type: map_at_1000 value: 34.069
    • type: map_at_3 value: 30.595
    • type: map_at_5 value: 31.837
    • type: mrr_at_1 value: 29.011
    • type: mrr_at_10 value: 36.977
    • type: mrr_at_100 value: 37.814
    • type: mrr_at_1000 value: 37.885999999999996
    • type: mrr_at_3 value: 34.966
    • type: mrr_at_5 value: 36.043
    • type: ndcg_at_1 value: 29.011
    • type: ndcg_at_10 value: 37.735
    • type: ndcg_at_100 value: 42.683
    • type: ndcg_at_1000 value: 45.198
    • type: ndcg_at_3 value: 33.650000000000006
    • type: ndcg_at_5 value: 35.386
    • type: precision_at_1 value: 29.011
    • type: precision_at_10 value: 6.259
    • type: precision_at_100 value: 0.984
    • type: precision_at_1000 value: 0.13
    • type: precision_at_3 value: 15.329999999999998
    • type: precision_at_5 value: 10.541
    • type: recall_at_1 value: 24.842
    • type: recall_at_10 value: 48.304
    • type: recall_at_100 value: 70.04899999999999
    • type: recall_at_1000 value: 87.82600000000001
    • type: recall_at_3 value: 36.922
    • type: recall_at_5 value: 41.449999999999996
  • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics:
    • type: map_at_1 value: 24.252000000000002
    • type: map_at_10 value: 32.293
    • type: map_at_100 value: 33.816
    • type: map_at_1000 value: 34.053
    • type: map_at_3 value: 29.781999999999996
    • type: map_at_5 value: 31.008000000000003
    • type: mrr_at_1 value: 29.051
    • type: mrr_at_10 value: 36.722
    • type: mrr_at_100 value: 37.663000000000004
    • type: mrr_at_1000 value: 37.734
    • type: mrr_at_3 value: 34.354
    • type: mrr_at_5 value: 35.609
    • type: ndcg_at_1 value: 29.051
    • type: ndcg_at_10 value: 37.775999999999996
    • type: ndcg_at_100 value: 43.221
    • type: ndcg_at_1000 value: 46.116
    • type: ndcg_at_3 value: 33.403
    • type: ndcg_at_5 value: 35.118
    • type: precision_at_1 value: 29.051
    • type: precision_at_10 value: 7.332
    • type: precision_at_100 value: 1.49
    • type: precision_at_1000 value: 0.23600000000000002
    • type: precision_at_3 value: 15.415000000000001
    • type: precision_at_5 value: 11.107
    • type: recall_at_1 value: 24.252000000000002
    • type: recall_at_10 value: 47.861
    • type: recall_at_100 value: 72.21600000000001
    • type: recall_at_1000 value: 90.886
    • type: recall_at_3 value: 35.533
    • type: recall_at_5 value: 39.959
  • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics:
    • type: map_at_1 value: 20.025000000000002
    • type: map_at_10 value: 27.154
    • type: map_at_100 value: 28.118
    • type: map_at_1000 value: 28.237000000000002
    • type: map_at_3 value: 25.017
    • type: map_at_5 value: 25.832
    • type: mrr_at_1 value: 21.627
    • type: mrr_at_10 value: 28.884999999999998
    • type: mrr_at_100 value: 29.741
    • type: mrr_at_1000 value: 29.831999999999997
    • type: mrr_at_3 value: 26.741
    • type: mrr_at_5 value: 27.628000000000004
    • type: ndcg_at_1 value: 21.627
    • type: ndcg_at_10 value: 31.436999999999998
    • type: ndcg_at_100 value: 36.181000000000004
    • type: ndcg_at_1000 value: 38.986
    • type: ndcg_at_3 value: 27.025
    • type: ndcg_at_5 value: 28.436
    • type: precision_at_1 value: 21.627
    • type: precision_at_10 value: 5.009
    • type: precision_at_100 value: 0.7929999999999999
    • type: precision_at_1000 value: 0.11299999999999999
    • type: precision_at_3 value: 11.522
    • type: precision_at_5 value: 7.763000000000001
    • type: recall_at_1 value: 20.025000000000002
    • type: recall_at_10 value: 42.954
    • type: recall_at_100 value: 64.67500000000001
    • type: recall_at_1000 value: 85.301
    • type: recall_at_3 value: 30.892999999999997
    • type: recall_at_5 value: 34.288000000000004
  • task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics:
    • type: map_at_1 value: 10.079
    • type: map_at_10 value: 16.930999999999997
    • type: map_at_100 value: 18.398999999999997
    • type: map_at_1000 value: 18.561
    • type: map_at_3 value: 14.294
    • type: map_at_5 value: 15.579
    • type: mrr_at_1 value: 22.606
    • type: mrr_at_10 value: 32.513
    • type: mrr_at_100 value: 33.463
    • type: mrr_at_1000 value: 33.513999999999996
    • type: mrr_at_3 value: 29.479
    • type: mrr_at_5 value: 31.3
    • type: ndcg_at_1 value: 22.606
    • type: ndcg_at_10 value: 24.053
    • type: ndcg_at_100 value: 30.258000000000003
    • type: ndcg_at_1000 value: 33.516
    • type: ndcg_at_3 value: 19.721
    • type: ndcg_at_5 value: 21.144
    • type: precision_at_1 value: 22.606
    • type: precision_at_10 value: 7.55
    • type: precision_at_100 value: 1.399
    • type: precision_at_1000 value: 0.2
    • type: precision_at_3 value: 14.701
    • type: precision_at_5 value: 11.192
    • type: recall_at_1 value: 10.079
    • type: recall_at_10 value: 28.970000000000002
    • type: recall_at_100 value: 50.805
    • type: recall_at_1000 value: 69.378
    • type: recall_at_3 value: 18.199
    • type: recall_at_5 value: 22.442
  • task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics:
    • type: map_at_1 value: 7.794
    • type: map_at_10 value: 15.165999999999999
    • type: map_at_100 value: 20.508000000000003
    • type: map_at_1000 value: 21.809
    • type: map_at_3 value: 11.568000000000001
    • type: map_at_5 value: 13.059000000000001
    • type: mrr_at_1 value: 56.49999999999999
    • type: mrr_at_10 value: 65.90899999999999
    • type: mrr_at_100 value: 66.352
    • type: mrr_at_1000 value: 66.369
    • type: mrr_at_3 value: 64.0
    • type: mrr_at_5 value: 65.10000000000001
    • type: ndcg_at_1 value: 44.25
    • type: ndcg_at_10 value: 32.649
    • type: ndcg_at_100 value: 36.668
    • type: ndcg_at_1000 value: 43.918
    • type: ndcg_at_3 value: 37.096000000000004
    • type: ndcg_at_5 value: 34.048
    • type: precision_at_1 value: 56.49999999999999
    • type: precision_at_10 value: 25.45
    • type: precision_at_100 value: 8.055
    • type: precision_at_1000 value: 1.7489999999999999
    • type: precision_at_3 value: 41.0
    • type: precision_at_5 value: 32.85
    • type: recall_at_1 value: 7.794
    • type: recall_at_10 value: 20.101
    • type: recall_at_100 value: 42.448
    • type: recall_at_1000 value: 65.88000000000001
    • type: recall_at_3 value: 12.753
    • type: recall_at_5 value: 15.307
  • task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics:
    • type: accuracy value: 44.01
    • type: f1 value: 38.659680951114964
  • task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics:
    • type: map_at_1 value: 49.713
    • type: map_at_10 value: 61.79
    • type: map_at_100 value: 62.28
    • type: map_at_1000 value: 62.297000000000004
    • type: map_at_3 value: 59.361
    • type: map_at_5 value: 60.92100000000001
    • type: mrr_at_1 value: 53.405
    • type: mrr_at_10 value: 65.79899999999999
    • type: mrr_at_100 value: 66.219
    • type: mrr_at_1000 value: 66.227
    • type: mrr_at_3 value: 63.431000000000004
    • type: mrr_at_5 value: 64.98
    • type: ndcg_at_1 value: 53.405
    • type: ndcg_at_10 value: 68.01899999999999
    • type: ndcg_at_100 value: 70.197
    • type: ndcg_at_1000 value: 70.571
    • type: ndcg_at_3 value: 63.352
    • type: ndcg_at_5 value: 66.018
    • type: precision_at_1 value: 53.405
    • type: precision_at_10 value: 9.119
    • type: precision_at_100 value: 1.03
    • type: precision_at_1000 value: 0.107
    • type: precision_at_3 value: 25.602999999999998
    • type: precision_at_5 value: 16.835
    • type: recall_at_1 value: 49.713
    • type: recall_at_10 value: 83.306
    • type: recall_at_100 value: 92.92
    • type: recall_at_1000 value: 95.577
    • type: recall_at_3 value: 70.798
    • type: recall_at_5 value: 77.254
  • task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics:
    • type: map_at_1 value: 15.310000000000002
    • type: map_at_10 value: 26.204
    • type: map_at_100 value: 27.932000000000002
    • type: map_at_1000 value: 28.121000000000002
    • type: map_at_3 value: 22.481
    • type: map_at_5 value: 24.678
    • type: mrr_at_1 value: 29.784
    • type: mrr_at_10 value: 39.582
    • type: mrr_at_100 value: 40.52
    • type: mrr_at_1000 value: 40.568
    • type: mrr_at_3 value: 37.114000000000004
    • type: mrr_at_5 value: 38.596000000000004
    • type: ndcg_at_1 value: 29.784
    • type: ndcg_at_10 value: 33.432
    • type: ndcg_at_100 value: 40.281
    • type: ndcg_at_1000 value: 43.653999999999996
    • type: ndcg_at_3 value: 29.612
    • type: ndcg_at_5 value: 31.223
    • type: precision_at_1 value: 29.784
    • type: precision_at_10 value: 9.645
    • type: precision_at_100 value: 1.645
    • type: precision_at_1000 value: 0.22499999999999998
    • type: precision_at_3 value: 20.165
    • type: precision_at_5 value: 15.401000000000002
    • type: recall_at_1 value: 15.310000000000002
    • type: recall_at_10 value: 40.499
    • type: recall_at_100 value: 66.643
    • type: recall_at_1000 value: 87.059
    • type: recall_at_3 value: 27.492
    • type: recall_at_5 value: 33.748
  • task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics:
    • type: map_at_1 value: 33.599000000000004
    • type: map_at_10 value: 47.347
    • type: map_at_100 value: 48.191
    • type: map_at_1000 value: 48.263
    • type: map_at_3 value: 44.698
    • type: map_at_5 value: 46.278999999999996
    • type: mrr_at_1 value: 67.19800000000001
    • type: mrr_at_10 value: 74.054
    • type: mrr_at_100 value: 74.376
    • type: mrr_at_1000 value: 74.392
    • type: mrr_at_3 value: 72.849
    • type: mrr_at_5 value: 73.643
    • type: ndcg_at_1 value: 67.19800000000001
    • type: ndcg_at_10 value: 56.482
    • type: ndcg_at_100 value: 59.694
    • type: ndcg_at_1000 value: 61.204
    • type: ndcg_at_3 value: 52.43299999999999
    • type: ndcg_at_5 value: 54.608000000000004
    • type: precision_at_1 value: 67.19800000000001
    • type: precision_at_10 value: 11.613999999999999
    • type: precision_at_100 value: 1.415
    • type: precision_at_1000 value: 0.16199999999999998
    • type: precision_at_3 value: 32.726
    • type: precision_at_5 value: 21.349999999999998
    • type: recall_at_1 value: 33.599000000000004
    • type: recall_at_10 value: 58.069
    • type: recall_at_100 value: 70.736
    • type: recall_at_1000 value: 80.804
    • type: recall_at_3 value: 49.088
    • type: recall_at_5 value: 53.376000000000005
  • task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics:
    • type: accuracy value: 73.64359999999999
    • type: ap value: 67.54685976014599
    • type: f1 value: 73.55148707559482
  • task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics:
    • type: map_at_1 value: 19.502
    • type: map_at_10 value: 30.816
    • type: map_at_100 value: 32.007999999999996
    • type: map_at_1000 value: 32.067
    • type: map_at_3 value: 27.215
    • type: map_at_5 value: 29.304000000000002
    • type: mrr_at_1 value: 20.072000000000003
    • type: mrr_at_10 value: 31.406
    • type: mrr_at_100 value: 32.549
    • type: mrr_at_1000 value: 32.602
    • type: mrr_at_3 value: 27.839000000000002
    • type: mrr_at_5 value: 29.926000000000002
    • type: ndcg_at_1 value: 20.086000000000002
    • type: ndcg_at_10 value: 37.282
    • type: ndcg_at_100 value: 43.206
    • type: ndcg_at_1000 value: 44.690000000000005
    • type: ndcg_at_3 value: 29.932
    • type: ndcg_at_5 value: 33.668
    • type: precision_at_1 value: 20.086000000000002
    • type: precision_at_10 value: 5.961
    • type: precision_at_100 value: 0.898
    • type: precision_at_1000 value: 0.10200000000000001
    • type: precision_at_3 value: 12.856000000000002
    • type: precision_at_5 value: 9.596
    • type: recall_at_1 value: 19.502
    • type: recall_at_10 value: 57.182
    • type: recall_at_100 value: 84.952
    • type: recall_at_1000 value: 96.34700000000001
    • type: recall_at_3 value: 37.193
    • type: recall_at_5 value: 46.157
  • task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics:
    • type: accuracy value: 93.96488828089375
    • type: f1 value: 93.32119260543482
  • task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics:
    • type: accuracy value: 72.4965800273598
    • type: f1 value: 49.34896217536082
  • task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics:
    • type: accuracy value: 67.60928043039678
    • type: f1 value: 64.34244712074538
  • task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics:
    • type: accuracy value: 69.75453934095493
    • type: f1 value: 68.39224867489249
  • task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics:
    • type: v_measure value: 31.862573504920082
  • task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics:
    • type: v_measure value: 27.511123551196803
  • task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics:
    • type: map value: 30.99145104942086
    • type: mrr value: 32.03606480418627
  • task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics:
    • type: map_at_1 value: 5.015
    • type: map_at_10 value: 11.054
    • type: map_at_100 value: 13.773
    • type: map_at_1000 value: 15.082999999999998
    • type: map_at_3 value: 8.253
    • type: map_at_5 value: 9.508999999999999
    • type: mrr_at_1 value: 42.105
    • type: mrr_at_10 value: 50.44499999999999
    • type: mrr_at_100 value: 51.080000000000005
    • type: mrr_at_1000 value: 51.129999999999995
    • type: mrr_at_3 value: 48.555
    • type: mrr_at_5 value: 49.84
    • type: ndcg_at_1 value: 40.402
    • type: ndcg_at_10 value: 30.403000000000002
    • type: ndcg_at_100 value: 28.216
    • type: ndcg_at_1000 value: 37.021
    • type: ndcg_at_3 value: 35.53
    • type: ndcg_at_5 value: 33.202999999999996
    • type: precision_at_1 value: 42.105
    • type: precision_at_10 value: 22.353
    • type: precision_at_100 value: 7.266
    • type: precision_at_1000 value: 2.011
    • type: precision_at_3 value: 32.921
    • type: precision_at_5 value: 28.297
    • type: recall_at_1 value: 5.015
    • type: recall_at_10 value: 14.393
    • type: recall_at_100 value: 28.893
    • type: recall_at_1000 value: 60.18
    • type: recall_at_3 value: 9.184000000000001
    • type: recall_at_5 value: 11.39
  • task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics:
    • type: map_at_1 value: 29.524
    • type: map_at_10 value: 44.182
    • type: map_at_100 value: 45.228
    • type: map_at_1000 value: 45.265
    • type: map_at_3 value: 39.978
    • type: map_at_5 value: 42.482
    • type: mrr_at_1 value: 33.256
    • type: mrr_at_10 value: 46.661
    • type: mrr_at_100 value: 47.47
    • type: mrr_at_1000 value: 47.496
    • type: mrr_at_3 value: 43.187999999999995
    • type: mrr_at_5 value: 45.330999999999996
    • type: ndcg_at_1 value: 33.227000000000004
    • type: ndcg_at_10 value: 51.589
    • type: ndcg_at_100 value: 56.043
    • type: ndcg_at_1000 value: 56.937000000000005
    • type: ndcg_at_3 value: 43.751
    • type: ndcg_at_5 value: 47.937000000000005
    • type: precision_at_1 value: 33.227000000000004
    • type: precision_at_10 value: 8.556999999999999
    • type: precision_at_100 value: 1.103
    • type: precision_at_1000 value: 0.11900000000000001
    • type: precision_at_3 value: 19.921
    • type: precision_at_5 value: 14.396999999999998
    • type: recall_at_1 value: 29.524
    • type: recall_at_10 value: 71.615
    • type: recall_at_100 value: 91.056
    • type: recall_at_1000 value: 97.72800000000001
    • type: recall_at_3 value: 51.451
    • type: recall_at_5 value: 61.119
  • task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics:
    • type: map_at_1 value: 69.596
    • type: map_at_10 value: 83.281
    • type: map_at_100 value: 83.952
    • type: map_at_1000 value: 83.97200000000001
    • type: map_at_3 value: 80.315
    • type: map_at_5 value: 82.223
    • type: mrr_at_1 value: 80.17
    • type: mrr_at_10 value: 86.522
    • type: mrr_at_100 value: 86.644
    • type: mrr_at_1000 value: 86.64500000000001
    • type: mrr_at_3 value: 85.438
    • type: mrr_at_5 value: 86.21799999999999
    • type: ndcg_at_1 value: 80.19
    • type: ndcg_at_10 value: 87.19
    • type: ndcg_at_100 value: 88.567
    • type: ndcg_at_1000 value: 88.70400000000001
    • type: ndcg_at_3 value: 84.17999999999999
    • type: ndcg_at_5 value: 85.931
    • type: precision_at_1 value: 80.19
    • type: precision_at_10 value: 13.209000000000001
    • type: precision_at_100 value: 1.518
    • type: precision_at_1000 value: 0.157
    • type: precision_at_3 value: 36.717
    • type: precision_at_5 value: 24.248
    • type: recall_at_1 value: 69.596
    • type: recall_at_10 value: 94.533
    • type: recall_at_100 value: 99.322
    • type: recall_at_1000 value: 99.965
    • type: recall_at_3 value: 85.911
    • type: recall_at_5 value: 90.809
  • task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics:
    • type: v_measure value: 49.27650627571912
  • task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics:
    • type: v_measure value: 57.08550946534183
  • task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics:
    • type: map_at_1 value: 4.568
    • type: map_at_10 value: 10.862
    • type: map_at_100 value: 12.757
    • type: map_at_1000 value: 13.031
    • type: map_at_3 value: 7.960000000000001
    • type: map_at_5 value: 9.337
    • type: mrr_at_1 value: 22.5
    • type: mrr_at_10 value: 32.6
    • type: mrr_at_100 value: 33.603
    • type: mrr_at_1000 value: 33.672000000000004
    • type: mrr_at_3 value: 29.299999999999997
    • type: mrr_at_5 value: 31.25
    • type: ndcg_at_1 value: 22.5
    • type: ndcg_at_10 value: 18.605
    • type: ndcg_at_100 value: 26.029999999999998
    • type: ndcg_at_1000 value: 31.256
    • type: ndcg_at_3 value: 17.873
    • type: ndcg_at_5 value: 15.511
    • type: precision_at_1 value: 22.5
    • type: precision_at_10 value: 9.58
    • type: precision_at_100 value: 2.033
    • type: precision_at_1000 value: 0.33
    • type: precision_at_3 value: 16.633
    • type: precision_at_5 value: 13.54
    • type: recall_at_1 value: 4.568
    • type: recall_at_10 value: 19.402
    • type: recall_at_100 value: 41.277
    • type: recall_at_1000 value: 66.963
    • type: recall_at_3 value: 10.112
    • type: recall_at_5 value: 13.712
  • task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics:
    • type: cos_sim_pearson value: 83.31992291680787
    • type: cos_sim_spearman value: 76.7212346922664
    • type: euclidean_pearson value: 80.42189271706478
    • type: euclidean_spearman value: 76.7212342532493
    • type: manhattan_pearson value: 80.33171093031578
    • type: manhattan_spearman value: 76.63192883074694
  • task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics:
    • type: cos_sim_pearson value: 83.16654278886763
    • type: cos_sim_spearman value: 73.66390263429565
    • type: euclidean_pearson value: 79.7485360086639
    • type: euclidean_spearman value: 73.66389870373436
    • type: manhattan_pearson value: 79.73652237443706
    • type: manhattan_spearman value: 73.65296117151647
  • task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics:
    • type: cos_sim_pearson value: 82.40389689929246
    • type: cos_sim_spearman value: 83.29727595993955
    • type: euclidean_pearson value: 82.23970587854079
    • type: euclidean_spearman value: 83.29727595993955
    • type: manhattan_pearson value: 82.18823600831897
    • type: manhattan_spearman value: 83.20746192209594
  • task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics:
    • type: cos_sim_pearson value: 81.73505246913413
    • type: cos_sim_spearman value: 79.1686548248754
    • type: euclidean_pearson value: 80.48889135993412
    • type: euclidean_spearman value: 79.16864112930354
    • type: manhattan_pearson value: 80.40720651057302
    • type: manhattan_spearman value: 79.0640155089286
  • task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics:
    • type: cos_sim_pearson value: 86.3953512879065
    • type: cos_sim_spearman value: 87.29947322714338
    • type: euclidean_pearson value: 86.59759438529645
    • type: euclidean_spearman value: 87.29947511092824
    • type: manhattan_pearson value: 86.52097806169155
    • type: manhattan_spearman value: 87.22987242146534
  • task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics:
    • type: cos_sim_pearson value: 82.48565753792056
    • type: cos_sim_spearman value: 83.6049720319893
    • type: euclidean_pearson value: 82.56452023172913
    • type: euclidean_spearman value: 83.60490168191697
    • type: manhattan_pearson value: 82.58079941137872
    • type: manhattan_spearman value: 83.60975807374051
  • 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: 88.18239976618212
    • type: cos_sim_spearman value: 88.23061724730616
    • type: euclidean_pearson value: 87.78482472776658
    • type: euclidean_spearman value: 88.23061724730616
    • type: manhattan_pearson value: 87.75059641730239
    • type: manhattan_spearman value: 88.22527413524622
  • task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (en) config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics:
    • type: cos_sim_pearson value: 63.42816418706765
    • type: cos_sim_spearman value: 63.4569864520124
    • type: euclidean_pearson value: 64.35405409953853
    • type: euclidean_spearman value: 63.4569864520124
    • type: manhattan_pearson value: 63.96649236073056
    • type: manhattan_spearman value: 63.01448583722708
  • task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics:
    • type: cos_sim_pearson value: 83.41659638047614
    • type: cos_sim_spearman value: 84.03893866106175
    • type: euclidean_pearson value: 84.2251203953798
    • type: euclidean_spearman value: 84.03893866106175
    • type: manhattan_pearson value: 84.22733643205514
    • type: manhattan_spearman value: 84.06504411263612
  • task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics:
    • type: map value: 79.75608022582414
    • type: mrr value: 94.0947732369301
  • task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics:
    • type: map_at_1 value: 50.161
    • type: map_at_10 value: 59.458999999999996
    • type: map_at_100 value: 60.156
    • type: map_at_1000 value: 60.194
    • type: map_at_3 value: 56.45400000000001
    • type: map_at_5 value: 58.165
    • type: mrr_at_1 value: 53.333
    • type: mrr_at_10 value: 61.050000000000004
    • type: mrr_at_100 value: 61.586
    • type: mrr_at_1000 value: 61.624
    • type: mrr_at_3 value: 58.889
    • type: mrr_at_5 value: 60.122
    • type: ndcg_at_1 value: 53.333
    • type: ndcg_at_10 value: 63.888999999999996
    • type: ndcg_at_100 value: 66.963
    • type: ndcg_at_1000 value: 68.062
    • type: ndcg_at_3 value: 59.01
    • type: ndcg_at_5 value: 61.373999999999995
    • type: precision_at_1 value: 53.333
    • type: precision_at_10 value: 8.633000000000001
    • type: precision_at_100 value: 1.027
    • type: precision_at_1000 value: 0.11199999999999999
    • type: precision_at_3 value: 23.111
    • type: precision_at_5 value: 15.467
    • type: recall_at_1 value: 50.161
    • type: recall_at_10 value: 75.922
    • type: recall_at_100 value: 90.0
    • type: recall_at_1000 value: 98.667
    • type: recall_at_3 value: 62.90599999999999
    • type: recall_at_5 value: 68.828
  • task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics:
    • type: cos_sim_accuracy value: 99.81188118811882
    • type: cos_sim_ap value: 95.11619225962413
    • type: cos_sim_f1 value: 90.35840484603736
    • type: cos_sim_precision value: 91.23343527013252
    • type: cos_sim_recall value: 89.5
    • type: dot_accuracy value: 99.81188118811882
    • type: dot_ap value: 95.11619225962413
    • type: dot_f1 value: 90.35840484603736
    • type: dot_precision value: 91.23343527013252
    • type: dot_recall value: 89.5
    • type: euclidean_accuracy value: 99.81188118811882
    • type: euclidean_ap value: 95.11619225962413
    • type: euclidean_f1 value: 90.35840484603736
    • type: euclidean_precision value: 91.23343527013252
    • type: euclidean_recall value: 89.5
    • type: manhattan_accuracy value: 99.80891089108911
    • type: manhattan_ap value: 95.07294266220966
    • type: manhattan_f1 value: 90.21794221996959
    • type: manhattan_precision value: 91.46968139773895
    • type: manhattan_recall value: 89.0
    • type: max_accuracy value: 99.81188118811882
    • type: max_ap value: 95.11619225962413
    • type: max_f1 value: 90.35840484603736
  • task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics:
    • type: v_measure value: 55.3481874105239
  • task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics:
    • type: v_measure value: 34.421291695525
  • task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics:
    • type: map value: 49.98746633276634
    • type: mrr value: 50.63143249724133
  • task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics:
    • type: cos_sim_pearson value: 31.009961979844036
    • type: cos_sim_spearman value: 30.558416108881044
    • type: dot_pearson value: 31.009964941134253
    • type: dot_spearman value: 30.545760761761393
  • task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics:
    • type: map_at_1 value: 0.207
    • type: map_at_10 value: 1.6
    • type: map_at_100 value: 8.594
    • type: map_at_1000 value: 20.213
    • type: map_at_3 value: 0.585
    • type: map_at_5 value: 0.9039999999999999
    • type: mrr_at_1 value: 78.0
    • type: mrr_at_10 value: 87.4
    • type: mrr_at_100 value: 87.4
    • type: mrr_at_1000 value: 87.4
    • type: mrr_at_3 value: 86.667
    • type: mrr_at_5 value: 87.06700000000001
    • type: ndcg_at_1 value: 73.0
    • type: ndcg_at_10 value: 65.18
    • type: ndcg_at_100 value: 49.631
    • type: ndcg_at_1000 value: 43.498999999999995
    • type: ndcg_at_3 value: 71.83800000000001
    • type: ndcg_at_5 value: 69.271
    • type: precision_at_1 value: 78.0
    • type: precision_at_10 value: 69.19999999999999
    • type: precision_at_100 value: 50.980000000000004
    • type: precision_at_1000 value: 19.426
    • type: precision_at_3 value: 77.333
    • type: precision_at_5 value: 74.0
    • type: recall_at_1 value: 0.207
    • type: recall_at_10 value: 1.822
    • type: recall_at_100 value: 11.849
    • type: recall_at_1000 value: 40.492
    • type: recall_at_3 value: 0.622
    • type: recall_at_5 value: 0.9809999999999999
  • task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics:
    • type: map_at_1 value: 2.001
    • type: map_at_10 value: 10.376000000000001
    • type: map_at_100 value: 16.936999999999998
    • type: map_at_1000 value: 18.615000000000002
    • type: map_at_3 value: 5.335999999999999
    • type: map_at_5 value: 7.374
    • type: mrr_at_1 value: 20.408
    • type: mrr_at_10 value: 38.29
    • type: mrr_at_100 value: 39.33
    • type: mrr_at_1000 value: 39.347
    • type: mrr_at_3 value: 32.993
    • type: mrr_at_5 value: 36.973
    • type: ndcg_at_1 value: 17.347
    • type: ndcg_at_10 value: 23.515
    • type: ndcg_at_100 value: 37.457
    • type: ndcg_at_1000 value: 49.439
    • type: ndcg_at_3 value: 22.762999999999998
    • type: ndcg_at_5 value: 22.622
    • type: precision_at_1 value: 20.408
    • type: precision_at_10 value: 22.448999999999998
    • type: precision_at_100 value: 8.184
    • type: precision_at_1000 value: 1.608
    • type: precision_at_3 value: 25.85
    • type: precision_at_5 value: 25.306
    • type: recall_at_1 value: 2.001
    • type: recall_at_10 value: 17.422
    • type: recall_at_100 value: 51.532999999999994
    • type: recall_at_1000 value: 87.466
    • type: recall_at_3 value: 6.861000000000001
    • type: recall_at_5 value: 10.502
  • task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics:
    • type: accuracy value: 71.54419999999999
    • type: ap value: 14.372170450843907
    • type: f1 value: 54.94420257390529
  • task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics:
    • type: accuracy value: 59.402942840973395
    • type: f1 value: 59.4166538875571
  • task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics:
    • type: v_measure value: 41.569064336457906
  • task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics:
    • type: cos_sim_accuracy value: 85.31322644096085
    • type: cos_sim_ap value: 72.14518894837381
    • type: cos_sim_f1 value: 66.67489813557229
    • type: cos_sim_precision value: 62.65954977953121
    • type: cos_sim_recall value: 71.2401055408971
    • type: dot_accuracy value: 85.31322644096085
    • type: dot_ap value: 72.14521480685293
    • type: dot_f1 value: 66.67489813557229
    • type: dot_precision value: 62.65954977953121
    • type: dot_recall value: 71.2401055408971
    • type: euclidean_accuracy value: 85.31322644096085
    • type: euclidean_ap value: 72.14520820485349
    • type: euclidean_f1 value: 66.67489813557229
    • type: euclidean_precision value: 62.65954977953121
    • type: euclidean_recall value: 71.2401055408971
    • type: manhattan_accuracy value: 85.21785778148656
    • type: manhattan_ap value: 72.01177147657364
    • type: manhattan_f1 value: 66.62594673833374
    • type: manhattan_precision value: 62.0336669699727
    • type: manhattan_recall value: 71.95250659630607
    • type: max_accuracy value: 85.31322644096085
    • type: max_ap value: 72.14521480685293
    • type: max_f1 value: 66.67489813557229
  • task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics:
    • type: cos_sim_accuracy value: 89.12756626693057
    • type: cos_sim_ap value: 86.05430786440826
    • type: cos_sim_f1 value: 78.27759692216631
    • type: cos_sim_precision value: 75.33466248931929
    • type: cos_sim_recall value: 81.45980905451185
    • type: dot_accuracy value: 89.12950673341872
    • type: dot_ap value: 86.05431161145492
    • type: dot_f1 value: 78.27759692216631
    • type: dot_precision value: 75.33466248931929
    • type: dot_recall value: 81.45980905451185
    • type: euclidean_accuracy value: 89.12756626693057
    • type: euclidean_ap value: 86.05431303247397
    • type: euclidean_f1 value: 78.27759692216631
    • type: euclidean_precision value: 75.33466248931929
    • type: euclidean_recall value: 81.45980905451185
    • type: manhattan_accuracy value: 89.04994760740482
    • type: manhattan_ap value: 86.00860610892074
    • type: manhattan_f1 value: 78.1846776005392
    • type: manhattan_precision value: 76.10438839480975
    • type: manhattan_recall value: 80.3818909762858
    • type: max_accuracy value: 89.12950673341872
    • type: max_ap value: 86.05431303247397
    • type: max_f1 value: 78.27759692216631

---

The text embedding set trained by Jina AI.

Quick Start

The easiest way to starting using jina-embeddings-v2-small-en is to use Jina AI's Embedding API.

Intended Usage & Model Info

jina-embeddings-v2-small-en is an English, monolingual embedding model supporting 8192 sequence length. It is based on a BERT architecture (JinaBERT) that supports the symmetric bidirectional variant of ALiBi to allow longer sequence length. The backbone jina-bert-v2-small-en is pretrained on the C4 dataset. The model is further trained on Jina AI's collection of more than 400 millions of sentence pairs and hard negatives. These pairs were obtained from various domains and were carefully selected through a thorough cleaning process.

The embedding model was trained using 512 sequence length, but extrapolates to 8k sequence length (or even longer) thanks to ALiBi. This makes our model useful for a range of use cases, especially when processing long documents is needed, including long document retrieval, semantic textual similarity, text reranking, recommendation, RAG and LLM-based generative search, etc.

This model has 33 million parameters, which enables lightning-fast and memory efficient inference, while still delivering impressive performance. Additionally, we provide the following embedding models:

• jina-embeddings-v2-small-en: 33 million parameters (you are here).
• jina-embeddings-v2-base-en: 137 million parameters.
• jina-embeddings-v2-base-zh: 161 million parameters Chinese-English Bilingual embeddings.
• jina-embeddings-v2-base-de: 161 million parameters German-English Bilingual embeddings.
• jina-embeddings-v2-base-es: Spanish-English Bilingual embeddings (soon).

Data & Parameters

Jina Embeddings V2 technical report

Usage

Why mean pooling?

mean poooling takes all token embeddings from model output and averaging them at sentence/paragraph level. It has been proved to be the most effective way to produce high-quality sentence embeddings. We offer an encode function to deal with this.

However, if you would like to do it without using the default encode function:

import torch
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModel

def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0]
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)

sentences = ['How is the weather today?', 'What is the current weather like today?']

tokenizer = AutoTokenizer.from_pretrained('jinaai/jina-embeddings-v2-small-en')
model = AutoModel.from_pretrained('jinaai/jina-embeddings-v2-small-en', trust_remote_code=True)

encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

with torch.no_grad():
    model_output = model(**encoded_input)

embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
embeddings = F.normalize(embeddings, p=2, dim=1)

You can use Jina Embedding models directly from transformers package.

!pip install transformers
from transformers import AutoModel
from numpy.linalg import norm

cos_sim = lambda a,b: (a @ b.T) / (norm(a)*norm(b))
model = AutoModel.from_pretrained('jinaai/jina-embeddings-v2-small-en', trust_remote_code=True) # trust_remote_code is needed to use the encode method
embeddings = model.encode(['How is the weather today?', 'What is the current weather like today?'])
print(cos_sim(embeddings[0], embeddings[1]))

If you only want to handle shorter sequence, such as 2k, pass the max_length parameter to the encode function:

embeddings = model.encode(
    ['Very long ... document'],
    max_length=2048
)

The latest sentence-transformers also supports Jina embeddings:

!pip install -U sentence-transformers
from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim

model = SentenceTransformer(
    "jinaai/jina-embeddings-v2-small-en", # switch to en/zh for English or Chinese
    trust_remote_code=True
)

# control your input sequence length up to 8192
model.max_seq_length = 1024

embeddings = model.encode([
    'How is the weather today?',
    'What is the current weather like today?'
])
print(cos_sim(embeddings[0], embeddings[1]))

Alternatives to Using Transformers Package

1. Managed SaaS: Get started with a free key on Jina AI's Embedding API.
2. Private and high-performance deployment: Get started by picking from our suite of models and deploy them on AWS Sagemaker.

RAG Performance

According to the latest blog post from LLamaIndex,

In summary, to achieve the peak performance in both hit rate and MRR, the combination of OpenAI or JinaAI-Base embeddings with the CohereRerank/bge-reranker-large reranker stands out.

Plans

1. Bilingual embedding models supporting more European & Asian languages, including Spanish, French, Italian and Japanese.
2. Multimodal embedding models enable Multimodal RAG applications.
3. High-performt rerankers.

Trouble Shooting

Loading of Model Code failed

If you forgot to pass the trust_remote_code=True flag when calling AutoModel.from_pretrained or initializing the model via the SentenceTransformer class, you will receive an error that the model weights could not be initialized. This is caused by tranformers falling back to creating a default BERT model, instead of a jina-embedding model:

Some weights of the model checkpoint at jinaai/jina-embeddings-v2-base-en were not used when initializing BertModel: ['encoder.layer.2.mlp.layernorm.weight', 'encoder.layer.3.mlp.layernorm.weight', 'encoder.layer.10.mlp.wo.bias', 'encoder.layer.5.mlp.wo.bias', 'encoder.layer.2.mlp.layernorm.bias', 'encoder.layer.1.mlp.gated_layers.weight', 'encoder.layer.5.mlp.gated_layers.weight', 'encoder.layer.8.mlp.layernorm.bias', ...

Contact

Join our Discord community and chat with other community members about ideas.

Citation

If you find Jina Embeddings useful in your research, please cite the following paper:

@misc{günther2023jina,
      title={Jina Embeddings 2: 8192-Token General-Purpose Text Embeddings for Long Documents}, 
      author={Michael Günther and Jackmin Ong and Isabelle Mohr and Alaeddine Abdessalem and Tanguy Abel and Mohammad Kalim Akram and Susana Guzman and Georgios Mastrapas and Saba Sturua and Bo Wang and Maximilian Werk and Nan Wang and Han Xiao},
      year={2023},
      eprint={2310.19923},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}