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Add infinity / docker example to readme (#13)
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metadata
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  - mteb
  - arctic
  - snowflake-arctic-embed
  - transformers.js
model-index:
  - name: snowflake-arctic-embed-l
    results:
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_counterfactual
          name: MTEB AmazonCounterfactualClassification (en)
          config: en
          split: test
          revision: e8379541af4e31359cca9fbcf4b00f2671dba205
        metrics:
          - type: accuracy
            value: 74.80597014925374
          - type: ap
            value: 37.911466766189875
          - type: f1
            value: 68.88606927542106
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_polarity
          name: MTEB AmazonPolarityClassification
          config: default
          split: test
          revision: e2d317d38cd51312af73b3d32a06d1a08b442046
        metrics:
          - type: accuracy
            value: 78.402275
          - type: ap
            value: 73.03294793248114
          - type: f1
            value: 78.3147786132161
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_reviews_multi
          name: MTEB AmazonReviewsClassification (en)
          config: en
          split: test
          revision: 1399c76144fd37290681b995c656ef9b2e06e26d
        metrics:
          - type: accuracy
            value: 36.717999999999996
          - type: f1
            value: 35.918044248787766
      - task:
          type: Retrieval
        dataset:
          type: mteb/arguana
          name: MTEB ArguAna
          config: default
          split: test
          revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
        metrics:
          - type: map_at_1
            value: 34.495
          - type: map_at_10
            value: 50.236000000000004
          - type: map_at_100
            value: 50.944
          - type: map_at_1000
            value: 50.94499999999999
          - type: map_at_3
            value: 45.341
          - type: map_at_5
            value: 48.286
          - type: mrr_at_1
            value: 35.135
          - type: mrr_at_10
            value: 50.471
          - type: mrr_at_100
            value: 51.185
          - type: mrr_at_1000
            value: 51.187000000000005
          - type: mrr_at_3
            value: 45.602
          - type: mrr_at_5
            value: 48.468
          - type: ndcg_at_1
            value: 34.495
          - type: ndcg_at_10
            value: 59.086000000000006
          - type: ndcg_at_100
            value: 61.937
          - type: ndcg_at_1000
            value: 61.966
          - type: ndcg_at_3
            value: 49.062
          - type: ndcg_at_5
            value: 54.367
          - type: precision_at_1
            value: 34.495
          - type: precision_at_10
            value: 8.734
          - type: precision_at_100
            value: 0.9939999999999999
          - type: precision_at_1000
            value: 0.1
          - type: precision_at_3
            value: 19.962
          - type: precision_at_5
            value: 14.552000000000001
          - type: recall_at_1
            value: 34.495
          - type: recall_at_10
            value: 87.33999999999999
          - type: recall_at_100
            value: 99.431
          - type: recall_at_1000
            value: 99.644
          - type: recall_at_3
            value: 59.885999999999996
          - type: recall_at_5
            value: 72.76
      - task:
          type: Clustering
        dataset:
          type: mteb/arxiv-clustering-p2p
          name: MTEB ArxivClusteringP2P
          config: default
          split: test
          revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
        metrics:
          - type: v_measure
            value: 47.46440874635501
      - task:
          type: Clustering
        dataset:
          type: mteb/arxiv-clustering-s2s
          name: MTEB ArxivClusteringS2S
          config: default
          split: test
          revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
        metrics:
          - type: v_measure
            value: 38.28720154213723
      - task:
          type: Reranking
        dataset:
          type: mteb/askubuntudupquestions-reranking
          name: MTEB AskUbuntuDupQuestions
          config: default
          split: test
          revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
        metrics:
          - type: map
            value: 60.34614226394902
          - type: mrr
            value: 75.05628105351096
      - task:
          type: STS
        dataset:
          type: mteb/biosses-sts
          name: MTEB BIOSSES
          config: default
          split: test
          revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
        metrics:
          - type: cos_sim_pearson
            value: 87.41072716728198
          - type: cos_sim_spearman
            value: 86.34534093114372
          - type: euclidean_pearson
            value: 85.34009667750838
          - type: euclidean_spearman
            value: 86.34534093114372
          - type: manhattan_pearson
            value: 85.2158833586889
          - type: manhattan_spearman
            value: 86.60920236509224
      - task:
          type: Classification
        dataset:
          type: mteb/banking77
          name: MTEB Banking77Classification
          config: default
          split: test
          revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
        metrics:
          - type: accuracy
            value: 80.06493506493507
          - type: f1
            value: 79.28108600339833
      - task:
          type: Clustering
        dataset:
          type: jinaai/big-patent-clustering
          name: MTEB BigPatentClustering
          config: default
          split: test
          revision: 62d5330920bca426ce9d3c76ea914f15fc83e891
        metrics:
          - type: v_measure
            value: 20.545049432417287
      - task:
          type: Clustering
        dataset:
          type: mteb/biorxiv-clustering-p2p
          name: MTEB BiorxivClusteringP2P
          config: default
          split: test
          revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
        metrics:
          - type: v_measure
            value: 37.54369718479804
      - task:
          type: Clustering
        dataset:
          type: mteb/biorxiv-clustering-s2s
          name: MTEB BiorxivClusteringS2S
          config: default
          split: test
          revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
        metrics:
          - type: v_measure
            value: 32.64941588219162
      - task:
          type: Retrieval
        dataset:
          type: mteb/cqadupstack-android
          name: MTEB CQADupstackAndroidRetrieval
          config: default
          split: test
          revision: f46a197baaae43b4f621051089b82a364682dfeb
        metrics:
          - type: map_at_1
            value: 37.264
          - type: map_at_10
            value: 49.43
          - type: map_at_100
            value: 50.967
          - type: map_at_1000
            value: 51.08200000000001
          - type: map_at_3
            value: 45.742
          - type: map_at_5
            value: 47.764
          - type: mrr_at_1
            value: 44.921
          - type: mrr_at_10
            value: 54.879999999999995
          - type: mrr_at_100
            value: 55.525000000000006
          - type: mrr_at_1000
            value: 55.565
          - type: mrr_at_3
            value: 52.480000000000004
          - type: mrr_at_5
            value: 53.86
          - type: ndcg_at_1
            value: 44.921
          - type: ndcg_at_10
            value: 55.664
          - type: ndcg_at_100
            value: 60.488
          - type: ndcg_at_1000
            value: 62.138000000000005
          - type: ndcg_at_3
            value: 50.797000000000004
          - type: ndcg_at_5
            value: 52.94799999999999
          - type: precision_at_1
            value: 44.921
          - type: precision_at_10
            value: 10.587
          - type: precision_at_100
            value: 1.629
          - type: precision_at_1000
            value: 0.203
          - type: precision_at_3
            value: 24.034
          - type: precision_at_5
            value: 17.224999999999998
          - type: recall_at_1
            value: 37.264
          - type: recall_at_10
            value: 67.15
          - type: recall_at_100
            value: 86.811
          - type: recall_at_1000
            value: 97.172
          - type: recall_at_3
            value: 53.15800000000001
          - type: recall_at_5
            value: 59.116
      - task:
          type: Retrieval
        dataset:
          type: mteb/cqadupstack-english
          name: MTEB CQADupstackEnglishRetrieval
          config: default
          split: test
          revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
        metrics:
          - type: map_at_1
            value: 36.237
          - type: map_at_10
            value: 47.941
          - type: map_at_100
            value: 49.131
          - type: map_at_1000
            value: 49.26
          - type: map_at_3
            value: 44.561
          - type: map_at_5
            value: 46.28
          - type: mrr_at_1
            value: 45.605000000000004
          - type: mrr_at_10
            value: 54.039
          - type: mrr_at_100
            value: 54.653
          - type: mrr_at_1000
            value: 54.688
          - type: mrr_at_3
            value: 52.006
          - type: mrr_at_5
            value: 53.096
          - type: ndcg_at_1
            value: 45.605000000000004
          - type: ndcg_at_10
            value: 53.916
          - type: ndcg_at_100
            value: 57.745999999999995
          - type: ndcg_at_1000
            value: 59.492999999999995
          - type: ndcg_at_3
            value: 49.774
          - type: ndcg_at_5
            value: 51.434999999999995
          - type: precision_at_1
            value: 45.605000000000004
          - type: precision_at_10
            value: 10.229000000000001
          - type: precision_at_100
            value: 1.55
          - type: precision_at_1000
            value: 0.2
          - type: precision_at_3
            value: 24.098
          - type: precision_at_5
            value: 16.726
          - type: recall_at_1
            value: 36.237
          - type: recall_at_10
            value: 64.03
          - type: recall_at_100
            value: 80.423
          - type: recall_at_1000
            value: 91.03
          - type: recall_at_3
            value: 51.20400000000001
          - type: recall_at_5
            value: 56.298
      - task:
          type: Retrieval
        dataset:
          type: mteb/cqadupstack-gaming
          name: MTEB CQADupstackGamingRetrieval
          config: default
          split: test
          revision: 4885aa143210c98657558c04aaf3dc47cfb54340
        metrics:
          - type: map_at_1
            value: 47.278
          - type: map_at_10
            value: 59.757000000000005
          - type: map_at_100
            value: 60.67
          - type: map_at_1000
            value: 60.714
          - type: map_at_3
            value: 56.714
          - type: map_at_5
            value: 58.453
          - type: mrr_at_1
            value: 53.73
          - type: mrr_at_10
            value: 62.970000000000006
          - type: mrr_at_100
            value: 63.507999999999996
          - type: mrr_at_1000
            value: 63.53
          - type: mrr_at_3
            value: 60.909
          - type: mrr_at_5
            value: 62.172000000000004
          - type: ndcg_at_1
            value: 53.73
          - type: ndcg_at_10
            value: 64.97
          - type: ndcg_at_100
            value: 68.394
          - type: ndcg_at_1000
            value: 69.255
          - type: ndcg_at_3
            value: 60.228
          - type: ndcg_at_5
            value: 62.617999999999995
          - type: precision_at_1
            value: 53.73
          - type: precision_at_10
            value: 10.056
          - type: precision_at_100
            value: 1.265
          - type: precision_at_1000
            value: 0.13699999999999998
          - type: precision_at_3
            value: 26.332
          - type: precision_at_5
            value: 17.743000000000002
          - type: recall_at_1
            value: 47.278
          - type: recall_at_10
            value: 76.86500000000001
          - type: recall_at_100
            value: 91.582
          - type: recall_at_1000
            value: 97.583
          - type: recall_at_3
            value: 64.443
          - type: recall_at_5
            value: 70.283
      - task:
          type: Retrieval
        dataset:
          type: mteb/cqadupstack-gis
          name: MTEB CQADupstackGisRetrieval
          config: default
          split: test
          revision: 5003b3064772da1887988e05400cf3806fe491f2
        metrics:
          - type: map_at_1
            value: 29.702
          - type: map_at_10
            value: 39.463
          - type: map_at_100
            value: 40.508
          - type: map_at_1000
            value: 40.579
          - type: map_at_3
            value: 36.748999999999995
          - type: map_at_5
            value: 38.296
          - type: mrr_at_1
            value: 31.977
          - type: mrr_at_10
            value: 41.739
          - type: mrr_at_100
            value: 42.586
          - type: mrr_at_1000
            value: 42.636
          - type: mrr_at_3
            value: 39.096
          - type: mrr_at_5
            value: 40.695
          - type: ndcg_at_1
            value: 31.977
          - type: ndcg_at_10
            value: 44.855000000000004
          - type: ndcg_at_100
            value: 49.712
          - type: ndcg_at_1000
            value: 51.443000000000005
          - type: ndcg_at_3
            value: 39.585
          - type: ndcg_at_5
            value: 42.244
          - type: precision_at_1
            value: 31.977
          - type: precision_at_10
            value: 6.768000000000001
          - type: precision_at_100
            value: 0.9690000000000001
          - type: precision_at_1000
            value: 0.116
          - type: precision_at_3
            value: 16.761
          - type: precision_at_5
            value: 11.593
          - type: recall_at_1
            value: 29.702
          - type: recall_at_10
            value: 59.082
          - type: recall_at_100
            value: 80.92
          - type: recall_at_1000
            value: 93.728
          - type: recall_at_3
            value: 45.212
          - type: recall_at_5
            value: 51.449
      - task:
          type: Retrieval
        dataset:
          type: mteb/cqadupstack-mathematica
          name: MTEB CQADupstackMathematicaRetrieval
          config: default
          split: test
          revision: 90fceea13679c63fe563ded68f3b6f06e50061de
        metrics:
          - type: map_at_1
            value: 21.336
          - type: map_at_10
            value: 30.137999999999998
          - type: map_at_100
            value: 31.385
          - type: map_at_1000
            value: 31.495
          - type: map_at_3
            value: 27.481
          - type: map_at_5
            value: 28.772
          - type: mrr_at_1
            value: 25.871
          - type: mrr_at_10
            value: 34.686
          - type: mrr_at_100
            value: 35.649
          - type: mrr_at_1000
            value: 35.705
          - type: mrr_at_3
            value: 32.09
          - type: mrr_at_5
            value: 33.52
          - type: ndcg_at_1
            value: 25.871
          - type: ndcg_at_10
            value: 35.617
          - type: ndcg_at_100
            value: 41.272999999999996
          - type: ndcg_at_1000
            value: 43.725
          - type: ndcg_at_3
            value: 30.653999999999996
          - type: ndcg_at_5
            value: 32.714
          - type: precision_at_1
            value: 25.871
          - type: precision_at_10
            value: 6.4799999999999995
          - type: precision_at_100
            value: 1.0699999999999998
          - type: precision_at_1000
            value: 0.13999999999999999
          - type: precision_at_3
            value: 14.469000000000001
          - type: precision_at_5
            value: 10.274
          - type: recall_at_1
            value: 21.336
          - type: recall_at_10
            value: 47.746
          - type: recall_at_100
            value: 71.773
          - type: recall_at_1000
            value: 89.05199999999999
          - type: recall_at_3
            value: 34.172999999999995
          - type: recall_at_5
            value: 39.397999999999996
      - task:
          type: Retrieval
        dataset:
          type: mteb/cqadupstack-physics
          name: MTEB CQADupstackPhysicsRetrieval
          config: default
          split: test
          revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
        metrics:
          - type: map_at_1
            value: 34.424
          - type: map_at_10
            value: 45.647999999999996
          - type: map_at_100
            value: 46.907
          - type: map_at_1000
            value: 47.010999999999996
          - type: map_at_3
            value: 42.427
          - type: map_at_5
            value: 44.285000000000004
          - type: mrr_at_1
            value: 41.867
          - type: mrr_at_10
            value: 51.17699999999999
          - type: mrr_at_100
            value: 51.937
          - type: mrr_at_1000
            value: 51.975
          - type: mrr_at_3
            value: 48.941
          - type: mrr_at_5
            value: 50.322
          - type: ndcg_at_1
            value: 41.867
          - type: ndcg_at_10
            value: 51.534
          - type: ndcg_at_100
            value: 56.696999999999996
          - type: ndcg_at_1000
            value: 58.475
          - type: ndcg_at_3
            value: 46.835
          - type: ndcg_at_5
            value: 49.161
          - type: precision_at_1
            value: 41.867
          - type: precision_at_10
            value: 9.134
          - type: precision_at_100
            value: 1.362
          - type: precision_at_1000
            value: 0.17099999999999999
          - type: precision_at_3
            value: 22.073
          - type: precision_at_5
            value: 15.495999999999999
          - type: recall_at_1
            value: 34.424
          - type: recall_at_10
            value: 63.237
          - type: recall_at_100
            value: 84.774
          - type: recall_at_1000
            value: 95.987
          - type: recall_at_3
            value: 49.888
          - type: recall_at_5
            value: 55.940999999999995
      - task:
          type: Retrieval
        dataset:
          type: mteb/cqadupstack-programmers
          name: MTEB CQADupstackProgrammersRetrieval
          config: default
          split: test
          revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
        metrics:
          - type: map_at_1
            value: 30.72
          - type: map_at_10
            value: 41.327999999999996
          - type: map_at_100
            value: 42.651
          - type: map_at_1000
            value: 42.739
          - type: map_at_3
            value: 38.223
          - type: map_at_5
            value: 40.053
          - type: mrr_at_1
            value: 37.9
          - type: mrr_at_10
            value: 46.857
          - type: mrr_at_100
            value: 47.673
          - type: mrr_at_1000
            value: 47.711999999999996
          - type: mrr_at_3
            value: 44.292
          - type: mrr_at_5
            value: 45.845
          - type: ndcg_at_1
            value: 37.9
          - type: ndcg_at_10
            value: 47.105999999999995
          - type: ndcg_at_100
            value: 52.56999999999999
          - type: ndcg_at_1000
            value: 54.37800000000001
          - type: ndcg_at_3
            value: 42.282
          - type: ndcg_at_5
            value: 44.646
          - type: precision_at_1
            value: 37.9
          - type: precision_at_10
            value: 8.368
          - type: precision_at_100
            value: 1.283
          - type: precision_at_1000
            value: 0.16
          - type: precision_at_3
            value: 20.015
          - type: precision_at_5
            value: 14.132
          - type: recall_at_1
            value: 30.72
          - type: recall_at_10
            value: 58.826
          - type: recall_at_100
            value: 82.104
          - type: recall_at_1000
            value: 94.194
          - type: recall_at_3
            value: 44.962999999999994
          - type: recall_at_5
            value: 51.426
      - task:
          type: Retrieval
        dataset:
          type: mteb/cqadupstack
          name: MTEB CQADupstackRetrieval
          config: default
          split: test
          revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
        metrics:
          - type: map_at_1
            value: 31.656583333333334
          - type: map_at_10
            value: 41.59883333333333
          - type: map_at_100
            value: 42.80350000000001
          - type: map_at_1000
            value: 42.91075
          - type: map_at_3
            value: 38.68908333333333
          - type: map_at_5
            value: 40.27733333333334
          - type: mrr_at_1
            value: 37.23483333333334
          - type: mrr_at_10
            value: 45.782000000000004
          - type: mrr_at_100
            value: 46.577083333333334
          - type: mrr_at_1000
            value: 46.62516666666667
          - type: mrr_at_3
            value: 43.480666666666664
          - type: mrr_at_5
            value: 44.79833333333333
          - type: ndcg_at_1
            value: 37.23483333333334
          - type: ndcg_at_10
            value: 46.971500000000006
          - type: ndcg_at_100
            value: 51.90125
          - type: ndcg_at_1000
            value: 53.86366666666667
          - type: ndcg_at_3
            value: 42.31791666666667
          - type: ndcg_at_5
            value: 44.458666666666666
          - type: precision_at_1
            value: 37.23483333333334
          - type: precision_at_10
            value: 8.044583333333332
          - type: precision_at_100
            value: 1.2334166666666666
          - type: precision_at_1000
            value: 0.15925
          - type: precision_at_3
            value: 19.240833333333327
          - type: precision_at_5
            value: 13.435083333333333
          - type: recall_at_1
            value: 31.656583333333334
          - type: recall_at_10
            value: 58.44758333333333
          - type: recall_at_100
            value: 79.93658333333332
          - type: recall_at_1000
            value: 93.32491666666668
          - type: recall_at_3
            value: 45.44266666666667
          - type: recall_at_5
            value: 50.99866666666666
      - task:
          type: Retrieval
        dataset:
          type: mteb/cqadupstack-stats
          name: MTEB CQADupstackStatsRetrieval
          config: default
          split: test
          revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
        metrics:
          - type: map_at_1
            value: 28.247
          - type: map_at_10
            value: 35.443999999999996
          - type: map_at_100
            value: 36.578
          - type: map_at_1000
            value: 36.675999999999995
          - type: map_at_3
            value: 33.276
          - type: map_at_5
            value: 34.536
          - type: mrr_at_1
            value: 31.747999999999998
          - type: mrr_at_10
            value: 38.413000000000004
          - type: mrr_at_100
            value: 39.327
          - type: mrr_at_1000
            value: 39.389
          - type: mrr_at_3
            value: 36.401
          - type: mrr_at_5
            value: 37.543
          - type: ndcg_at_1
            value: 31.747999999999998
          - type: ndcg_at_10
            value: 39.646
          - type: ndcg_at_100
            value: 44.861000000000004
          - type: ndcg_at_1000
            value: 47.197
          - type: ndcg_at_3
            value: 35.764
          - type: ndcg_at_5
            value: 37.635999999999996
          - type: precision_at_1
            value: 31.747999999999998
          - type: precision_at_10
            value: 6.12
          - type: precision_at_100
            value: 0.942
          - type: precision_at_1000
            value: 0.123
          - type: precision_at_3
            value: 15.235000000000001
          - type: precision_at_5
            value: 10.491
          - type: recall_at_1
            value: 28.247
          - type: recall_at_10
            value: 49.456
          - type: recall_at_100
            value: 73.02499999999999
          - type: recall_at_1000
            value: 89.898
          - type: recall_at_3
            value: 38.653999999999996
          - type: recall_at_5
            value: 43.259
      - task:
          type: Retrieval
        dataset:
          type: mteb/cqadupstack-tex
          name: MTEB CQADupstackTexRetrieval
          config: default
          split: test
          revision: 46989137a86843e03a6195de44b09deda022eec7
        metrics:
          - type: map_at_1
            value: 22.45
          - type: map_at_10
            value: 30.476999999999997
          - type: map_at_100
            value: 31.630999999999997
          - type: map_at_1000
            value: 31.755
          - type: map_at_3
            value: 27.989000000000004
          - type: map_at_5
            value: 29.410999999999998
          - type: mrr_at_1
            value: 26.979
          - type: mrr_at_10
            value: 34.316
          - type: mrr_at_100
            value: 35.272999999999996
          - type: mrr_at_1000
            value: 35.342
          - type: mrr_at_3
            value: 32.14
          - type: mrr_at_5
            value: 33.405
          - type: ndcg_at_1
            value: 26.979
          - type: ndcg_at_10
            value: 35.166
          - type: ndcg_at_100
            value: 40.583000000000006
          - type: ndcg_at_1000
            value: 43.282
          - type: ndcg_at_3
            value: 30.916
          - type: ndcg_at_5
            value: 32.973
          - type: precision_at_1
            value: 26.979
          - type: precision_at_10
            value: 6.132
          - type: precision_at_100
            value: 1.047
          - type: precision_at_1000
            value: 0.145
          - type: precision_at_3
            value: 14.360999999999999
          - type: precision_at_5
            value: 10.227
          - type: recall_at_1
            value: 22.45
          - type: recall_at_10
            value: 45.348
          - type: recall_at_100
            value: 69.484
          - type: recall_at_1000
            value: 88.628
          - type: recall_at_3
            value: 33.338
          - type: recall_at_5
            value: 38.746
      - task:
          type: Retrieval
        dataset:
          type: mteb/cqadupstack-unix
          name: MTEB CQADupstackUnixRetrieval
          config: default
          split: test
          revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
        metrics:
          - type: map_at_1
            value: 32.123000000000005
          - type: map_at_10
            value: 41.778
          - type: map_at_100
            value: 42.911
          - type: map_at_1000
            value: 42.994
          - type: map_at_3
            value: 38.558
          - type: map_at_5
            value: 40.318
          - type: mrr_at_1
            value: 37.687
          - type: mrr_at_10
            value: 45.889
          - type: mrr_at_100
            value: 46.672999999999995
          - type: mrr_at_1000
            value: 46.72
          - type: mrr_at_3
            value: 43.33
          - type: mrr_at_5
            value: 44.734
          - type: ndcg_at_1
            value: 37.687
          - type: ndcg_at_10
            value: 47.258
          - type: ndcg_at_100
            value: 52.331
          - type: ndcg_at_1000
            value: 54.152
          - type: ndcg_at_3
            value: 41.857
          - type: ndcg_at_5
            value: 44.283
          - type: precision_at_1
            value: 37.687
          - type: precision_at_10
            value: 7.892
          - type: precision_at_100
            value: 1.183
          - type: precision_at_1000
            value: 0.14300000000000002
          - type: precision_at_3
            value: 18.781
          - type: precision_at_5
            value: 13.134
          - type: recall_at_1
            value: 32.123000000000005
          - type: recall_at_10
            value: 59.760000000000005
          - type: recall_at_100
            value: 81.652
          - type: recall_at_1000
            value: 94.401
          - type: recall_at_3
            value: 44.996
          - type: recall_at_5
            value: 51.184
      - task:
          type: Retrieval
        dataset:
          type: mteb/cqadupstack-webmasters
          name: MTEB CQADupstackWebmastersRetrieval
          config: default
          split: test
          revision: 160c094312a0e1facb97e55eeddb698c0abe3571
        metrics:
          - type: map_at_1
            value: 33.196999999999996
          - type: map_at_10
            value: 42.012
          - type: map_at_100
            value: 43.663999999999994
          - type: map_at_1000
            value: 43.883
          - type: map_at_3
            value: 39.33
          - type: map_at_5
            value: 40.586
          - type: mrr_at_1
            value: 39.328
          - type: mrr_at_10
            value: 46.57
          - type: mrr_at_100
            value: 47.508
          - type: mrr_at_1000
            value: 47.558
          - type: mrr_at_3
            value: 44.532
          - type: mrr_at_5
            value: 45.58
          - type: ndcg_at_1
            value: 39.328
          - type: ndcg_at_10
            value: 47.337
          - type: ndcg_at_100
            value: 52.989
          - type: ndcg_at_1000
            value: 55.224
          - type: ndcg_at_3
            value: 43.362
          - type: ndcg_at_5
            value: 44.866
          - type: precision_at_1
            value: 39.328
          - type: precision_at_10
            value: 8.577
          - type: precision_at_100
            value: 1.5789999999999997
          - type: precision_at_1000
            value: 0.25
          - type: precision_at_3
            value: 19.697
          - type: precision_at_5
            value: 13.755
          - type: recall_at_1
            value: 33.196999999999996
          - type: recall_at_10
            value: 56.635000000000005
          - type: recall_at_100
            value: 81.882
          - type: recall_at_1000
            value: 95.342
          - type: recall_at_3
            value: 44.969
          - type: recall_at_5
            value: 49.266
      - task:
          type: Retrieval
        dataset:
          type: mteb/cqadupstack-wordpress
          name: MTEB CQADupstackWordpressRetrieval
          config: default
          split: test
          revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
        metrics:
          - type: map_at_1
            value: 26.901000000000003
          - type: map_at_10
            value: 35.77
          - type: map_at_100
            value: 36.638999999999996
          - type: map_at_1000
            value: 36.741
          - type: map_at_3
            value: 33.219
          - type: map_at_5
            value: 34.574
          - type: mrr_at_1
            value: 29.205
          - type: mrr_at_10
            value: 37.848
          - type: mrr_at_100
            value: 38.613
          - type: mrr_at_1000
            value: 38.682
          - type: mrr_at_3
            value: 35.551
          - type: mrr_at_5
            value: 36.808
          - type: ndcg_at_1
            value: 29.205
          - type: ndcg_at_10
            value: 40.589
          - type: ndcg_at_100
            value: 45.171
          - type: ndcg_at_1000
            value: 47.602
          - type: ndcg_at_3
            value: 35.760999999999996
          - type: ndcg_at_5
            value: 37.980000000000004
          - type: precision_at_1
            value: 29.205
          - type: precision_at_10
            value: 6.192
          - type: precision_at_100
            value: 0.922
          - type: precision_at_1000
            value: 0.123
          - type: precision_at_3
            value: 15.034
          - type: precision_at_5
            value: 10.424999999999999
          - type: recall_at_1
            value: 26.901000000000003
          - type: recall_at_10
            value: 53.236000000000004
          - type: recall_at_100
            value: 74.809
          - type: recall_at_1000
            value: 92.884
          - type: recall_at_3
            value: 40.314
          - type: recall_at_5
            value: 45.617999999999995
      - task:
          type: Retrieval
        dataset:
          type: mteb/climate-fever
          name: MTEB ClimateFEVER
          config: default
          split: test
          revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
        metrics:
          - type: map_at_1
            value: 16.794999999999998
          - type: map_at_10
            value: 29.322
          - type: map_at_100
            value: 31.463
          - type: map_at_1000
            value: 31.643
          - type: map_at_3
            value: 24.517
          - type: map_at_5
            value: 27.237000000000002
          - type: mrr_at_1
            value: 37.655
          - type: mrr_at_10
            value: 50.952
          - type: mrr_at_100
            value: 51.581999999999994
          - type: mrr_at_1000
            value: 51.61
          - type: mrr_at_3
            value: 47.991
          - type: mrr_at_5
            value: 49.744
          - type: ndcg_at_1
            value: 37.655
          - type: ndcg_at_10
            value: 39.328
          - type: ndcg_at_100
            value: 46.358
          - type: ndcg_at_1000
            value: 49.245
          - type: ndcg_at_3
            value: 33.052
          - type: ndcg_at_5
            value: 35.407
          - type: precision_at_1
            value: 37.655
          - type: precision_at_10
            value: 12.202
          - type: precision_at_100
            value: 1.9789999999999999
          - type: precision_at_1000
            value: 0.252
          - type: precision_at_3
            value: 24.973
          - type: precision_at_5
            value: 19.075
          - type: recall_at_1
            value: 16.794999999999998
          - type: recall_at_10
            value: 45.716
          - type: recall_at_100
            value: 68.919
          - type: recall_at_1000
            value: 84.71600000000001
          - type: recall_at_3
            value: 30.135
          - type: recall_at_5
            value: 37.141999999999996
      - task:
          type: Retrieval
        dataset:
          type: mteb/dbpedia
          name: MTEB DBPedia
          config: default
          split: test
          revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
        metrics:
          - type: map_at_1
            value: 9.817
          - type: map_at_10
            value: 22.058
          - type: map_at_100
            value: 31.805
          - type: map_at_1000
            value: 33.562999999999995
          - type: map_at_3
            value: 15.537
          - type: map_at_5
            value: 18.199
          - type: mrr_at_1
            value: 72.75
          - type: mrr_at_10
            value: 79.804
          - type: mrr_at_100
            value: 80.089
          - type: mrr_at_1000
            value: 80.09100000000001
          - type: mrr_at_3
            value: 78.75
          - type: mrr_at_5
            value: 79.325
          - type: ndcg_at_1
            value: 59.875
          - type: ndcg_at_10
            value: 45.972
          - type: ndcg_at_100
            value: 51.092999999999996
          - type: ndcg_at_1000
            value: 58.048
          - type: ndcg_at_3
            value: 50.552
          - type: ndcg_at_5
            value: 47.672
          - type: precision_at_1
            value: 72.75
          - type: precision_at_10
            value: 37.05
          - type: precision_at_100
            value: 12.005
          - type: precision_at_1000
            value: 2.221
          - type: precision_at_3
            value: 54.083000000000006
          - type: precision_at_5
            value: 46.2
          - type: recall_at_1
            value: 9.817
          - type: recall_at_10
            value: 27.877000000000002
          - type: recall_at_100
            value: 57.974000000000004
          - type: recall_at_1000
            value: 80.085
          - type: recall_at_3
            value: 16.911
          - type: recall_at_5
            value: 20.689
      - task:
          type: Classification
        dataset:
          type: mteb/emotion
          name: MTEB EmotionClassification
          config: default
          split: test
          revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
        metrics:
          - type: accuracy
            value: 46.464999999999996
          - type: f1
            value: 42.759588662873796
      - task:
          type: Retrieval
        dataset:
          type: mteb/fever
          name: MTEB FEVER
          config: default
          split: test
          revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
        metrics:
          - type: map_at_1
            value: 75.82900000000001
          - type: map_at_10
            value: 84.613
          - type: map_at_100
            value: 84.845
          - type: map_at_1000
            value: 84.855
          - type: map_at_3
            value: 83.498
          - type: map_at_5
            value: 84.29299999999999
          - type: mrr_at_1
            value: 81.69800000000001
          - type: mrr_at_10
            value: 88.84100000000001
          - type: mrr_at_100
            value: 88.887
          - type: mrr_at_1000
            value: 88.888
          - type: mrr_at_3
            value: 88.179
          - type: mrr_at_5
            value: 88.69200000000001
          - type: ndcg_at_1
            value: 81.69800000000001
          - type: ndcg_at_10
            value: 88.21799999999999
          - type: ndcg_at_100
            value: 88.961
          - type: ndcg_at_1000
            value: 89.131
          - type: ndcg_at_3
            value: 86.591
          - type: ndcg_at_5
            value: 87.666
          - type: precision_at_1
            value: 81.69800000000001
          - type: precision_at_10
            value: 10.615
          - type: precision_at_100
            value: 1.125
          - type: precision_at_1000
            value: 0.11499999999999999
          - type: precision_at_3
            value: 33.208
          - type: precision_at_5
            value: 20.681
          - type: recall_at_1
            value: 75.82900000000001
          - type: recall_at_10
            value: 94.97
          - type: recall_at_100
            value: 97.786
          - type: recall_at_1000
            value: 98.809
          - type: recall_at_3
            value: 90.625
          - type: recall_at_5
            value: 93.345
      - task:
          type: Retrieval
        dataset:
          type: mteb/fiqa
          name: MTEB FiQA2018
          config: default
          split: test
          revision: 27a168819829fe9bcd655c2df245fb19452e8e06
        metrics:
          - type: map_at_1
            value: 22.788
          - type: map_at_10
            value: 36.71
          - type: map_at_100
            value: 38.527
          - type: map_at_1000
            value: 38.701
          - type: map_at_3
            value: 32.318999999999996
          - type: map_at_5
            value: 34.809
          - type: mrr_at_1
            value: 44.444
          - type: mrr_at_10
            value: 52.868
          - type: mrr_at_100
            value: 53.52400000000001
          - type: mrr_at_1000
            value: 53.559999999999995
          - type: mrr_at_3
            value: 50.153999999999996
          - type: mrr_at_5
            value: 51.651
          - type: ndcg_at_1
            value: 44.444
          - type: ndcg_at_10
            value: 44.707
          - type: ndcg_at_100
            value: 51.174
          - type: ndcg_at_1000
            value: 53.996
          - type: ndcg_at_3
            value: 40.855999999999995
          - type: ndcg_at_5
            value: 42.113
          - type: precision_at_1
            value: 44.444
          - type: precision_at_10
            value: 12.021999999999998
          - type: precision_at_100
            value: 1.8950000000000002
          - type: precision_at_1000
            value: 0.241
          - type: precision_at_3
            value: 26.8
          - type: precision_at_5
            value: 19.66
          - type: recall_at_1
            value: 22.788
          - type: recall_at_10
            value: 51.793
          - type: recall_at_100
            value: 75.69500000000001
          - type: recall_at_1000
            value: 92.292
          - type: recall_at_3
            value: 37.375
          - type: recall_at_5
            value: 43.682
      - task:
          type: Retrieval
        dataset:
          type: mteb/hotpotqa
          name: MTEB HotpotQA
          config: default
          split: test
          revision: ab518f4d6fcca38d87c25209f94beba119d02014
        metrics:
          - type: map_at_1
            value: 41.276
          - type: map_at_10
            value: 67.245
          - type: map_at_100
            value: 68.061
          - type: map_at_1000
            value: 68.11399999999999
          - type: map_at_3
            value: 63.693
          - type: map_at_5
            value: 65.90899999999999
          - type: mrr_at_1
            value: 82.552
          - type: mrr_at_10
            value: 87.741
          - type: mrr_at_100
            value: 87.868
          - type: mrr_at_1000
            value: 87.871
          - type: mrr_at_3
            value: 86.98599999999999
          - type: mrr_at_5
            value: 87.469
          - type: ndcg_at_1
            value: 82.552
          - type: ndcg_at_10
            value: 75.176
          - type: ndcg_at_100
            value: 77.902
          - type: ndcg_at_1000
            value: 78.852
          - type: ndcg_at_3
            value: 70.30499999999999
          - type: ndcg_at_5
            value: 73.00999999999999
          - type: precision_at_1
            value: 82.552
          - type: precision_at_10
            value: 15.765
          - type: precision_at_100
            value: 1.788
          - type: precision_at_1000
            value: 0.191
          - type: precision_at_3
            value: 45.375
          - type: precision_at_5
            value: 29.360999999999997
          - type: recall_at_1
            value: 41.276
          - type: recall_at_10
            value: 78.825
          - type: recall_at_100
            value: 89.41900000000001
          - type: recall_at_1000
            value: 95.625
          - type: recall_at_3
            value: 68.062
          - type: recall_at_5
            value: 73.40299999999999
      - task:
          type: Classification
        dataset:
          type: mteb/imdb
          name: MTEB ImdbClassification
          config: default
          split: test
          revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
        metrics:
          - type: accuracy
            value: 72.876
          - type: ap
            value: 67.15477852410164
          - type: f1
            value: 72.65147370025373
      - task:
          type: Retrieval
        dataset:
          type: mteb/msmarco
          name: MTEB MSMARCO
          config: default
          split: dev
          revision: c5a29a104738b98a9e76336939199e264163d4a0
        metrics:
          - type: map_at_1
            value: 21.748
          - type: map_at_10
            value: 34.626000000000005
          - type: map_at_100
            value: 35.813
          - type: map_at_1000
            value: 35.859
          - type: map_at_3
            value: 30.753000000000004
          - type: map_at_5
            value: 33.049
          - type: mrr_at_1
            value: 22.35
          - type: mrr_at_10
            value: 35.23
          - type: mrr_at_100
            value: 36.359
          - type: mrr_at_1000
            value: 36.399
          - type: mrr_at_3
            value: 31.436999999999998
          - type: mrr_at_5
            value: 33.687
          - type: ndcg_at_1
            value: 22.364
          - type: ndcg_at_10
            value: 41.677
          - type: ndcg_at_100
            value: 47.355999999999995
          - type: ndcg_at_1000
            value: 48.494
          - type: ndcg_at_3
            value: 33.85
          - type: ndcg_at_5
            value: 37.942
          - type: precision_at_1
            value: 22.364
          - type: precision_at_10
            value: 6.6000000000000005
          - type: precision_at_100
            value: 0.9450000000000001
          - type: precision_at_1000
            value: 0.104
          - type: precision_at_3
            value: 14.527000000000001
          - type: precision_at_5
            value: 10.796999999999999
          - type: recall_at_1
            value: 21.748
          - type: recall_at_10
            value: 63.292
          - type: recall_at_100
            value: 89.427
          - type: recall_at_1000
            value: 98.13499999999999
          - type: recall_at_3
            value: 42.126000000000005
          - type: recall_at_5
            value: 51.968
      - task:
          type: Classification
        dataset:
          type: mteb/mtop_domain
          name: MTEB MTOPDomainClassification (en)
          config: en
          split: test
          revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
        metrics:
          - type: accuracy
            value: 92.62425900592795
          - type: f1
            value: 92.08497761553683
      - task:
          type: Classification
        dataset:
          type: mteb/mtop_intent
          name: MTEB MTOPIntentClassification (en)
          config: en
          split: test
          revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
        metrics:
          - type: accuracy
            value: 64.51436388508893
          - type: f1
            value: 45.884016531912906
      - task:
          type: Classification
        dataset:
          type: masakhane/masakhanews
          name: MTEB MasakhaNEWSClassification (eng)
          config: eng
          split: test
          revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
        metrics:
          - type: accuracy
            value: 76.57172995780591
          - type: f1
            value: 75.52979910878491
      - task:
          type: Clustering
        dataset:
          type: masakhane/masakhanews
          name: MTEB MasakhaNEWSClusteringP2P (eng)
          config: eng
          split: test
          revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
        metrics:
          - type: v_measure
            value: 44.84052695201612
      - task:
          type: Clustering
        dataset:
          type: masakhane/masakhanews
          name: MTEB MasakhaNEWSClusteringS2S (eng)
          config: eng
          split: test
          revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
        metrics:
          - type: v_measure
            value: 21.443971229936494
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_intent
          name: MTEB MassiveIntentClassification (en)
          config: en
          split: test
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
        metrics:
          - type: accuracy
            value: 65.79354404841965
          - type: f1
            value: 63.17260074126185
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_scenario
          name: MTEB MassiveScenarioClassification (en)
          config: en
          split: test
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
        metrics:
          - type: accuracy
            value: 71.09616677874916
          - type: f1
            value: 69.74285784421075
      - task:
          type: Clustering
        dataset:
          type: mteb/medrxiv-clustering-p2p
          name: MTEB MedrxivClusteringP2P
          config: default
          split: test
          revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
        metrics:
          - type: v_measure
            value: 31.474709231086184
      - task:
          type: Clustering
        dataset:
          type: mteb/medrxiv-clustering-s2s
          name: MTEB MedrxivClusteringS2S
          config: default
          split: test
          revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
        metrics:
          - type: v_measure
            value: 28.93630367824217
      - task:
          type: Reranking
        dataset:
          type: mteb/mind_small
          name: MTEB MindSmallReranking
          config: default
          split: test
          revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
        metrics:
          - type: map
            value: 29.08234393834005
          - type: mrr
            value: 29.740466971605432
      - task:
          type: Retrieval
        dataset:
          type: mteb/nfcorpus
          name: MTEB NFCorpus
          config: default
          split: test
          revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
        metrics:
          - type: map_at_1
            value: 6.2059999999999995
          - type: map_at_10
            value: 14.442
          - type: map_at_100
            value: 18.005
          - type: map_at_1000
            value: 19.488
          - type: map_at_3
            value: 10.666
          - type: map_at_5
            value: 12.45
          - type: mrr_at_1
            value: 47.678
          - type: mrr_at_10
            value: 57.519
          - type: mrr_at_100
            value: 58.13700000000001
          - type: mrr_at_1000
            value: 58.167
          - type: mrr_at_3
            value: 55.779
          - type: mrr_at_5
            value: 56.940000000000005
          - type: ndcg_at_1
            value: 45.82
          - type: ndcg_at_10
            value: 37.651
          - type: ndcg_at_100
            value: 34.001999999999995
          - type: ndcg_at_1000
            value: 42.626
          - type: ndcg_at_3
            value: 43.961
          - type: ndcg_at_5
            value: 41.461
          - type: precision_at_1
            value: 47.678
          - type: precision_at_10
            value: 27.584999999999997
          - type: precision_at_100
            value: 8.455
          - type: precision_at_1000
            value: 2.118
          - type: precision_at_3
            value: 41.692
          - type: precision_at_5
            value: 36.161
          - type: recall_at_1
            value: 6.2059999999999995
          - type: recall_at_10
            value: 18.599
          - type: recall_at_100
            value: 33.608
          - type: recall_at_1000
            value: 65.429
          - type: recall_at_3
            value: 12.126000000000001
          - type: recall_at_5
            value: 14.902000000000001
      - task:
          type: Retrieval
        dataset:
          type: mteb/nq
          name: MTEB NQ
          config: default
          split: test
          revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
        metrics:
          - type: map_at_1
            value: 39.117000000000004
          - type: map_at_10
            value: 55.535000000000004
          - type: map_at_100
            value: 56.32899999999999
          - type: map_at_1000
            value: 56.34400000000001
          - type: map_at_3
            value: 51.439
          - type: map_at_5
            value: 53.89699999999999
          - type: mrr_at_1
            value: 43.714
          - type: mrr_at_10
            value: 58.05200000000001
          - type: mrr_at_100
            value: 58.582
          - type: mrr_at_1000
            value: 58.592
          - type: mrr_at_3
            value: 54.896
          - type: mrr_at_5
            value: 56.874
          - type: ndcg_at_1
            value: 43.685
          - type: ndcg_at_10
            value: 63.108
          - type: ndcg_at_100
            value: 66.231
          - type: ndcg_at_1000
            value: 66.583
          - type: ndcg_at_3
            value: 55.659000000000006
          - type: ndcg_at_5
            value: 59.681
          - type: precision_at_1
            value: 43.685
          - type: precision_at_10
            value: 9.962
          - type: precision_at_100
            value: 1.174
          - type: precision_at_1000
            value: 0.121
          - type: precision_at_3
            value: 24.961
          - type: precision_at_5
            value: 17.352
          - type: recall_at_1
            value: 39.117000000000004
          - type: recall_at_10
            value: 83.408
          - type: recall_at_100
            value: 96.553
          - type: recall_at_1000
            value: 99.136
          - type: recall_at_3
            value: 64.364
          - type: recall_at_5
            value: 73.573
      - task:
          type: Classification
        dataset:
          type: ag_news
          name: MTEB NewsClassification
          config: default
          split: test
          revision: eb185aade064a813bc0b7f42de02595523103ca4
        metrics:
          - type: accuracy
            value: 78.87763157894737
          - type: f1
            value: 78.69611753876177
      - task:
          type: PairClassification
        dataset:
          type: GEM/opusparcus
          name: MTEB OpusparcusPC (en)
          config: en
          split: test
          revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
        metrics:
          - type: cos_sim_accuracy
            value: 99.89816700610999
          - type: cos_sim_ap
            value: 100
          - type: cos_sim_f1
            value: 99.9490575649516
          - type: cos_sim_precision
            value: 100
          - type: cos_sim_recall
            value: 99.89816700610999
          - type: dot_accuracy
            value: 99.89816700610999
          - type: dot_ap
            value: 100
          - type: dot_f1
            value: 99.9490575649516
          - type: dot_precision
            value: 100
          - type: dot_recall
            value: 99.89816700610999
          - type: euclidean_accuracy
            value: 99.89816700610999
          - type: euclidean_ap
            value: 100
          - type: euclidean_f1
            value: 99.9490575649516
          - type: euclidean_precision
            value: 100
          - type: euclidean_recall
            value: 99.89816700610999
          - type: manhattan_accuracy
            value: 99.89816700610999
          - type: manhattan_ap
            value: 100
          - type: manhattan_f1
            value: 99.9490575649516
          - type: manhattan_precision
            value: 100
          - type: manhattan_recall
            value: 99.89816700610999
          - type: max_accuracy
            value: 99.89816700610999
          - type: max_ap
            value: 100
          - type: max_f1
            value: 99.9490575649516
      - task:
          type: PairClassification
        dataset:
          type: paws-x
          name: MTEB PawsX (en)
          config: en
          split: test
          revision: 8a04d940a42cd40658986fdd8e3da561533a3646
        metrics:
          - type: cos_sim_accuracy
            value: 62
          - type: cos_sim_ap
            value: 62.26837791655737
          - type: cos_sim_f1
            value: 62.607449856733524
          - type: cos_sim_precision
            value: 46.36604774535809
          - type: cos_sim_recall
            value: 96.36163175303197
          - type: dot_accuracy
            value: 62
          - type: dot_ap
            value: 62.26736459439965
          - type: dot_f1
            value: 62.607449856733524
          - type: dot_precision
            value: 46.36604774535809
          - type: dot_recall
            value: 96.36163175303197
          - type: euclidean_accuracy
            value: 62
          - type: euclidean_ap
            value: 62.26826112548132
          - type: euclidean_f1
            value: 62.607449856733524
          - type: euclidean_precision
            value: 46.36604774535809
          - type: euclidean_recall
            value: 96.36163175303197
          - type: manhattan_accuracy
            value: 62
          - type: manhattan_ap
            value: 62.26223761507973
          - type: manhattan_f1
            value: 62.585034013605444
          - type: manhattan_precision
            value: 46.34146341463415
          - type: manhattan_recall
            value: 96.36163175303197
          - type: max_accuracy
            value: 62
          - type: max_ap
            value: 62.26837791655737
          - type: max_f1
            value: 62.607449856733524
      - task:
          type: Retrieval
        dataset:
          type: mteb/quora
          name: MTEB QuoraRetrieval
          config: default
          split: test
          revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
        metrics:
          - type: map_at_1
            value: 69.90899999999999
          - type: map_at_10
            value: 83.56700000000001
          - type: map_at_100
            value: 84.19200000000001
          - type: map_at_1000
            value: 84.212
          - type: map_at_3
            value: 80.658
          - type: map_at_5
            value: 82.473
          - type: mrr_at_1
            value: 80.4
          - type: mrr_at_10
            value: 86.699
          - type: mrr_at_100
            value: 86.798
          - type: mrr_at_1000
            value: 86.80099999999999
          - type: mrr_at_3
            value: 85.677
          - type: mrr_at_5
            value: 86.354
          - type: ndcg_at_1
            value: 80.43
          - type: ndcg_at_10
            value: 87.41
          - type: ndcg_at_100
            value: 88.653
          - type: ndcg_at_1000
            value: 88.81599999999999
          - type: ndcg_at_3
            value: 84.516
          - type: ndcg_at_5
            value: 86.068
          - type: precision_at_1
            value: 80.43
          - type: precision_at_10
            value: 13.234000000000002
          - type: precision_at_100
            value: 1.513
          - type: precision_at_1000
            value: 0.156
          - type: precision_at_3
            value: 36.93
          - type: precision_at_5
            value: 24.26
          - type: recall_at_1
            value: 69.90899999999999
          - type: recall_at_10
            value: 94.687
          - type: recall_at_100
            value: 98.96000000000001
          - type: recall_at_1000
            value: 99.79599999999999
          - type: recall_at_3
            value: 86.25699999999999
          - type: recall_at_5
            value: 90.70700000000001
      - task:
          type: Clustering
        dataset:
          type: mteb/reddit-clustering
          name: MTEB RedditClustering
          config: default
          split: test
          revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
        metrics:
          - type: v_measure
            value: 46.02256865360266
      - task:
          type: Clustering
        dataset:
          type: mteb/reddit-clustering-p2p
          name: MTEB RedditClusteringP2P
          config: default
          split: test
          revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
        metrics:
          - type: v_measure
            value: 62.43157528757563
      - task:
          type: Retrieval
        dataset:
          type: mteb/scidocs
          name: MTEB SCIDOCS
          config: default
          split: test
          revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
        metrics:
          - type: map_at_1
            value: 5.093
          - type: map_at_10
            value: 12.982
          - type: map_at_100
            value: 15.031
          - type: map_at_1000
            value: 15.334
          - type: map_at_3
            value: 9.339
          - type: map_at_5
            value: 11.183
          - type: mrr_at_1
            value: 25.1
          - type: mrr_at_10
            value: 36.257
          - type: mrr_at_100
            value: 37.351
          - type: mrr_at_1000
            value: 37.409
          - type: mrr_at_3
            value: 33.050000000000004
          - type: mrr_at_5
            value: 35.205
          - type: ndcg_at_1
            value: 25.1
          - type: ndcg_at_10
            value: 21.361
          - type: ndcg_at_100
            value: 29.396
          - type: ndcg_at_1000
            value: 34.849999999999994
          - type: ndcg_at_3
            value: 20.704
          - type: ndcg_at_5
            value: 18.086
          - type: precision_at_1
            value: 25.1
          - type: precision_at_10
            value: 10.94
          - type: precision_at_100
            value: 2.257
          - type: precision_at_1000
            value: 0.358
          - type: precision_at_3
            value: 19.467000000000002
          - type: precision_at_5
            value: 15.98
          - type: recall_at_1
            value: 5.093
          - type: recall_at_10
            value: 22.177
          - type: recall_at_100
            value: 45.842
          - type: recall_at_1000
            value: 72.598
          - type: recall_at_3
            value: 11.833
          - type: recall_at_5
            value: 16.173000000000002
      - task:
          type: STS
        dataset:
          type: mteb/sickr-sts
          name: MTEB SICK-R
          config: default
          split: test
          revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
        metrics:
          - type: cos_sim_pearson
            value: 73.56535226754596
          - type: cos_sim_spearman
            value: 69.32425977603488
          - type: euclidean_pearson
            value: 71.32425703470898
          - type: euclidean_spearman
            value: 69.32425217267013
          - type: manhattan_pearson
            value: 71.25897281394246
          - type: manhattan_spearman
            value: 69.27132577049578
      - task:
          type: STS
        dataset:
          type: mteb/sts12-sts
          name: MTEB STS12
          config: default
          split: test
          revision: a0d554a64d88156834ff5ae9920b964011b16384
        metrics:
          - type: cos_sim_pearson
            value: 69.66387868726018
          - type: cos_sim_spearman
            value: 67.85470749045027
          - type: euclidean_pearson
            value: 66.62075098063795
          - type: euclidean_spearman
            value: 67.85470749045027
          - type: manhattan_pearson
            value: 66.61455061901262
          - type: manhattan_spearman
            value: 67.87229618498695
      - task:
          type: STS
        dataset:
          type: mteb/sts13-sts
          name: MTEB STS13
          config: default
          split: test
          revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
        metrics:
          - type: cos_sim_pearson
            value: 75.65731331392575
          - type: cos_sim_spearman
            value: 77.48991626780108
          - type: euclidean_pearson
            value: 77.19884738623692
          - type: euclidean_spearman
            value: 77.48985836619045
          - type: manhattan_pearson
            value: 77.0656684243772
          - type: manhattan_spearman
            value: 77.30289226582691
      - task:
          type: STS
        dataset:
          type: mteb/sts14-sts
          name: MTEB STS14
          config: default
          split: test
          revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
        metrics:
          - type: cos_sim_pearson
            value: 69.37003253666457
          - type: cos_sim_spearman
            value: 69.77157648098141
          - type: euclidean_pearson
            value: 69.39543876030432
          - type: euclidean_spearman
            value: 69.77157648098141
          - type: manhattan_pearson
            value: 69.29901600459745
          - type: manhattan_spearman
            value: 69.65074167527128
      - task:
          type: STS
        dataset:
          type: mteb/sts15-sts
          name: MTEB STS15
          config: default
          split: test
          revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
        metrics:
          - type: cos_sim_pearson
            value: 78.56777256540136
          - type: cos_sim_spearman
            value: 80.16458787843023
          - type: euclidean_pearson
            value: 80.16475730686916
          - type: euclidean_spearman
            value: 80.16458787843023
          - type: manhattan_pearson
            value: 80.12814463670401
          - type: manhattan_spearman
            value: 80.1357907984809
      - task:
          type: STS
        dataset:
          type: mteb/sts16-sts
          name: MTEB STS16
          config: default
          split: test
          revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
        metrics:
          - type: cos_sim_pearson
            value: 76.09572350919031
          - type: cos_sim_spearman
            value: 77.94490233429326
          - type: euclidean_pearson
            value: 78.36595251203524
          - type: euclidean_spearman
            value: 77.94490233429326
          - type: manhattan_pearson
            value: 78.41538768125166
          - type: manhattan_spearman
            value: 78.01244379569542
      - 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: 80.7843552187951
          - type: cos_sim_spearman
            value: 82.28085055047386
          - type: euclidean_pearson
            value: 82.37373672515267
          - type: euclidean_spearman
            value: 82.28085055047386
          - type: manhattan_pearson
            value: 82.39387241346917
          - type: manhattan_spearman
            value: 82.36503339515906
      - task:
          type: STS
        dataset:
          type: mteb/sts22-crosslingual-sts
          name: MTEB STS22 (en)
          config: en
          split: test
          revision: eea2b4fe26a775864c896887d910b76a8098ad3f
        metrics:
          - type: cos_sim_pearson
            value: 68.29963929962095
          - type: cos_sim_spearman
            value: 67.96868942546051
          - type: euclidean_pearson
            value: 68.93524903869285
          - type: euclidean_spearman
            value: 67.96868942546051
          - type: manhattan_pearson
            value: 68.79144468444811
          - type: manhattan_spearman
            value: 67.69311483884324
      - task:
          type: STS
        dataset:
          type: mteb/stsbenchmark-sts
          name: MTEB STSBenchmark
          config: default
          split: test
          revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
        metrics:
          - type: cos_sim_pearson
            value: 72.84789696700685
          - type: cos_sim_spearman
            value: 75.67875747588545
          - type: euclidean_pearson
            value: 75.07752300463038
          - type: euclidean_spearman
            value: 75.67875747588545
          - type: manhattan_pearson
            value: 74.97934248140928
          - type: manhattan_spearman
            value: 75.62525644178724
      - task:
          type: STS
        dataset:
          type: PhilipMay/stsb_multi_mt
          name: MTEB STSBenchmarkMultilingualSTS (en)
          config: en
          split: test
          revision: 93d57ef91790589e3ce9c365164337a8a78b7632
        metrics:
          - type: cos_sim_pearson
            value: 72.84789702519309
          - type: cos_sim_spearman
            value: 75.67875747588545
          - type: euclidean_pearson
            value: 75.07752310061133
          - type: euclidean_spearman
            value: 75.67875747588545
          - type: manhattan_pearson
            value: 74.97934257159595
          - type: manhattan_spearman
            value: 75.62525644178724
      - task:
          type: Reranking
        dataset:
          type: mteb/scidocs-reranking
          name: MTEB SciDocsRR
          config: default
          split: test
          revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
        metrics:
          - type: map
            value: 81.55557720431086
          - type: mrr
            value: 94.91178665198272
      - task:
          type: Retrieval
        dataset:
          type: mteb/scifact
          name: MTEB SciFact
          config: default
          split: test
          revision: 0228b52cf27578f30900b9e5271d331663a030d7
        metrics:
          - type: map_at_1
            value: 59.260999999999996
          - type: map_at_10
            value: 69.36099999999999
          - type: map_at_100
            value: 69.868
          - type: map_at_1000
            value: 69.877
          - type: map_at_3
            value: 66.617
          - type: map_at_5
            value: 68.061
          - type: mrr_at_1
            value: 62.333000000000006
          - type: mrr_at_10
            value: 70.533
          - type: mrr_at_100
            value: 70.966
          - type: mrr_at_1000
            value: 70.975
          - type: mrr_at_3
            value: 68.667
          - type: mrr_at_5
            value: 69.717
          - type: ndcg_at_1
            value: 62.333000000000006
          - type: ndcg_at_10
            value: 73.82300000000001
          - type: ndcg_at_100
            value: 76.122
          - type: ndcg_at_1000
            value: 76.374
          - type: ndcg_at_3
            value: 69.27499999999999
          - type: ndcg_at_5
            value: 71.33
          - type: precision_at_1
            value: 62.333000000000006
          - type: precision_at_10
            value: 9.8
          - type: precision_at_100
            value: 1.097
          - type: precision_at_1000
            value: 0.11199999999999999
          - type: precision_at_3
            value: 26.889000000000003
          - type: precision_at_5
            value: 17.599999999999998
          - type: recall_at_1
            value: 59.260999999999996
          - type: recall_at_10
            value: 86.2
          - type: recall_at_100
            value: 96.667
          - type: recall_at_1000
            value: 98.667
          - type: recall_at_3
            value: 74.006
          - type: recall_at_5
            value: 79.167
      - task:
          type: PairClassification
        dataset:
          type: mteb/sprintduplicatequestions-pairclassification
          name: MTEB SprintDuplicateQuestions
          config: default
          split: test
          revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
        metrics:
          - type: cos_sim_accuracy
            value: 99.81881188118813
          - type: cos_sim_ap
            value: 95.20169041096409
          - type: cos_sim_f1
            value: 90.76224129227664
          - type: cos_sim_precision
            value: 91.64118246687055
          - type: cos_sim_recall
            value: 89.9
          - type: dot_accuracy
            value: 99.81881188118813
          - type: dot_ap
            value: 95.20169041096409
          - type: dot_f1
            value: 90.76224129227664
          - type: dot_precision
            value: 91.64118246687055
          - type: dot_recall
            value: 89.9
          - type: euclidean_accuracy
            value: 99.81881188118813
          - type: euclidean_ap
            value: 95.2016904109641
          - type: euclidean_f1
            value: 90.76224129227664
          - type: euclidean_precision
            value: 91.64118246687055
          - type: euclidean_recall
            value: 89.9
          - type: manhattan_accuracy
            value: 99.81881188118813
          - type: manhattan_ap
            value: 95.22680188132777
          - type: manhattan_f1
            value: 90.79013588324108
          - type: manhattan_precision
            value: 91.38804457953394
          - type: manhattan_recall
            value: 90.2
          - type: max_accuracy
            value: 99.81881188118813
          - type: max_ap
            value: 95.22680188132777
          - type: max_f1
            value: 90.79013588324108
      - task:
          type: Clustering
        dataset:
          type: mteb/stackexchange-clustering
          name: MTEB StackExchangeClustering
          config: default
          split: test
          revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
        metrics:
          - type: v_measure
            value: 57.8638628701308
      - task:
          type: Clustering
        dataset:
          type: mteb/stackexchange-clustering-p2p
          name: MTEB StackExchangeClusteringP2P
          config: default
          split: test
          revision: 815ca46b2622cec33ccafc3735d572c266efdb44
        metrics:
          - type: v_measure
            value: 37.82028248106046
      - task:
          type: Reranking
        dataset:
          type: mteb/stackoverflowdupquestions-reranking
          name: MTEB StackOverflowDupQuestions
          config: default
          split: test
          revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
        metrics:
          - type: map
            value: 50.870860210170946
          - type: mrr
            value: 51.608084521687466
      - task:
          type: Summarization
        dataset:
          type: mteb/summeval
          name: MTEB SummEval
          config: default
          split: test
          revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
        metrics:
          - type: cos_sim_pearson
            value: 31.60384207444685
          - type: cos_sim_spearman
            value: 30.84047452209471
          - type: dot_pearson
            value: 31.60384104417333
          - type: dot_spearman
            value: 30.84047452209471
      - task:
          type: Retrieval
        dataset:
          type: mteb/trec-covid
          name: MTEB TRECCOVID
          config: default
          split: test
          revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
        metrics:
          - type: map_at_1
            value: 0.246
          - type: map_at_10
            value: 2.051
          - type: map_at_100
            value: 13.129
          - type: map_at_1000
            value: 31.56
          - type: map_at_3
            value: 0.681
          - type: map_at_5
            value: 1.105
          - type: mrr_at_1
            value: 94
          - type: mrr_at_10
            value: 97
          - type: mrr_at_100
            value: 97
          - type: mrr_at_1000
            value: 97
          - type: mrr_at_3
            value: 97
          - type: mrr_at_5
            value: 97
          - type: ndcg_at_1
            value: 87
          - type: ndcg_at_10
            value: 80.716
          - type: ndcg_at_100
            value: 63.83
          - type: ndcg_at_1000
            value: 56.215
          - type: ndcg_at_3
            value: 84.531
          - type: ndcg_at_5
            value: 84.777
          - type: precision_at_1
            value: 94
          - type: precision_at_10
            value: 84.6
          - type: precision_at_100
            value: 66.03999999999999
          - type: precision_at_1000
            value: 24.878
          - type: precision_at_3
            value: 88.667
          - type: precision_at_5
            value: 89.60000000000001
          - type: recall_at_1
            value: 0.246
          - type: recall_at_10
            value: 2.2079999999999997
          - type: recall_at_100
            value: 15.895999999999999
          - type: recall_at_1000
            value: 52.683
          - type: recall_at_3
            value: 0.7040000000000001
          - type: recall_at_5
            value: 1.163
      - task:
          type: Retrieval
        dataset:
          type: mteb/touche2020
          name: MTEB Touche2020
          config: default
          split: test
          revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
        metrics:
          - type: map_at_1
            value: 3.852
          - type: map_at_10
            value: 14.316
          - type: map_at_100
            value: 20.982
          - type: map_at_1000
            value: 22.58
          - type: map_at_3
            value: 7.767
          - type: map_at_5
            value: 10.321
          - type: mrr_at_1
            value: 51.019999999999996
          - type: mrr_at_10
            value: 66.365
          - type: mrr_at_100
            value: 66.522
          - type: mrr_at_1000
            value: 66.522
          - type: mrr_at_3
            value: 62.925
          - type: mrr_at_5
            value: 64.762
          - type: ndcg_at_1
            value: 46.939
          - type: ndcg_at_10
            value: 34.516999999999996
          - type: ndcg_at_100
            value: 44.25
          - type: ndcg_at_1000
            value: 54.899
          - type: ndcg_at_3
            value: 40.203
          - type: ndcg_at_5
            value: 37.004
          - type: precision_at_1
            value: 51.019999999999996
          - type: precision_at_10
            value: 29.796
          - type: precision_at_100
            value: 8.633000000000001
          - type: precision_at_1000
            value: 1.584
          - type: precision_at_3
            value: 40.816
          - type: precision_at_5
            value: 35.918
          - type: recall_at_1
            value: 3.852
          - type: recall_at_10
            value: 20.891000000000002
          - type: recall_at_100
            value: 52.428
          - type: recall_at_1000
            value: 84.34899999999999
          - type: recall_at_3
            value: 8.834
          - type: recall_at_5
            value: 12.909
      - task:
          type: Classification
        dataset:
          type: mteb/toxic_conversations_50k
          name: MTEB ToxicConversationsClassification
          config: default
          split: test
          revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
        metrics:
          - type: accuracy
            value: 64.7092
          - type: ap
            value: 11.972915012305819
          - type: f1
            value: 49.91050149892115
      - task:
          type: Classification
        dataset:
          type: mteb/tweet_sentiment_extraction
          name: MTEB TweetSentimentExtractionClassification
          config: default
          split: test
          revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
        metrics:
          - type: accuracy
            value: 56.737408036219584
          - type: f1
            value: 57.07235266246011
      - task:
          type: Clustering
        dataset:
          type: mteb/twentynewsgroups-clustering
          name: MTEB TwentyNewsgroupsClustering
          config: default
          split: test
          revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
        metrics:
          - type: v_measure
            value: 35.9147539025798
      - task:
          type: PairClassification
        dataset:
          type: mteb/twittersemeval2015-pairclassification
          name: MTEB TwitterSemEval2015
          config: default
          split: test
          revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
        metrics:
          - type: cos_sim_accuracy
            value: 82.52369315133814
          - type: cos_sim_ap
            value: 62.34858091376534
          - type: cos_sim_f1
            value: 58.18225190839694
          - type: cos_sim_precision
            value: 53.09098824553766
          - type: cos_sim_recall
            value: 64.35356200527704
          - type: dot_accuracy
            value: 82.52369315133814
          - type: dot_ap
            value: 62.34857753814992
          - type: dot_f1
            value: 58.18225190839694
          - type: dot_precision
            value: 53.09098824553766
          - type: dot_recall
            value: 64.35356200527704
          - type: euclidean_accuracy
            value: 82.52369315133814
          - type: euclidean_ap
            value: 62.34857756663386
          - type: euclidean_f1
            value: 58.18225190839694
          - type: euclidean_precision
            value: 53.09098824553766
          - type: euclidean_recall
            value: 64.35356200527704
          - type: manhattan_accuracy
            value: 82.49389044525243
          - type: manhattan_ap
            value: 62.32245347238179
          - type: manhattan_f1
            value: 58.206309819213054
          - type: manhattan_precision
            value: 52.70704044511021
          - type: manhattan_recall
            value: 64.9868073878628
          - type: max_accuracy
            value: 82.52369315133814
          - type: max_ap
            value: 62.34858091376534
          - type: max_f1
            value: 58.206309819213054
      - task:
          type: PairClassification
        dataset:
          type: mteb/twitterurlcorpus-pairclassification
          name: MTEB TwitterURLCorpus
          config: default
          split: test
          revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
        metrics:
          - type: cos_sim_accuracy
            value: 88.34555827220863
          - type: cos_sim_ap
            value: 84.84152481680071
          - type: cos_sim_f1
            value: 76.860456739428
          - type: cos_sim_precision
            value: 72.21470150263978
          - type: cos_sim_recall
            value: 82.14505697566985
          - type: dot_accuracy
            value: 88.34555827220863
          - type: dot_ap
            value: 84.84152743322608
          - type: dot_f1
            value: 76.860456739428
          - type: dot_precision
            value: 72.21470150263978
          - type: dot_recall
            value: 82.14505697566985
          - type: euclidean_accuracy
            value: 88.34555827220863
          - type: euclidean_ap
            value: 84.84152589453169
          - type: euclidean_f1
            value: 76.860456739428
          - type: euclidean_precision
            value: 72.21470150263978
          - type: euclidean_recall
            value: 82.14505697566985
          - type: manhattan_accuracy
            value: 88.38242713548337
          - type: manhattan_ap
            value: 84.8112124970968
          - type: manhattan_f1
            value: 76.83599206057487
          - type: manhattan_precision
            value: 73.51244900829934
          - type: manhattan_recall
            value: 80.47428395441946
          - type: max_accuracy
            value: 88.38242713548337
          - type: max_ap
            value: 84.84152743322608
          - type: max_f1
            value: 76.860456739428
      - task:
          type: Clustering
        dataset:
          type: jinaai/cities_wiki_clustering
          name: MTEB WikiCitiesClustering
          config: default
          split: test
          revision: ddc9ee9242fa65332597f70e967ecc38b9d734fa
        metrics:
          - type: v_measure
            value: 85.5314389263015
new_version: Snowflake/snowflake-arctic-embed-l-v2.0

Snowflake's Arctic-embed-l

News | Models | Usage | Evaluation | Contact | FAQ License | Acknowledgement

News

12/04/2024: Release of snowflake-arctic-embed-l-v2.0 and snowflake-arctic-embed-m-v2.0 our newest models with multilingual workloads in mind. These models outperform prior versions of Arctic Embed and we suggest these replace prior versions!

07/26/2024: Release preprint [2407.18887] Embedding And Clustering Your Data Can Improve Contrastive Pretraining on arXiv.

07/18/2024: Release of snowflake-arctic-embed-m-v1.5, capable of producing highly compressible embedding vectors that preserve quality even when squished as small as 128 bytes per vector. Details about the development of this model are available in the launch post on the Snowflake engineering blog.

05/10/2024: Release the technical report on Arctic Embed

04/16/2024: Release the ** snowflake-arctic-embed ** family of text embedding models. The releases are state-of-the-art for Retrieval quality at each of their representative size profiles. Technical Report is coming shortly. For more details, please refer to our Github: Arctic-Text-Embed.

Models

snowflake-arctic-embed is a suite of text embedding models that focuses on creating high-quality retrieval models optimized for performance.

The snowflake-arctic-embedding models achieve state-of-the-art performance on the MTEB/BEIR leaderboard for each of their size variants. Evaluation is performed using these scripts. As shown below, each class of model size achieves SOTA retrieval accuracy compared to other top models.

The models are trained by leveraging existing open-source text representation models, such as bert-base-uncased, and are trained in a multi-stage pipeline to optimize their retrieval performance. First, the models are trained with large batches of query-document pairs where negatives are derived in-batch—pretraining leverages about 400m samples of a mix of public datasets and proprietary web search data. Following pretraining models are further optimized with long training on a smaller dataset (about 1m samples) of triplets of query, positive document, and negative document derived from hard harmful mining. Mining of the negatives and data curation is crucial to retrieval accuracy. A detailed technical report can be found here.

Name MTEB Retrieval Score (NDCG @ 10) Parameters (Millions) Embedding Dimension
snowflake-arctic-embed-xs 50.15 22 384
snowflake-arctic-embed-s 51.98 33 384
snowflake-arctic-embed-m 54.90 110 768
snowflake-arctic-embed-m-long 54.83 137 768
snowflake-arctic-embed-l 55.98 335 1024

Aside from being great open-source models, the largest model, snowflake-arctic-embed-l, can serve as a natural replacement for closed-source embedding, as shown below.

Model Name MTEB Retrieval Score (NDCG @ 10)
snowflake-arctic-embed-l 55.98
Google-gecko-text-embedding 55.7
text-embedding-3-large 55.44
Cohere-embed-english-v3.0 55.00
bge-large-en-v1.5 54.29

snowflake-arctic-embed-xs

This tiny model packs quite the punch. Based on the all-MiniLM-L6-v2 model with only 22m parameters and 384 dimensions, this model should meet even the strictest latency/TCO budgets. Despite its size, its retrieval accuracy is closer to that of models with 100m paramers.

Model Name MTEB Retrieval Score (NDCG @ 10)
snowflake-arctic-embed-xs 50.15
GIST-all-MiniLM-L6-v2 45.12
gte-tiny 44.92
all-MiniLM-L6-v2 41.95
bge-micro-v2 42.56

snowflake-arctic-embed-s

Based on the intfloat/e5-small-unsupervised model, this small model does not trade off retrieval accuracy for its small size. With only 33m parameters and 384 dimensions, this model should easily allow scaling to large datasets.

Model Name MTEB Retrieval Score (NDCG @ 10)
snowflake-arctic-embed-s 51.98
bge-small-en-v1.5 51.68
Cohere-embed-english-light-v3.0 51.34
text-embedding-3-small 51.08
e5-small-v2 49.04

snowflake-arctic-embed-m

Based on the intfloat/e5-base-unsupervised model, this medium model is the workhorse that provides the best retrieval performance without slowing down inference.

Model Name MTEB Retrieval Score (NDCG @ 10)
snowflake-arctic-embed-m 54.90
bge-base-en-v1.5 53.25
nomic-embed-text-v1.5 53.25
GIST-Embedding-v0 52.31
gte-base 52.31

snowflake-arctic-embed-m-long

Based on the nomic-ai/nomic-embed-text-v1-unsupervised model, this long-context variant of our medium-sized model is perfect for workloads that can be constrained by the regular 512 token context of our other models. Without the use of RPE, this model supports up to 2048 tokens. With RPE, it can scale to 8192!

Model Name MTEB Retrieval Score (NDCG @ 10)
snowflake-arctic-embed-m-long 54.83
nomic-embed-text-v1.5 53.01
nomic-embed-text-v1 52.81

snowflake-arctic-embed-l

Based on the intfloat/e5-large-unsupervised model, this large model is a direct drop-in for closed APIs and delivers the most accurate retrieval experience.

Model Name MTEB Retrieval Score (NDCG @ 10)
snowflake-arctic-embed-l 55.98
UAE-Large-V1 54.66
bge-large-en-v1.5 54.29
mxbai-embed-large-v1 54.39
e5-Large-v2 50.56

Usage

Using Sentence Transformers

You can use the sentence-transformers package to use an snowflake-arctic-embed model, as shown below.

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("Snowflake/snowflake-arctic-embed-l")

queries = ['what is snowflake?', 'Where can I get the best tacos?']
documents = ['The Data Cloud!', 'Mexico City of Course!']

query_embeddings = model.encode(queries, prompt_name="query")
document_embeddings = model.encode(documents)

scores = query_embeddings @ document_embeddings.T
for query, query_scores in zip(queries, scores):
    doc_score_pairs = list(zip(documents, query_scores))
    doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
    # Output passages & scores
    print("Query:", query)
    for document, score in doc_score_pairs:
        print(score, document)
Query: what is snowflake?
0.28976774 The Data Cloud!
0.19071159 Mexico City of Course!
Query: Where can I get the best tacos?
0.38650584 Mexico City of Course!
0.25145516 The Data Cloud!

Using Huggingface transformers

You can use the transformers package to use an snowflake-arctic-embed model, as shown below. For optimal retrieval quality, use the CLS token to embed each text portion and use the query prefix below (just on the query).

import torch
from transformers import AutoModel, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained('Snowflake/snowflake-arctic-embed-l')
model = AutoModel.from_pretrained('Snowflake/snowflake-arctic-embed-l', add_pooling_layer=False)
model.eval()

query_prefix = 'Represent this sentence for searching relevant passages: '
queries  = ['what is snowflake?', 'Where can I get the best tacos?']
queries_with_prefix = ["{}{}".format(query_prefix, i) for i in queries]
query_tokens = tokenizer(queries_with_prefix, padding=True, truncation=True, return_tensors='pt', max_length=512)

documents = ['The Data Cloud!', 'Mexico City of Course!']
document_tokens =  tokenizer(documents, padding=True, truncation=True, return_tensors='pt', max_length=512)

# Compute token embeddings
with torch.no_grad():
    query_embeddings = model(**query_tokens)[0][:, 0]
    document_embeddings = model(**document_tokens)[0][:, 0]


# normalize embeddings
query_embeddings = torch.nn.functional.normalize(query_embeddings, p=2, dim=1)
document_embeddings = torch.nn.functional.normalize(document_embeddings, p=2, dim=1)

scores = torch.mm(query_embeddings, document_embeddings.transpose(0, 1))
for query, query_scores in zip(queries, scores):
    doc_score_pairs = list(zip(documents, query_scores))
    doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
    #Output passages & scores
    print("Query:", query)
    for document, score in doc_score_pairs:
        print(score, document)

Using Transformers.js

If you haven't already, you can install the Transformers.js JavaScript library from NPM by running:

npm i @xenova/transformers

You can then use the model to compute embeddings as follows:

import { pipeline, dot } from '@xenova/transformers';

// Create feature extraction pipeline
const extractor = await pipeline('feature-extraction', 'Snowflake/snowflake-arctic-embed-l', {
    quantized: false, // Comment out this line to use the quantized version
});

// Generate sentence embeddings
const sentences = [
    'Represent this sentence for searching relevant passages: Where can I get the best tacos?',
    'The Data Cloud!',
    'Mexico City of Course!',
]
const output = await extractor(sentences, { normalize: true, pooling: 'cls' });

// Compute similarity scores
const [source_embeddings, ...document_embeddings ] = output.tolist();
const similarities = document_embeddings.map(x => dot(source_embeddings, x));
console.log(similarities); // [0.25145517380846977, 0.3865060421197194]

Using Infinity

OpenAI compatible API deployment with Infinity and Docker.

docker run --gpus all -v $PWD/data:/app/.cache -p "7997":"7997" \
michaelf34/infinity:0.0.70 \
v2 --model-id Snowflake/snowflake-arctic-embed-l --dtype float16 --batch-size 32 --engine torch --port 7997

FAQ

TBD

Contact

Feel free to open an issue or pull request if you have any questions or suggestions about this project. You also can email Daniel Campos([email protected]).

License

Arctic is licensed under the Apache-2. The released models can be used for commercial purposes free of charge.

Acknowledgement

We want to thank the open-source community, which has provided the great building blocks upon which we could make our models. We thank our modeling engineers, Danmei Xu, Luke Merrick, Gaurav Nuti, and Daniel Campos, for making these great models possible. We thank our leadership, Himabindu Pucha, Kelvin So, Vivek Raghunathan, and Sridhar Ramaswamy, for supporting this work. We also thank the open-source community for producing the great models we could build on top of and making these releases possible. Finally, we thank the researchers who created BEIR and MTEB benchmarks. It is largely thanks to their tireless work to define what better looks like that we could improve model performance.