README.md

metadata

tags:
  - mteb
  - sentence-transformers
  - transformers
  - Qwen2
  - sentence-similarity
license: apache-2.0
model-index:
  - name: gte-qwen2-7B-instruct
    results:
      - dataset:
          config: en
          name: MTEB AmazonCounterfactualClassification (en)
          revision: e8379541af4e31359cca9fbcf4b00f2671dba205
          split: test
          type: mteb/amazon_counterfactual
        metrics:
          - type: accuracy
            value: 83.98507462686567
          - type: ap
            value: 50.93015252587014
          - type: f1
            value: 78.50416599051215
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB AmazonPolarityClassification
          revision: e2d317d38cd51312af73b3d32a06d1a08b442046
          split: test
          type: mteb/amazon_polarity
        metrics:
          - type: accuracy
            value: 96.61065
          - type: ap
            value: 94.89174052954196
          - type: f1
            value: 96.60942596940565
        task:
          type: Classification
      - dataset:
          config: en
          name: MTEB AmazonReviewsClassification (en)
          revision: 1399c76144fd37290681b995c656ef9b2e06e26d
          split: test
          type: mteb/amazon_reviews_multi
        metrics:
          - type: accuracy
            value: 55.614000000000004
          - type: f1
            value: 54.90553480294904
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB ArguAna
          revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
          split: test
          type: mteb/arguana
        metrics:
          - type: map_at_1
            value: 45.164
          - type: map_at_10
            value: 61.519
          - type: map_at_100
            value: 61.769
          - type: map_at_1000
            value: 61.769
          - type: map_at_3
            value: 57.443999999999996
          - type: map_at_5
            value: 60.058
          - type: mrr_at_1
            value: 46.088
          - type: mrr_at_10
            value: 61.861
          - type: mrr_at_100
            value: 62.117999999999995
          - type: mrr_at_1000
            value: 62.117999999999995
          - type: mrr_at_3
            value: 57.729
          - type: mrr_at_5
            value: 60.392
          - type: ndcg_at_1
            value: 45.164
          - type: ndcg_at_10
            value: 69.72
          - type: ndcg_at_100
            value: 70.719
          - type: ndcg_at_1000
            value: 70.719
          - type: ndcg_at_3
            value: 61.517999999999994
          - type: ndcg_at_5
            value: 66.247
          - type: precision_at_1
            value: 45.164
          - type: precision_at_10
            value: 9.545
          - type: precision_at_100
            value: 0.996
          - type: precision_at_1000
            value: 0.1
          - type: precision_at_3
            value: 24.443
          - type: precision_at_5
            value: 16.97
          - type: recall_at_1
            value: 45.164
          - type: recall_at_10
            value: 95.448
          - type: recall_at_100
            value: 99.644
          - type: recall_at_1000
            value: 99.644
          - type: recall_at_3
            value: 73.329
          - type: recall_at_5
            value: 84.851
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB ArxivClusteringP2P
          revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
          split: test
          type: mteb/arxiv-clustering-p2p
        metrics:
          - type: v_measure
            value: 50.511868162026175
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB ArxivClusteringS2S
          revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
          split: test
          type: mteb/arxiv-clustering-s2s
        metrics:
          - type: v_measure
            value: 45.007803189284004
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB AskUbuntuDupQuestions
          revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
          split: test
          type: mteb/askubuntudupquestions-reranking
        metrics:
          - type: map
            value: 64.55292107723382
          - type: mrr
            value: 77.66158818097877
        task:
          type: Reranking
      - dataset:
          config: default
          name: MTEB BIOSSES
          revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
          split: test
          type: mteb/biosses-sts
        metrics:
          - type: cos_sim_pearson
            value: 85.65459047085452
          - type: cos_sim_spearman
            value: 82.10729255710761
          - type: euclidean_pearson
            value: 82.78079159312476
          - type: euclidean_spearman
            value: 80.50002701880933
          - type: manhattan_pearson
            value: 82.41372641383016
          - type: manhattan_spearman
            value: 80.57412509272639
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB Banking77Classification
          revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
          split: test
          type: mteb/banking77
        metrics:
          - type: accuracy
            value: 87.30844155844156
          - type: f1
            value: 87.25307322443255
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB BiorxivClusteringP2P
          revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
          split: test
          type: mteb/biorxiv-clustering-p2p
        metrics:
          - type: v_measure
            value: 43.20754608934859
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB BiorxivClusteringS2S
          revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
          split: test
          type: mteb/biorxiv-clustering-s2s
        metrics:
          - type: v_measure
            value: 38.818037697335505
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB CQADupstackAndroidRetrieval
          revision: f46a197baaae43b4f621051089b82a364682dfeb
          split: test
          type: BeIR/cqadupstack
        metrics:
          - type: map_at_1
            value: 35.423
          - type: map_at_10
            value: 47.198
          - type: map_at_100
            value: 48.899
          - type: map_at_1000
            value: 49.004
          - type: map_at_3
            value: 43.114999999999995
          - type: map_at_5
            value: 45.491
          - type: mrr_at_1
            value: 42.918
          - type: mrr_at_10
            value: 53.299
          - type: mrr_at_100
            value: 54.032000000000004
          - type: mrr_at_1000
            value: 54.055
          - type: mrr_at_3
            value: 50.453
          - type: mrr_at_5
            value: 52.205999999999996
          - type: ndcg_at_1
            value: 42.918
          - type: ndcg_at_10
            value: 53.98
          - type: ndcg_at_100
            value: 59.57
          - type: ndcg_at_1000
            value: 60.879000000000005
          - type: ndcg_at_3
            value: 48.224000000000004
          - type: ndcg_at_5
            value: 50.998
          - type: precision_at_1
            value: 42.918
          - type: precision_at_10
            value: 10.299999999999999
          - type: precision_at_100
            value: 1.687
          - type: precision_at_1000
            value: 0.211
          - type: precision_at_3
            value: 22.842000000000002
          - type: precision_at_5
            value: 16.681
          - type: recall_at_1
            value: 35.423
          - type: recall_at_10
            value: 66.824
          - type: recall_at_100
            value: 89.564
          - type: recall_at_1000
            value: 97.501
          - type: recall_at_3
            value: 50.365
          - type: recall_at_5
            value: 57.921
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB CQADupstackEnglishRetrieval
          revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
          split: test
          type: BeIR/cqadupstack
        metrics:
          - type: map_at_1
            value: 33.205
          - type: map_at_10
            value: 44.859
          - type: map_at_100
            value: 46.135
          - type: map_at_1000
            value: 46.259
          - type: map_at_3
            value: 41.839
          - type: map_at_5
            value: 43.662
          - type: mrr_at_1
            value: 41.146
          - type: mrr_at_10
            value: 50.621
          - type: mrr_at_100
            value: 51.207
          - type: mrr_at_1000
            value: 51.246
          - type: mrr_at_3
            value: 48.535000000000004
          - type: mrr_at_5
            value: 49.818
          - type: ndcg_at_1
            value: 41.146
          - type: ndcg_at_10
            value: 50.683
          - type: ndcg_at_100
            value: 54.82
          - type: ndcg_at_1000
            value: 56.69
          - type: ndcg_at_3
            value: 46.611000000000004
          - type: ndcg_at_5
            value: 48.66
          - type: precision_at_1
            value: 41.146
          - type: precision_at_10
            value: 9.439
          - type: precision_at_100
            value: 1.465
          - type: precision_at_1000
            value: 0.194
          - type: precision_at_3
            value: 22.59
          - type: precision_at_5
            value: 15.86
          - type: recall_at_1
            value: 33.205
          - type: recall_at_10
            value: 61.028999999999996
          - type: recall_at_100
            value: 78.152
          - type: recall_at_1000
            value: 89.59700000000001
          - type: recall_at_3
            value: 49.05
          - type: recall_at_5
            value: 54.836
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB CQADupstackGamingRetrieval
          revision: 4885aa143210c98657558c04aaf3dc47cfb54340
          split: test
          type: BeIR/cqadupstack
        metrics:
          - type: map_at_1
            value: 41.637
          - type: map_at_10
            value: 55.162
          - type: map_at_100
            value: 56.142
          - type: map_at_1000
            value: 56.188
          - type: map_at_3
            value: 51.564
          - type: map_at_5
            value: 53.696
          - type: mrr_at_1
            value: 47.524
          - type: mrr_at_10
            value: 58.243
          - type: mrr_at_100
            value: 58.879999999999995
          - type: mrr_at_1000
            value: 58.9
          - type: mrr_at_3
            value: 55.69499999999999
          - type: mrr_at_5
            value: 57.284
          - type: ndcg_at_1
            value: 47.524
          - type: ndcg_at_10
            value: 61.305
          - type: ndcg_at_100
            value: 65.077
          - type: ndcg_at_1000
            value: 65.941
          - type: ndcg_at_3
            value: 55.422000000000004
          - type: ndcg_at_5
            value: 58.516
          - type: precision_at_1
            value: 47.524
          - type: precision_at_10
            value: 9.918000000000001
          - type: precision_at_100
            value: 1.276
          - type: precision_at_1000
            value: 0.13899999999999998
          - type: precision_at_3
            value: 24.765
          - type: precision_at_5
            value: 17.204
          - type: recall_at_1
            value: 41.637
          - type: recall_at_10
            value: 76.185
          - type: recall_at_100
            value: 92.149
          - type: recall_at_1000
            value: 98.199
          - type: recall_at_3
            value: 60.856
          - type: recall_at_5
            value: 68.25099999999999
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB CQADupstackGisRetrieval
          revision: 5003b3064772da1887988e05400cf3806fe491f2
          split: test
          type: BeIR/cqadupstack
        metrics:
          - type: map_at_1
            value: 26.27
          - type: map_at_10
            value: 37.463
          - type: map_at_100
            value: 38.434000000000005
          - type: map_at_1000
            value: 38.509
          - type: map_at_3
            value: 34.226
          - type: map_at_5
            value: 36.161
          - type: mrr_at_1
            value: 28.588
          - type: mrr_at_10
            value: 39.383
          - type: mrr_at_100
            value: 40.23
          - type: mrr_at_1000
            value: 40.281
          - type: mrr_at_3
            value: 36.422
          - type: mrr_at_5
            value: 38.252
          - type: ndcg_at_1
            value: 28.588
          - type: ndcg_at_10
            value: 43.511
          - type: ndcg_at_100
            value: 48.274
          - type: ndcg_at_1000
            value: 49.975
          - type: ndcg_at_3
            value: 37.319
          - type: ndcg_at_5
            value: 40.568
          - type: precision_at_1
            value: 28.588
          - type: precision_at_10
            value: 6.893000000000001
          - type: precision_at_100
            value: 0.9900000000000001
          - type: precision_at_1000
            value: 0.117
          - type: precision_at_3
            value: 16.347
          - type: precision_at_5
            value: 11.661000000000001
          - type: recall_at_1
            value: 26.27
          - type: recall_at_10
            value: 60.284000000000006
          - type: recall_at_100
            value: 81.902
          - type: recall_at_1000
            value: 94.43
          - type: recall_at_3
            value: 43.537
          - type: recall_at_5
            value: 51.475
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB CQADupstackMathematicaRetrieval
          revision: 90fceea13679c63fe563ded68f3b6f06e50061de
          split: test
          type: BeIR/cqadupstack
        metrics:
          - type: map_at_1
            value: 18.168
          - type: map_at_10
            value: 28.410000000000004
          - type: map_at_100
            value: 29.78
          - type: map_at_1000
            value: 29.892999999999997
          - type: map_at_3
            value: 25.238
          - type: map_at_5
            value: 26.96
          - type: mrr_at_1
            value: 23.507
          - type: mrr_at_10
            value: 33.382
          - type: mrr_at_100
            value: 34.404
          - type: mrr_at_1000
            value: 34.467999999999996
          - type: mrr_at_3
            value: 30.637999999999998
          - type: mrr_at_5
            value: 32.199
          - type: ndcg_at_1
            value: 23.507
          - type: ndcg_at_10
            value: 34.571000000000005
          - type: ndcg_at_100
            value: 40.663
          - type: ndcg_at_1000
            value: 43.236000000000004
          - type: ndcg_at_3
            value: 29.053
          - type: ndcg_at_5
            value: 31.563999999999997
          - type: precision_at_1
            value: 23.507
          - type: precision_at_10
            value: 6.654
          - type: precision_at_100
            value: 1.113
          - type: precision_at_1000
            value: 0.146
          - type: precision_at_3
            value: 14.427999999999999
          - type: precision_at_5
            value: 10.498000000000001
          - type: recall_at_1
            value: 18.168
          - type: recall_at_10
            value: 48.443000000000005
          - type: recall_at_100
            value: 74.47
          - type: recall_at_1000
            value: 92.494
          - type: recall_at_3
            value: 33.379999999999995
          - type: recall_at_5
            value: 39.76
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB CQADupstackPhysicsRetrieval
          revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
          split: test
          type: BeIR/cqadupstack
        metrics:
          - type: map_at_1
            value: 32.39
          - type: map_at_10
            value: 44.479
          - type: map_at_100
            value: 45.977000000000004
          - type: map_at_1000
            value: 46.087
          - type: map_at_3
            value: 40.976
          - type: map_at_5
            value: 43.038
          - type: mrr_at_1
            value: 40.135
          - type: mrr_at_10
            value: 50.160000000000004
          - type: mrr_at_100
            value: 51.052
          - type: mrr_at_1000
            value: 51.087
          - type: mrr_at_3
            value: 47.818
          - type: mrr_at_5
            value: 49.171
          - type: ndcg_at_1
            value: 40.135
          - type: ndcg_at_10
            value: 50.731
          - type: ndcg_at_100
            value: 56.452000000000005
          - type: ndcg_at_1000
            value: 58.123000000000005
          - type: ndcg_at_3
            value: 45.507
          - type: ndcg_at_5
            value: 48.11
          - type: precision_at_1
            value: 40.135
          - type: precision_at_10
            value: 9.192
          - type: precision_at_100
            value: 1.397
          - type: precision_at_1000
            value: 0.169
          - type: precision_at_3
            value: 21.816
          - type: precision_at_5
            value: 15.476
          - type: recall_at_1
            value: 32.39
          - type: recall_at_10
            value: 63.597
          - type: recall_at_100
            value: 86.737
          - type: recall_at_1000
            value: 97.039
          - type: recall_at_3
            value: 48.906
          - type: recall_at_5
            value: 55.659000000000006
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB CQADupstackProgrammersRetrieval
          revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
          split: test
          type: BeIR/cqadupstack
        metrics:
          - type: map_at_1
            value: 28.397
          - type: map_at_10
            value: 39.871
          - type: map_at_100
            value: 41.309000000000005
          - type: map_at_1000
            value: 41.409
          - type: map_at_3
            value: 36.047000000000004
          - type: map_at_5
            value: 38.104
          - type: mrr_at_1
            value: 34.703
          - type: mrr_at_10
            value: 44.773
          - type: mrr_at_100
            value: 45.64
          - type: mrr_at_1000
            value: 45.678999999999995
          - type: mrr_at_3
            value: 41.705
          - type: mrr_at_5
            value: 43.406
          - type: ndcg_at_1
            value: 34.703
          - type: ndcg_at_10
            value: 46.271
          - type: ndcg_at_100
            value: 52.037
          - type: ndcg_at_1000
            value: 53.81700000000001
          - type: ndcg_at_3
            value: 39.966
          - type: ndcg_at_5
            value: 42.801
          - type: precision_at_1
            value: 34.703
          - type: precision_at_10
            value: 8.744
          - type: precision_at_100
            value: 1.348
          - type: precision_at_1000
            value: 0.167
          - type: precision_at_3
            value: 19.102
          - type: precision_at_5
            value: 13.836
          - type: recall_at_1
            value: 28.397
          - type: recall_at_10
            value: 60.299
          - type: recall_at_100
            value: 84.595
          - type: recall_at_1000
            value: 96.155
          - type: recall_at_3
            value: 43.065
          - type: recall_at_5
            value: 50.371
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB CQADupstackRetrieval
          revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
          split: test
          type: BeIR/cqadupstack
        metrics:
          - type: map_at_1
            value: 28.044333333333338
          - type: map_at_10
            value: 38.78691666666666
          - type: map_at_100
            value: 40.113
          - type: map_at_1000
            value: 40.22125
          - type: map_at_3
            value: 35.52966666666667
          - type: map_at_5
            value: 37.372749999999996
          - type: mrr_at_1
            value: 33.159083333333335
          - type: mrr_at_10
            value: 42.913583333333335
          - type: mrr_at_100
            value: 43.7845
          - type: mrr_at_1000
            value: 43.830333333333336
          - type: mrr_at_3
            value: 40.29816666666667
          - type: mrr_at_5
            value: 41.81366666666667
          - type: ndcg_at_1
            value: 33.159083333333335
          - type: ndcg_at_10
            value: 44.75750000000001
          - type: ndcg_at_100
            value: 50.13658333333334
          - type: ndcg_at_1000
            value: 52.037
          - type: ndcg_at_3
            value: 39.34258333333334
          - type: ndcg_at_5
            value: 41.93708333333333
          - type: precision_at_1
            value: 33.159083333333335
          - type: precision_at_10
            value: 7.952416666666667
          - type: precision_at_100
            value: 1.2571666666666668
          - type: precision_at_1000
            value: 0.16099999999999998
          - type: precision_at_3
            value: 18.303833333333337
          - type: precision_at_5
            value: 13.057083333333333
          - type: recall_at_1
            value: 28.044333333333338
          - type: recall_at_10
            value: 58.237249999999996
          - type: recall_at_100
            value: 81.35391666666666
          - type: recall_at_1000
            value: 94.21283333333334
          - type: recall_at_3
            value: 43.32341666666667
          - type: recall_at_5
            value: 49.94908333333333
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB CQADupstackStatsRetrieval
          revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
          split: test
          type: BeIR/cqadupstack
        metrics:
          - type: map_at_1
            value: 27.838
          - type: map_at_10
            value: 36.04
          - type: map_at_100
            value: 37.113
          - type: map_at_1000
            value: 37.204
          - type: map_at_3
            value: 33.585
          - type: map_at_5
            value: 34.845
          - type: mrr_at_1
            value: 30.982
          - type: mrr_at_10
            value: 39.105000000000004
          - type: mrr_at_100
            value: 39.98
          - type: mrr_at_1000
            value: 40.042
          - type: mrr_at_3
            value: 36.912
          - type: mrr_at_5
            value: 38.062000000000005
          - type: ndcg_at_1
            value: 30.982
          - type: ndcg_at_10
            value: 40.982
          - type: ndcg_at_100
            value: 46.092
          - type: ndcg_at_1000
            value: 48.25
          - type: ndcg_at_3
            value: 36.41
          - type: ndcg_at_5
            value: 38.379999999999995
          - type: precision_at_1
            value: 30.982
          - type: precision_at_10
            value: 6.534
          - type: precision_at_100
            value: 0.9820000000000001
          - type: precision_at_1000
            value: 0.124
          - type: precision_at_3
            value: 15.745999999999999
          - type: precision_at_5
            value: 10.828
          - type: recall_at_1
            value: 27.838
          - type: recall_at_10
            value: 52.971000000000004
          - type: recall_at_100
            value: 76.357
          - type: recall_at_1000
            value: 91.973
          - type: recall_at_3
            value: 40.157
          - type: recall_at_5
            value: 45.147999999999996
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB CQADupstackTexRetrieval
          revision: 46989137a86843e03a6195de44b09deda022eec7
          split: test
          type: BeIR/cqadupstack
        metrics:
          - type: map_at_1
            value: 19.059
          - type: map_at_10
            value: 27.454
          - type: map_at_100
            value: 28.736
          - type: map_at_1000
            value: 28.865000000000002
          - type: map_at_3
            value: 24.773999999999997
          - type: map_at_5
            value: 26.266000000000002
          - type: mrr_at_1
            value: 23.125
          - type: mrr_at_10
            value: 31.267
          - type: mrr_at_100
            value: 32.32
          - type: mrr_at_1000
            value: 32.394
          - type: mrr_at_3
            value: 28.894
          - type: mrr_at_5
            value: 30.281000000000002
          - type: ndcg_at_1
            value: 23.125
          - type: ndcg_at_10
            value: 32.588
          - type: ndcg_at_100
            value: 38.432
          - type: ndcg_at_1000
            value: 41.214
          - type: ndcg_at_3
            value: 27.938000000000002
          - type: ndcg_at_5
            value: 30.127
          - type: precision_at_1
            value: 23.125
          - type: precision_at_10
            value: 5.9639999999999995
          - type: precision_at_100
            value: 1.047
          - type: precision_at_1000
            value: 0.148
          - type: precision_at_3
            value: 13.294
          - type: precision_at_5
            value: 9.628
          - type: recall_at_1
            value: 19.059
          - type: recall_at_10
            value: 44.25
          - type: recall_at_100
            value: 69.948
          - type: recall_at_1000
            value: 89.35300000000001
          - type: recall_at_3
            value: 31.114000000000004
          - type: recall_at_5
            value: 36.846000000000004
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB CQADupstackUnixRetrieval
          revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
          split: test
          type: BeIR/cqadupstack
        metrics:
          - type: map_at_1
            value: 28.355999999999998
          - type: map_at_10
            value: 39.055
          - type: map_at_100
            value: 40.486
          - type: map_at_1000
            value: 40.571
          - type: map_at_3
            value: 35.69
          - type: map_at_5
            value: 37.605
          - type: mrr_at_1
            value: 33.302
          - type: mrr_at_10
            value: 42.986000000000004
          - type: mrr_at_100
            value: 43.957
          - type: mrr_at_1000
            value: 43.996
          - type: mrr_at_3
            value: 40.111999999999995
          - type: mrr_at_5
            value: 41.735
          - type: ndcg_at_1
            value: 33.302
          - type: ndcg_at_10
            value: 44.962999999999994
          - type: ndcg_at_100
            value: 50.917
          - type: ndcg_at_1000
            value: 52.622
          - type: ndcg_at_3
            value: 39.182
          - type: ndcg_at_5
            value: 41.939
          - type: precision_at_1
            value: 33.302
          - type: precision_at_10
            value: 7.779999999999999
          - type: precision_at_100
            value: 1.203
          - type: precision_at_1000
            value: 0.145
          - type: precision_at_3
            value: 18.035
          - type: precision_at_5
            value: 12.873000000000001
          - type: recall_at_1
            value: 28.355999999999998
          - type: recall_at_10
            value: 58.782000000000004
          - type: recall_at_100
            value: 84.02199999999999
          - type: recall_at_1000
            value: 95.511
          - type: recall_at_3
            value: 43.126999999999995
          - type: recall_at_5
            value: 50.14999999999999
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB CQADupstackWebmastersRetrieval
          revision: 160c094312a0e1facb97e55eeddb698c0abe3571
          split: test
          type: BeIR/cqadupstack
        metrics:
          - type: map_at_1
            value: 27.391
          - type: map_at_10
            value: 37.523
          - type: map_at_100
            value: 39.312000000000005
          - type: map_at_1000
            value: 39.54
          - type: map_at_3
            value: 34.231
          - type: map_at_5
            value: 36.062
          - type: mrr_at_1
            value: 32.016
          - type: mrr_at_10
            value: 41.747
          - type: mrr_at_100
            value: 42.812
          - type: mrr_at_1000
            value: 42.844
          - type: mrr_at_3
            value: 39.129999999999995
          - type: mrr_at_5
            value: 40.524
          - type: ndcg_at_1
            value: 32.016
          - type: ndcg_at_10
            value: 43.826
          - type: ndcg_at_100
            value: 50.373999999999995
          - type: ndcg_at_1000
            value: 52.318
          - type: ndcg_at_3
            value: 38.479
          - type: ndcg_at_5
            value: 40.944
          - type: precision_at_1
            value: 32.016
          - type: precision_at_10
            value: 8.280999999999999
          - type: precision_at_100
            value: 1.6760000000000002
          - type: precision_at_1000
            value: 0.25
          - type: precision_at_3
            value: 18.05
          - type: precision_at_5
            value: 13.083
          - type: recall_at_1
            value: 27.391
          - type: recall_at_10
            value: 56.928999999999995
          - type: recall_at_100
            value: 85.169
          - type: recall_at_1000
            value: 96.665
          - type: recall_at_3
            value: 42.264
          - type: recall_at_5
            value: 48.556
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB CQADupstackWordpressRetrieval
          revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
          split: test
          type: BeIR/cqadupstack
        metrics:
          - type: map_at_1
            value: 18.398
          - type: map_at_10
            value: 27.929
          - type: map_at_100
            value: 29.032999999999998
          - type: map_at_1000
            value: 29.126
          - type: map_at_3
            value: 25.070999999999998
          - type: map_at_5
            value: 26.583000000000002
          - type: mrr_at_1
            value: 19.963
          - type: mrr_at_10
            value: 29.997
          - type: mrr_at_100
            value: 30.9
          - type: mrr_at_1000
            value: 30.972
          - type: mrr_at_3
            value: 27.264
          - type: mrr_at_5
            value: 28.826
          - type: ndcg_at_1
            value: 19.963
          - type: ndcg_at_10
            value: 33.678999999999995
          - type: ndcg_at_100
            value: 38.931
          - type: ndcg_at_1000
            value: 41.379
          - type: ndcg_at_3
            value: 28.000000000000004
          - type: ndcg_at_5
            value: 30.637999999999998
          - type: precision_at_1
            value: 19.963
          - type: precision_at_10
            value: 5.7299999999999995
          - type: precision_at_100
            value: 0.902
          - type: precision_at_1000
            value: 0.122
          - type: precision_at_3
            value: 12.631
          - type: precision_at_5
            value: 9.057
          - type: recall_at_1
            value: 18.398
          - type: recall_at_10
            value: 49.254
          - type: recall_at_100
            value: 73.182
          - type: recall_at_1000
            value: 91.637
          - type: recall_at_3
            value: 34.06
          - type: recall_at_5
            value: 40.416000000000004
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB ClimateFEVER
          revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
          split: test
          type: mteb/climate-fever
        metrics:
          - type: map_at_1
            value: 19.681
          - type: map_at_10
            value: 32.741
          - type: map_at_100
            value: 34.811
          - type: map_at_1000
            value: 35.003
          - type: map_at_3
            value: 27.697
          - type: map_at_5
            value: 30.372
          - type: mrr_at_1
            value: 44.951
          - type: mrr_at_10
            value: 56.34400000000001
          - type: mrr_at_100
            value: 56.961
          - type: mrr_at_1000
            value: 56.987
          - type: mrr_at_3
            value: 53.681
          - type: mrr_at_5
            value: 55.407
          - type: ndcg_at_1
            value: 44.951
          - type: ndcg_at_10
            value: 42.905
          - type: ndcg_at_100
            value: 49.95
          - type: ndcg_at_1000
            value: 52.917
          - type: ndcg_at_3
            value: 36.815
          - type: ndcg_at_5
            value: 38.817
          - type: precision_at_1
            value: 44.951
          - type: precision_at_10
            value: 12.989999999999998
          - type: precision_at_100
            value: 2.068
          - type: precision_at_1000
            value: 0.263
          - type: precision_at_3
            value: 27.275
          - type: precision_at_5
            value: 20.365
          - type: recall_at_1
            value: 19.681
          - type: recall_at_10
            value: 48.272999999999996
          - type: recall_at_100
            value: 71.87400000000001
          - type: recall_at_1000
            value: 87.929
          - type: recall_at_3
            value: 32.653999999999996
          - type: recall_at_5
            value: 39.364
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB DBPedia
          revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
          split: test
          type: mteb/dbpedia
        metrics:
          - type: map_at_1
            value: 10.231
          - type: map_at_10
            value: 22.338
          - type: map_at_100
            value: 31.927
          - type: map_at_1000
            value: 33.87
          - type: map_at_3
            value: 15.559999999999999
          - type: map_at_5
            value: 18.239
          - type: mrr_at_1
            value: 75
          - type: mrr_at_10
            value: 81.303
          - type: mrr_at_100
            value: 81.523
          - type: mrr_at_1000
            value: 81.53
          - type: mrr_at_3
            value: 80.083
          - type: mrr_at_5
            value: 80.758
          - type: ndcg_at_1
            value: 64.625
          - type: ndcg_at_10
            value: 48.687000000000005
          - type: ndcg_at_100
            value: 52.791
          - type: ndcg_at_1000
            value: 60.041999999999994
          - type: ndcg_at_3
            value: 53.757999999999996
          - type: ndcg_at_5
            value: 50.76500000000001
          - type: precision_at_1
            value: 75
          - type: precision_at_10
            value: 38.3
          - type: precision_at_100
            value: 12.025
          - type: precision_at_1000
            value: 2.3970000000000002
          - type: precision_at_3
            value: 55.417
          - type: precision_at_5
            value: 47.5
          - type: recall_at_1
            value: 10.231
          - type: recall_at_10
            value: 27.697
          - type: recall_at_100
            value: 57.409
          - type: recall_at_1000
            value: 80.547
          - type: recall_at_3
            value: 16.668
          - type: recall_at_5
            value: 20.552
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB EmotionClassification
          revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
          split: test
          type: mteb/emotion
        metrics:
          - type: accuracy
            value: 61.365
          - type: f1
            value: 56.7540827912991
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB FEVER
          revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
          split: test
          type: mteb/fever
        metrics:
          - type: map_at_1
            value: 83.479
          - type: map_at_10
            value: 88.898
          - type: map_at_100
            value: 89.11
          - type: map_at_1000
            value: 89.12400000000001
          - type: map_at_3
            value: 88.103
          - type: map_at_5
            value: 88.629
          - type: mrr_at_1
            value: 89.934
          - type: mrr_at_10
            value: 93.91000000000001
          - type: mrr_at_100
            value: 93.937
          - type: mrr_at_1000
            value: 93.938
          - type: mrr_at_3
            value: 93.62700000000001
          - type: mrr_at_5
            value: 93.84599999999999
          - type: ndcg_at_1
            value: 89.934
          - type: ndcg_at_10
            value: 91.574
          - type: ndcg_at_100
            value: 92.238
          - type: ndcg_at_1000
            value: 92.45
          - type: ndcg_at_3
            value: 90.586
          - type: ndcg_at_5
            value: 91.16300000000001
          - type: precision_at_1
            value: 89.934
          - type: precision_at_10
            value: 10.555
          - type: precision_at_100
            value: 1.1159999999999999
          - type: precision_at_1000
            value: 0.11499999999999999
          - type: precision_at_3
            value: 33.588
          - type: precision_at_5
            value: 20.642
          - type: recall_at_1
            value: 83.479
          - type: recall_at_10
            value: 94.971
          - type: recall_at_100
            value: 97.397
          - type: recall_at_1000
            value: 98.666
          - type: recall_at_3
            value: 92.24799999999999
          - type: recall_at_5
            value: 93.797
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB FiQA2018
          revision: 27a168819829fe9bcd655c2df245fb19452e8e06
          split: test
          type: mteb/fiqa
        metrics:
          - type: map_at_1
            value: 27.16
          - type: map_at_10
            value: 45.593
          - type: map_at_100
            value: 47.762
          - type: map_at_1000
            value: 47.899
          - type: map_at_3
            value: 39.237
          - type: map_at_5
            value: 42.970000000000006
          - type: mrr_at_1
            value: 52.623
          - type: mrr_at_10
            value: 62.637
          - type: mrr_at_100
            value: 63.169
          - type: mrr_at_1000
            value: 63.185
          - type: mrr_at_3
            value: 59.928000000000004
          - type: mrr_at_5
            value: 61.702999999999996
          - type: ndcg_at_1
            value: 52.623
          - type: ndcg_at_10
            value: 54.701
          - type: ndcg_at_100
            value: 61.263
          - type: ndcg_at_1000
            value: 63.134
          - type: ndcg_at_3
            value: 49.265
          - type: ndcg_at_5
            value: 51.665000000000006
          - type: precision_at_1
            value: 52.623
          - type: precision_at_10
            value: 15.185
          - type: precision_at_100
            value: 2.202
          - type: precision_at_1000
            value: 0.254
          - type: precision_at_3
            value: 32.767
          - type: precision_at_5
            value: 24.722
          - type: recall_at_1
            value: 27.16
          - type: recall_at_10
            value: 63.309000000000005
          - type: recall_at_100
            value: 86.722
          - type: recall_at_1000
            value: 97.505
          - type: recall_at_3
            value: 45.045
          - type: recall_at_5
            value: 54.02400000000001
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB HotpotQA
          revision: ab518f4d6fcca38d87c25209f94beba119d02014
          split: test
          type: mteb/hotpotqa
        metrics:
          - type: map_at_1
            value: 42.573
          - type: map_at_10
            value: 59.373
          - type: map_at_100
            value: 60.292
          - type: map_at_1000
            value: 60.358999999999995
          - type: map_at_3
            value: 56.159000000000006
          - type: map_at_5
            value: 58.123999999999995
          - type: mrr_at_1
            value: 85.14500000000001
          - type: mrr_at_10
            value: 89.25999999999999
          - type: mrr_at_100
            value: 89.373
          - type: mrr_at_1000
            value: 89.377
          - type: mrr_at_3
            value: 88.618
          - type: mrr_at_5
            value: 89.036
          - type: ndcg_at_1
            value: 85.14500000000001
          - type: ndcg_at_10
            value: 68.95
          - type: ndcg_at_100
            value: 71.95
          - type: ndcg_at_1000
            value: 73.232
          - type: ndcg_at_3
            value: 64.546
          - type: ndcg_at_5
            value: 66.945
          - type: precision_at_1
            value: 85.14500000000001
          - type: precision_at_10
            value: 13.865
          - type: precision_at_100
            value: 1.619
          - type: precision_at_1000
            value: 0.179
          - type: precision_at_3
            value: 39.703
          - type: precision_at_5
            value: 25.718000000000004
          - type: recall_at_1
            value: 42.573
          - type: recall_at_10
            value: 69.325
          - type: recall_at_100
            value: 80.932
          - type: recall_at_1000
            value: 89.446
          - type: recall_at_3
            value: 59.553999999999995
          - type: recall_at_5
            value: 64.294
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB ImdbClassification
          revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
          split: test
          type: mteb/imdb
        metrics:
          - type: accuracy
            value: 95.8336
          - type: ap
            value: 93.78862962194073
          - type: f1
            value: 95.83192650728371
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB MSMARCO
          revision: c5a29a104738b98a9e76336939199e264163d4a0
          split: dev
          type: mteb/msmarco
        metrics:
          - type: map_at_1
            value: 23.075000000000003
          - type: map_at_10
            value: 36.102000000000004
          - type: map_at_100
            value: 37.257
          - type: map_at_1000
            value: 37.3
          - type: map_at_3
            value: 32.144
          - type: map_at_5
            value: 34.359
          - type: mrr_at_1
            value: 23.711
          - type: mrr_at_10
            value: 36.671
          - type: mrr_at_100
            value: 37.763999999999996
          - type: mrr_at_1000
            value: 37.801
          - type: mrr_at_3
            value: 32.775
          - type: mrr_at_5
            value: 34.977000000000004
          - type: ndcg_at_1
            value: 23.711
          - type: ndcg_at_10
            value: 43.361
          - type: ndcg_at_100
            value: 48.839
          - type: ndcg_at_1000
            value: 49.88
          - type: ndcg_at_3
            value: 35.269
          - type: ndcg_at_5
            value: 39.224
          - type: precision_at_1
            value: 23.711
          - type: precision_at_10
            value: 6.866999999999999
          - type: precision_at_100
            value: 0.96
          - type: precision_at_1000
            value: 0.105
          - type: precision_at_3
            value: 15.096000000000002
          - type: precision_at_5
            value: 11.083
          - type: recall_at_1
            value: 23.075000000000003
          - type: recall_at_10
            value: 65.756
          - type: recall_at_100
            value: 90.88199999999999
          - type: recall_at_1000
            value: 98.739
          - type: recall_at_3
            value: 43.691
          - type: recall_at_5
            value: 53.15800000000001
        task:
          type: Retrieval
      - dataset:
          config: en
          name: MTEB MTOPDomainClassification (en)
          revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
          split: test
          type: mteb/mtop_domain
        metrics:
          - type: accuracy
            value: 97.69493844049248
          - type: f1
            value: 97.55048089616261
        task:
          type: Classification
      - dataset:
          config: en
          name: MTEB MTOPIntentClassification (en)
          revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
          split: test
          type: mteb/mtop_intent
        metrics:
          - type: accuracy
            value: 88.75968992248062
          - type: f1
            value: 72.26321223399123
        task:
          type: Classification
      - dataset:
          config: en
          name: MTEB MassiveIntentClassification (en)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 82.40080699394754
          - type: f1
            value: 79.62590029057968
        task:
          type: Classification
      - dataset:
          config: en
          name: MTEB MassiveScenarioClassification (en)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 84.49562878278414
          - type: f1
            value: 84.0040193313333
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB MedrxivClusteringP2P
          revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
          split: test
          type: mteb/medrxiv-clustering-p2p
        metrics:
          - type: v_measure
            value: 39.386760057101945
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB MedrxivClusteringS2S
          revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
          split: test
          type: mteb/medrxiv-clustering-s2s
        metrics:
          - type: v_measure
            value: 37.89687154075537
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB MindSmallReranking
          revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
          split: test
          type: mteb/mind_small
        metrics:
          - type: map
            value: 33.94151656057482
          - type: mrr
            value: 35.32684700746953
        task:
          type: Reranking
      - dataset:
          config: default
          name: MTEB NFCorpus
          revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
          split: test
          type: mteb/nfcorpus
        metrics:
          - type: map_at_1
            value: 6.239999999999999
          - type: map_at_10
            value: 14.862
          - type: map_at_100
            value: 18.955
          - type: map_at_1000
            value: 20.694000000000003
          - type: map_at_3
            value: 10.683
          - type: map_at_5
            value: 12.674
          - type: mrr_at_1
            value: 50.15500000000001
          - type: mrr_at_10
            value: 59.697
          - type: mrr_at_100
            value: 60.095
          - type: mrr_at_1000
            value: 60.129999999999995
          - type: mrr_at_3
            value: 58.35900000000001
          - type: mrr_at_5
            value: 58.839
          - type: ndcg_at_1
            value: 48.452
          - type: ndcg_at_10
            value: 39.341
          - type: ndcg_at_100
            value: 35.866
          - type: ndcg_at_1000
            value: 45.111000000000004
          - type: ndcg_at_3
            value: 44.527
          - type: ndcg_at_5
            value: 42.946
          - type: precision_at_1
            value: 50.15500000000001
          - type: precision_at_10
            value: 29.536
          - type: precision_at_100
            value: 9.142
          - type: precision_at_1000
            value: 2.2849999999999997
          - type: precision_at_3
            value: 41.899
          - type: precision_at_5
            value: 37.647000000000006
          - type: recall_at_1
            value: 6.239999999999999
          - type: recall_at_10
            value: 19.278000000000002
          - type: recall_at_100
            value: 36.074
          - type: recall_at_1000
            value: 70.017
          - type: recall_at_3
            value: 12.066
          - type: recall_at_5
            value: 15.254000000000001
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB NQ
          revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
          split: test
          type: mteb/nq
        metrics:
          - type: map_at_1
            value: 39.75
          - type: map_at_10
            value: 56.443
          - type: map_at_100
            value: 57.233999999999995
          - type: map_at_1000
            value: 57.249
          - type: map_at_3
            value: 52.032999999999994
          - type: map_at_5
            value: 54.937999999999995
          - type: mrr_at_1
            value: 44.728
          - type: mrr_at_10
            value: 58.939
          - type: mrr_at_100
            value: 59.489000000000004
          - type: mrr_at_1000
            value: 59.499
          - type: mrr_at_3
            value: 55.711999999999996
          - type: mrr_at_5
            value: 57.89
          - type: ndcg_at_1
            value: 44.728
          - type: ndcg_at_10
            value: 63.998999999999995
          - type: ndcg_at_100
            value: 67.077
          - type: ndcg_at_1000
            value: 67.40899999999999
          - type: ndcg_at_3
            value: 56.266000000000005
          - type: ndcg_at_5
            value: 60.88
          - type: precision_at_1
            value: 44.728
          - type: precision_at_10
            value: 10.09
          - type: precision_at_100
            value: 1.1809999999999998
          - type: precision_at_1000
            value: 0.121
          - type: precision_at_3
            value: 25.145
          - type: precision_at_5
            value: 17.822
          - type: recall_at_1
            value: 39.75
          - type: recall_at_10
            value: 84.234
          - type: recall_at_100
            value: 97.055
          - type: recall_at_1000
            value: 99.517
          - type: recall_at_3
            value: 64.851
          - type: recall_at_5
            value: 75.343
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB QuoraRetrieval
          revision: None
          split: test
          type: mteb/quora
        metrics:
          - type: map_at_1
            value: 72.085
          - type: map_at_10
            value: 86.107
          - type: map_at_100
            value: 86.727
          - type: map_at_1000
            value: 86.74
          - type: map_at_3
            value: 83.21
          - type: map_at_5
            value: 85.06
          - type: mrr_at_1
            value: 82.94
          - type: mrr_at_10
            value: 88.845
          - type: mrr_at_100
            value: 88.926
          - type: mrr_at_1000
            value: 88.927
          - type: mrr_at_3
            value: 87.993
          - type: mrr_at_5
            value: 88.62299999999999
          - type: ndcg_at_1
            value: 82.97
          - type: ndcg_at_10
            value: 89.645
          - type: ndcg_at_100
            value: 90.717
          - type: ndcg_at_1000
            value: 90.78
          - type: ndcg_at_3
            value: 86.99900000000001
          - type: ndcg_at_5
            value: 88.52600000000001
          - type: precision_at_1
            value: 82.97
          - type: precision_at_10
            value: 13.569
          - type: precision_at_100
            value: 1.539
          - type: precision_at_1000
            value: 0.157
          - type: precision_at_3
            value: 38.043
          - type: precision_at_5
            value: 24.992
          - type: recall_at_1
            value: 72.085
          - type: recall_at_10
            value: 96.262
          - type: recall_at_100
            value: 99.77000000000001
          - type: recall_at_1000
            value: 99.997
          - type: recall_at_3
            value: 88.652
          - type: recall_at_5
            value: 93.01899999999999
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB RedditClustering
          revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
          split: test
          type: mteb/reddit-clustering
        metrics:
          - type: v_measure
            value: 55.82153952668092
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB RedditClusteringP2P
          revision: 282350215ef01743dc01b456c7f5241fa8937f16
          split: test
          type: mteb/reddit-clustering-p2p
        metrics:
          - type: v_measure
            value: 62.094465801879295
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB SCIDOCS
          revision: None
          split: test
          type: mteb/scidocs
        metrics:
          - type: map_at_1
            value: 5.688
          - type: map_at_10
            value: 15.201999999999998
          - type: map_at_100
            value: 18.096
          - type: map_at_1000
            value: 18.481
          - type: map_at_3
            value: 10.734
          - type: map_at_5
            value: 12.94
          - type: mrr_at_1
            value: 28.000000000000004
          - type: mrr_at_10
            value: 41.101
          - type: mrr_at_100
            value: 42.202
          - type: mrr_at_1000
            value: 42.228
          - type: mrr_at_3
            value: 37.683
          - type: mrr_at_5
            value: 39.708
          - type: ndcg_at_1
            value: 28.000000000000004
          - type: ndcg_at_10
            value: 24.976000000000003
          - type: ndcg_at_100
            value: 35.129
          - type: ndcg_at_1000
            value: 40.77
          - type: ndcg_at_3
            value: 23.787
          - type: ndcg_at_5
            value: 20.816000000000003
          - type: precision_at_1
            value: 28.000000000000004
          - type: precision_at_10
            value: 13.04
          - type: precision_at_100
            value: 2.761
          - type: precision_at_1000
            value: 0.41000000000000003
          - type: precision_at_3
            value: 22.6
          - type: precision_at_5
            value: 18.52
          - type: recall_at_1
            value: 5.688
          - type: recall_at_10
            value: 26.43
          - type: recall_at_100
            value: 56.02
          - type: recall_at_1000
            value: 83.21
          - type: recall_at_3
            value: 13.752
          - type: recall_at_5
            value: 18.777
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB SICK-R
          revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
          split: test
          type: mteb/sickr-sts
        metrics:
          - type: cos_sim_pearson
            value: 85.15084859283178
          - type: cos_sim_spearman
            value: 80.49030614009419
          - type: euclidean_pearson
            value: 81.84574978672468
          - type: euclidean_spearman
            value: 79.89787150656818
          - type: manhattan_pearson
            value: 81.63076538567131
          - type: manhattan_spearman
            value: 79.69867352121841
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB STS12
          revision: a0d554a64d88156834ff5ae9920b964011b16384
          split: test
          type: mteb/sts12-sts
        metrics:
          - type: cos_sim_pearson
            value: 84.64097921490992
          - type: cos_sim_spearman
            value: 77.25370084896514
          - type: euclidean_pearson
            value: 82.71210826468788
          - type: euclidean_spearman
            value: 78.50445584994826
          - type: manhattan_pearson
            value: 82.92580164330298
          - type: manhattan_spearman
            value: 78.69686891301019
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB STS13
          revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
          split: test
          type: mteb/sts13-sts
        metrics:
          - type: cos_sim_pearson
            value: 87.24596417308994
          - type: cos_sim_spearman
            value: 87.79454220555091
          - type: euclidean_pearson
            value: 87.40242561671164
          - type: euclidean_spearman
            value: 88.25955597373556
          - type: manhattan_pearson
            value: 87.25160240485849
          - type: manhattan_spearman
            value: 88.155794979818
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB STS14
          revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
          split: test
          type: mteb/sts14-sts
        metrics:
          - type: cos_sim_pearson
            value: 84.44914233422564
          - type: cos_sim_spearman
            value: 82.91015471820322
          - type: euclidean_pearson
            value: 84.7206656630327
          - type: euclidean_spearman
            value: 83.86408872059216
          - type: manhattan_pearson
            value: 84.72816725158454
          - type: manhattan_spearman
            value: 84.01603388572788
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB STS15
          revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
          split: test
          type: mteb/sts15-sts
        metrics:
          - type: cos_sim_pearson
            value: 87.6168026237477
          - type: cos_sim_spearman
            value: 88.45414278092397
          - type: euclidean_pearson
            value: 88.57023240882022
          - type: euclidean_spearman
            value: 89.04102190922094
          - type: manhattan_pearson
            value: 88.66695535796354
          - type: manhattan_spearman
            value: 89.19898476680969
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB STS16
          revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
          split: test
          type: mteb/sts16-sts
        metrics:
          - type: cos_sim_pearson
            value: 84.27925826089424
          - type: cos_sim_spearman
            value: 85.45291099550461
          - type: euclidean_pearson
            value: 83.63853036580834
          - type: euclidean_spearman
            value: 84.33468035821484
          - type: manhattan_pearson
            value: 83.72778773251596
          - type: manhattan_spearman
            value: 84.51583132445376
        task:
          type: STS
      - dataset:
          config: en-en
          name: MTEB STS17 (en-en)
          revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
          split: test
          type: mteb/sts17-crosslingual-sts
        metrics:
          - type: cos_sim_pearson
            value: 89.67375185692552
          - type: cos_sim_spearman
            value: 90.32542469203855
          - type: euclidean_pearson
            value: 89.63513717951847
          - type: euclidean_spearman
            value: 89.87760271003745
          - type: manhattan_pearson
            value: 89.28381452982924
          - type: manhattan_spearman
            value: 89.53568197785721
        task:
          type: STS
      - dataset:
          config: en
          name: MTEB STS22 (en)
          revision: eea2b4fe26a775864c896887d910b76a8098ad3f
          split: test
          type: mteb/sts22-crosslingual-sts
        metrics:
          - type: cos_sim_pearson
            value: 66.24644693819846
          - type: cos_sim_spearman
            value: 66.09889420525377
          - type: euclidean_pearson
            value: 63.72551583520747
          - type: euclidean_spearman
            value: 63.01385470780679
          - type: manhattan_pearson
            value: 64.09258157214097
          - type: manhattan_spearman
            value: 63.080517752822594
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB STSBenchmark
          revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
          split: test
          type: mteb/stsbenchmark-sts
        metrics:
          - type: cos_sim_pearson
            value: 86.27321463839989
          - type: cos_sim_spearman
            value: 86.37572865993327
          - type: euclidean_pearson
            value: 86.36268020198149
          - type: euclidean_spearman
            value: 86.31089339478922
          - type: manhattan_pearson
            value: 86.4260445761947
          - type: manhattan_spearman
            value: 86.45885895320457
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB SciDocsRR
          revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
          split: test
          type: mteb/scidocs-reranking
        metrics:
          - type: map
            value: 86.52456702387798
          - type: mrr
            value: 96.34556529164372
        task:
          type: Reranking
      - dataset:
          config: default
          name: MTEB SciFact
          revision: 0228b52cf27578f30900b9e5271d331663a030d7
          split: test
          type: mteb/scifact
        metrics:
          - type: map_at_1
            value: 61.99400000000001
          - type: map_at_10
            value: 73.38799999999999
          - type: map_at_100
            value: 73.747
          - type: map_at_1000
            value: 73.75
          - type: map_at_3
            value: 70.04599999999999
          - type: map_at_5
            value: 72.095
          - type: mrr_at_1
            value: 65
          - type: mrr_at_10
            value: 74.42800000000001
          - type: mrr_at_100
            value: 74.722
          - type: mrr_at_1000
            value: 74.725
          - type: mrr_at_3
            value: 72.056
          - type: mrr_at_5
            value: 73.60600000000001
          - type: ndcg_at_1
            value: 65
          - type: ndcg_at_10
            value: 78.435
          - type: ndcg_at_100
            value: 79.922
          - type: ndcg_at_1000
            value: 80.00500000000001
          - type: ndcg_at_3
            value: 73.05199999999999
          - type: ndcg_at_5
            value: 75.98
          - type: precision_at_1
            value: 65
          - type: precision_at_10
            value: 10.5
          - type: precision_at_100
            value: 1.123
          - type: precision_at_1000
            value: 0.11299999999999999
          - type: precision_at_3
            value: 28.555999999999997
          - type: precision_at_5
            value: 19
          - type: recall_at_1
            value: 61.99400000000001
          - type: recall_at_10
            value: 92.72200000000001
          - type: recall_at_100
            value: 99.333
          - type: recall_at_1000
            value: 100
          - type: recall_at_3
            value: 78.739
          - type: recall_at_5
            value: 85.828
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB SprintDuplicateQuestions
          revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
          split: test
          type: mteb/sprintduplicatequestions-pairclassification
        metrics:
          - type: cos_sim_accuracy
            value: 99.79009900990098
          - type: cos_sim_ap
            value: 95.3203137438653
          - type: cos_sim_f1
            value: 89.12386706948641
          - type: cos_sim_precision
            value: 89.75659229208925
          - type: cos_sim_recall
            value: 88.5
          - type: dot_accuracy
            value: 99.67821782178218
          - type: dot_ap
            value: 89.94069840000675
          - type: dot_f1
            value: 83.45902463549521
          - type: dot_precision
            value: 83.9231547017189
          - type: dot_recall
            value: 83
          - type: euclidean_accuracy
            value: 99.78613861386138
          - type: euclidean_ap
            value: 95.10648259135526
          - type: euclidean_f1
            value: 88.77338877338877
          - type: euclidean_precision
            value: 92.42424242424242
          - type: euclidean_recall
            value: 85.39999999999999
          - type: manhattan_accuracy
            value: 99.7950495049505
          - type: manhattan_ap
            value: 95.29987661320946
          - type: manhattan_f1
            value: 89.21313183949972
          - type: manhattan_precision
            value: 93.14472252448314
          - type: manhattan_recall
            value: 85.6
          - type: max_accuracy
            value: 99.7950495049505
          - type: max_ap
            value: 95.3203137438653
          - type: max_f1
            value: 89.21313183949972
        task:
          type: PairClassification
      - dataset:
          config: default
          name: MTEB StackExchangeClustering
          revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
          split: test
          type: mteb/stackexchange-clustering
        metrics:
          - type: v_measure
            value: 67.65446577183913
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB StackExchangeClusteringP2P
          revision: 815ca46b2622cec33ccafc3735d572c266efdb44
          split: test
          type: mteb/stackexchange-clustering-p2p
        metrics:
          - type: v_measure
            value: 46.30749237193961
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB StackOverflowDupQuestions
          revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
          split: test
          type: mteb/stackoverflowdupquestions-reranking
        metrics:
          - type: map
            value: 54.91481849959949
          - type: mrr
            value: 55.853506175197346
        task:
          type: Reranking
      - dataset:
          config: default
          name: MTEB SummEval
          revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
          split: test
          type: mteb/summeval
        metrics:
          - type: cos_sim_pearson
            value: 30.08196549170419
          - type: cos_sim_spearman
            value: 31.16661390597077
          - type: dot_pearson
            value: 29.892258410943466
          - type: dot_spearman
            value: 30.51328811965085
        task:
          type: Summarization
      - dataset:
          config: default
          name: MTEB TRECCOVID
          revision: None
          split: test
          type: mteb/trec-covid
        metrics:
          - type: map_at_1
            value: 0.23900000000000002
          - type: map_at_10
            value: 2.173
          - type: map_at_100
            value: 14.24
          - type: map_at_1000
            value: 35.309000000000005
          - type: map_at_3
            value: 0.7100000000000001
          - type: map_at_5
            value: 1.163
          - type: mrr_at_1
            value: 92
          - type: mrr_at_10
            value: 96
          - type: mrr_at_100
            value: 96
          - type: mrr_at_1000
            value: 96
          - type: mrr_at_3
            value: 96
          - type: mrr_at_5
            value: 96
          - type: ndcg_at_1
            value: 90
          - type: ndcg_at_10
            value: 85.382
          - type: ndcg_at_100
            value: 68.03
          - type: ndcg_at_1000
            value: 61.021
          - type: ndcg_at_3
            value: 89.765
          - type: ndcg_at_5
            value: 88.444
          - type: precision_at_1
            value: 92
          - type: precision_at_10
            value: 88
          - type: precision_at_100
            value: 70.02000000000001
          - type: precision_at_1000
            value: 26.984
          - type: precision_at_3
            value: 94
          - type: precision_at_5
            value: 92.80000000000001
          - type: recall_at_1
            value: 0.23900000000000002
          - type: recall_at_10
            value: 2.313
          - type: recall_at_100
            value: 17.049
          - type: recall_at_1000
            value: 57.489999999999995
          - type: recall_at_3
            value: 0.737
          - type: recall_at_5
            value: 1.221
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB Touche2020
          revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
          split: test
          type: mteb/touche2020
        metrics:
          - type: map_at_1
            value: 2.75
          - type: map_at_10
            value: 11.29
          - type: map_at_100
            value: 18.032999999999998
          - type: map_at_1000
            value: 19.746
          - type: map_at_3
            value: 6.555
          - type: map_at_5
            value: 8.706999999999999
          - type: mrr_at_1
            value: 34.694
          - type: mrr_at_10
            value: 50.55
          - type: mrr_at_100
            value: 51.659
          - type: mrr_at_1000
            value: 51.659
          - type: mrr_at_3
            value: 47.278999999999996
          - type: mrr_at_5
            value: 49.728
          - type: ndcg_at_1
            value: 32.653
          - type: ndcg_at_10
            value: 27.894000000000002
          - type: ndcg_at_100
            value: 39.769
          - type: ndcg_at_1000
            value: 51.495999999999995
          - type: ndcg_at_3
            value: 32.954
          - type: ndcg_at_5
            value: 31.502999999999997
          - type: precision_at_1
            value: 34.694
          - type: precision_at_10
            value: 23.265
          - type: precision_at_100
            value: 7.898
          - type: precision_at_1000
            value: 1.58
          - type: precision_at_3
            value: 34.694
          - type: precision_at_5
            value: 31.429000000000002
          - type: recall_at_1
            value: 2.75
          - type: recall_at_10
            value: 16.953
          - type: recall_at_100
            value: 48.68
          - type: recall_at_1000
            value: 85.18599999999999
          - type: recall_at_3
            value: 7.710999999999999
          - type: recall_at_5
            value: 11.484
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB ToxicConversationsClassification
          revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
          split: test
          type: mteb/toxic_conversations_50k
        metrics:
          - type: accuracy
            value: 82.66099999999999
          - type: ap
            value: 25.555698090238337
          - type: f1
            value: 66.48402012461622
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB TweetSentimentExtractionClassification
          revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
          split: test
          type: mteb/tweet_sentiment_extraction
        metrics:
          - type: accuracy
            value: 72.94567062818335
          - type: f1
            value: 73.28139189595674
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB TwentyNewsgroupsClustering
          revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
          split: test
          type: mteb/twentynewsgroups-clustering
        metrics:
          - type: v_measure
            value: 49.581627240203474
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB TwitterSemEval2015
          revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
          split: test
          type: mteb/twittersemeval2015-pairclassification
        metrics:
          - type: cos_sim_accuracy
            value: 87.78089050485785
          - type: cos_sim_ap
            value: 79.64487116574168
          - type: cos_sim_f1
            value: 72.46563021970964
          - type: cos_sim_precision
            value: 70.62359128474831
          - type: cos_sim_recall
            value: 74.40633245382587
          - type: dot_accuracy
            value: 86.2609524944865
          - type: dot_ap
            value: 75.513046857613
          - type: dot_f1
            value: 68.58213616489695
          - type: dot_precision
            value: 65.12455516014235
          - type: dot_recall
            value: 72.42744063324538
          - type: euclidean_accuracy
            value: 87.6080348095607
          - type: euclidean_ap
            value: 79.00204933649795
          - type: euclidean_f1
            value: 72.14495342605589
          - type: euclidean_precision
            value: 69.85421299728193
          - type: euclidean_recall
            value: 74.5910290237467
          - type: manhattan_accuracy
            value: 87.59611372712642
          - type: manhattan_ap
            value: 78.78523756706264
          - type: manhattan_f1
            value: 71.86499137718648
          - type: manhattan_precision
            value: 67.39833641404806
          - type: manhattan_recall
            value: 76.96569920844327
          - type: max_accuracy
            value: 87.78089050485785
          - type: max_ap
            value: 79.64487116574168
          - type: max_f1
            value: 72.46563021970964
        task:
          type: PairClassification
      - dataset:
          config: default
          name: MTEB TwitterURLCorpus
          revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
          split: test
          type: mteb/twitterurlcorpus-pairclassification
        metrics:
          - type: cos_sim_accuracy
            value: 89.98719292117825
          - type: cos_sim_ap
            value: 87.58146137353202
          - type: cos_sim_f1
            value: 80.28543232369239
          - type: cos_sim_precision
            value: 79.1735289714029
          - type: cos_sim_recall
            value: 81.42901139513397
          - type: dot_accuracy
            value: 88.9199363526992
          - type: dot_ap
            value: 84.98499998630417
          - type: dot_f1
            value: 78.21951400757969
          - type: dot_precision
            value: 75.58523624874336
          - type: dot_recall
            value: 81.04404065291038
          - type: euclidean_accuracy
            value: 89.77374160748244
          - type: euclidean_ap
            value: 87.35151562835209
          - type: euclidean_f1
            value: 79.92160922940393
          - type: euclidean_precision
            value: 76.88531587933979
          - type: euclidean_recall
            value: 83.20757622420696
          - type: manhattan_accuracy
            value: 89.72717041176699
          - type: manhattan_ap
            value: 87.34065592142515
          - type: manhattan_f1
            value: 79.85603419187943
          - type: manhattan_precision
            value: 77.82243332115455
          - type: manhattan_recall
            value: 81.99876809362489
          - type: max_accuracy
            value: 89.98719292117825
          - type: max_ap
            value: 87.58146137353202
          - type: max_f1
            value: 80.28543232369239
        task:
          type: PairClassification
      - dataset:
          config: default
          name: MTEB AFQMC
          revision: b44c3b011063adb25877c13823db83bb193913c4
          split: validation
          type: C-MTEB/AFQMC
        metrics:
          - type: cos_sim_pearson
            value: 53.45954203592337
          - type: cos_sim_spearman
            value: 58.42154680418638
          - type: euclidean_pearson
            value: 56.41543791722753
          - type: euclidean_spearman
            value: 58.39328016640146
          - type: manhattan_pearson
            value: 56.318510356833876
          - type: manhattan_spearman
            value: 58.28423447818184
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB ATEC
          revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865
          split: test
          type: C-MTEB/ATEC
        metrics:
          - type: cos_sim_pearson
            value: 50.78356460675945
          - type: cos_sim_spearman
            value: 55.6530411663269
          - type: euclidean_pearson
            value: 56.50763660417816
          - type: euclidean_spearman
            value: 55.733823335669065
          - type: manhattan_pearson
            value: 56.45323093512866
          - type: manhattan_spearman
            value: 55.63248619032702
        task:
          type: STS
      - dataset:
          config: zh
          name: MTEB AmazonReviewsClassification (zh)
          revision: 1399c76144fd37290681b995c656ef9b2e06e26d
          split: test
          type: mteb/amazon_reviews_multi
        metrics:
          - type: accuracy
            value: 47.209999999999994
          - type: f1
            value: 46.08892432018655
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB BQ
          revision: e3dda5e115e487b39ec7e618c0c6a29137052a55
          split: test
          type: C-MTEB/BQ
        metrics:
          - type: cos_sim_pearson
            value: 70.25573992001478
          - type: cos_sim_spearman
            value: 73.85247134951433
          - type: euclidean_pearson
            value: 72.60033082168442
          - type: euclidean_spearman
            value: 73.72445893756499
          - type: manhattan_pearson
            value: 72.59932284620231
          - type: manhattan_spearman
            value: 73.68002490614583
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB CLSClusteringP2P
          revision: 4b6227591c6c1a73bc76b1055f3b7f3588e72476
          split: test
          type: C-MTEB/CLSClusteringP2P
        metrics:
          - type: v_measure
            value: 45.21317724305628
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB CLSClusteringS2S
          revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f
          split: test
          type: C-MTEB/CLSClusteringS2S
        metrics:
          - type: v_measure
            value: 42.49825170976724
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB CMedQAv1
          revision: 8d7f1e942507dac42dc58017c1a001c3717da7df
          split: test
          type: C-MTEB/CMedQAv1-reranking
        metrics:
          - type: map
            value: 88.15661686810597
          - type: mrr
            value: 90.11222222222223
        task:
          type: Reranking
      - dataset:
          config: default
          name: MTEB CMedQAv2
          revision: 23d186750531a14a0357ca22cd92d712fd512ea0
          split: test
          type: C-MTEB/CMedQAv2-reranking
        metrics:
          - type: map
            value: 88.1204726064383
          - type: mrr
            value: 90.20142857142858
        task:
          type: Reranking
      - dataset:
          config: default
          name: MTEB CmedqaRetrieval
          revision: cd540c506dae1cf9e9a59c3e06f42030d54e7301
          split: dev
          type: C-MTEB/CmedqaRetrieval
        metrics:
          - type: map_at_1
            value: 27.224999999999998
          - type: map_at_10
            value: 40.169
          - type: map_at_100
            value: 42
          - type: map_at_1000
            value: 42.109
          - type: map_at_3
            value: 35.76
          - type: map_at_5
            value: 38.221
          - type: mrr_at_1
            value: 40.56
          - type: mrr_at_10
            value: 49.118
          - type: mrr_at_100
            value: 50.092999999999996
          - type: mrr_at_1000
            value: 50.133
          - type: mrr_at_3
            value: 46.507
          - type: mrr_at_5
            value: 47.973
          - type: ndcg_at_1
            value: 40.56
          - type: ndcg_at_10
            value: 46.972
          - type: ndcg_at_100
            value: 54.04
          - type: ndcg_at_1000
            value: 55.862
          - type: ndcg_at_3
            value: 41.36
          - type: ndcg_at_5
            value: 43.704
          - type: precision_at_1
            value: 40.56
          - type: precision_at_10
            value: 10.302999999999999
          - type: precision_at_100
            value: 1.606
          - type: precision_at_1000
            value: 0.184
          - type: precision_at_3
            value: 23.064
          - type: precision_at_5
            value: 16.764000000000003
          - type: recall_at_1
            value: 27.224999999999998
          - type: recall_at_10
            value: 58.05200000000001
          - type: recall_at_100
            value: 87.092
          - type: recall_at_1000
            value: 99.099
          - type: recall_at_3
            value: 41.373
          - type: recall_at_5
            value: 48.453
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB Cmnli
          revision: 41bc36f332156f7adc9e38f53777c959b2ae9766
          split: validation
          type: C-MTEB/CMNLI
        metrics:
          - type: cos_sim_accuracy
            value: 77.40228502705953
          - type: cos_sim_ap
            value: 86.22359172956327
          - type: cos_sim_f1
            value: 78.96328293736501
          - type: cos_sim_precision
            value: 73.36945615091311
          - type: cos_sim_recall
            value: 85.48047696983868
          - type: dot_accuracy
            value: 75.53818400481059
          - type: dot_ap
            value: 83.70164011305312
          - type: dot_f1
            value: 77.67298719348754
          - type: dot_precision
            value: 67.49482401656314
          - type: dot_recall
            value: 91.46598082768296
          - type: euclidean_accuracy
            value: 77.94347564642213
          - type: euclidean_ap
            value: 86.4652108728609
          - type: euclidean_f1
            value: 79.15555555555555
          - type: euclidean_precision
            value: 75.41816641964853
          - type: euclidean_recall
            value: 83.28267477203647
          - type: manhattan_accuracy
            value: 77.45039085989175
          - type: manhattan_ap
            value: 86.09986583900665
          - type: manhattan_f1
            value: 78.93669264438988
          - type: manhattan_precision
            value: 72.63261296660117
          - type: manhattan_recall
            value: 86.43909282207154
          - type: max_accuracy
            value: 77.94347564642213
          - type: max_ap
            value: 86.4652108728609
          - type: max_f1
            value: 79.15555555555555
        task:
          type: PairClassification
      - dataset:
          config: default
          name: MTEB CovidRetrieval
          revision: 1271c7809071a13532e05f25fb53511ffce77117
          split: dev
          type: C-MTEB/CovidRetrieval
        metrics:
          - type: map_at_1
            value: 69.336
          - type: map_at_10
            value: 77.16
          - type: map_at_100
            value: 77.47500000000001
          - type: map_at_1000
            value: 77.482
          - type: map_at_3
            value: 75.42999999999999
          - type: map_at_5
            value: 76.468
          - type: mrr_at_1
            value: 69.44200000000001
          - type: mrr_at_10
            value: 77.132
          - type: mrr_at_100
            value: 77.43299999999999
          - type: mrr_at_1000
            value: 77.44
          - type: mrr_at_3
            value: 75.395
          - type: mrr_at_5
            value: 76.459
          - type: ndcg_at_1
            value: 69.547
          - type: ndcg_at_10
            value: 80.794
          - type: ndcg_at_100
            value: 82.245
          - type: ndcg_at_1000
            value: 82.40899999999999
          - type: ndcg_at_3
            value: 77.303
          - type: ndcg_at_5
            value: 79.168
          - type: precision_at_1
            value: 69.547
          - type: precision_at_10
            value: 9.305
          - type: precision_at_100
            value: 0.9979999999999999
          - type: precision_at_1000
            value: 0.101
          - type: precision_at_3
            value: 27.749000000000002
          - type: precision_at_5
            value: 17.576
          - type: recall_at_1
            value: 69.336
          - type: recall_at_10
            value: 92.097
          - type: recall_at_100
            value: 98.736
          - type: recall_at_1000
            value: 100
          - type: recall_at_3
            value: 82.64
          - type: recall_at_5
            value: 87.144
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB DuRetrieval
          revision: a1a333e290fe30b10f3f56498e3a0d911a693ced
          split: dev
          type: C-MTEB/DuRetrieval
        metrics:
          - type: map_at_1
            value: 26.817999999999998
          - type: map_at_10
            value: 82.67
          - type: map_at_100
            value: 85.304
          - type: map_at_1000
            value: 85.334
          - type: map_at_3
            value: 57.336
          - type: map_at_5
            value: 72.474
          - type: mrr_at_1
            value: 91.45
          - type: mrr_at_10
            value: 94.272
          - type: mrr_at_100
            value: 94.318
          - type: mrr_at_1000
            value: 94.32000000000001
          - type: mrr_at_3
            value: 94
          - type: mrr_at_5
            value: 94.17699999999999
          - type: ndcg_at_1
            value: 91.45
          - type: ndcg_at_10
            value: 89.404
          - type: ndcg_at_100
            value: 91.724
          - type: ndcg_at_1000
            value: 91.973
          - type: ndcg_at_3
            value: 88.104
          - type: ndcg_at_5
            value: 87.25699999999999
          - type: precision_at_1
            value: 91.45
          - type: precision_at_10
            value: 42.585
          - type: precision_at_100
            value: 4.838
          - type: precision_at_1000
            value: 0.49
          - type: precision_at_3
            value: 78.8
          - type: precision_at_5
            value: 66.66
          - type: recall_at_1
            value: 26.817999999999998
          - type: recall_at_10
            value: 90.67
          - type: recall_at_100
            value: 98.36200000000001
          - type: recall_at_1000
            value: 99.583
          - type: recall_at_3
            value: 59.614999999999995
          - type: recall_at_5
            value: 77.05199999999999
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB EcomRetrieval
          revision: 687de13dc7294d6fd9be10c6945f9e8fec8166b9
          split: dev
          type: C-MTEB/EcomRetrieval
        metrics:
          - type: map_at_1
            value: 47.699999999999996
          - type: map_at_10
            value: 57.589999999999996
          - type: map_at_100
            value: 58.226
          - type: map_at_1000
            value: 58.251
          - type: map_at_3
            value: 55.233
          - type: map_at_5
            value: 56.633
          - type: mrr_at_1
            value: 47.699999999999996
          - type: mrr_at_10
            value: 57.589999999999996
          - type: mrr_at_100
            value: 58.226
          - type: mrr_at_1000
            value: 58.251
          - type: mrr_at_3
            value: 55.233
          - type: mrr_at_5
            value: 56.633
          - type: ndcg_at_1
            value: 47.699999999999996
          - type: ndcg_at_10
            value: 62.505
          - type: ndcg_at_100
            value: 65.517
          - type: ndcg_at_1000
            value: 66.19800000000001
          - type: ndcg_at_3
            value: 57.643
          - type: ndcg_at_5
            value: 60.181
          - type: precision_at_1
            value: 47.699999999999996
          - type: precision_at_10
            value: 7.8
          - type: precision_at_100
            value: 0.919
          - type: precision_at_1000
            value: 0.097
          - type: precision_at_3
            value: 21.532999999999998
          - type: precision_at_5
            value: 14.16
          - type: recall_at_1
            value: 47.699999999999996
          - type: recall_at_10
            value: 78
          - type: recall_at_100
            value: 91.9
          - type: recall_at_1000
            value: 97.3
          - type: recall_at_3
            value: 64.60000000000001
          - type: recall_at_5
            value: 70.8
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB IFlyTek
          revision: 421605374b29664c5fc098418fe20ada9bd55f8a
          split: validation
          type: C-MTEB/IFlyTek-classification
        metrics:
          - type: accuracy
            value: 44.84801846864178
          - type: f1
            value: 37.47347897956339
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB JDReview
          revision: b7c64bd89eb87f8ded463478346f76731f07bf8b
          split: test
          type: C-MTEB/JDReview-classification
        metrics:
          - type: accuracy
            value: 85.81613508442777
          - type: ap
            value: 52.68244615477374
          - type: f1
            value: 80.0445640948843
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB LCQMC
          revision: 17f9b096f80380fce5ed12a9be8be7784b337daf
          split: test
          type: C-MTEB/LCQMC
        metrics:
          - type: cos_sim_pearson
            value: 69.57786502217138
          - type: cos_sim_spearman
            value: 75.39106054489906
          - type: euclidean_pearson
            value: 73.72082954602402
          - type: euclidean_spearman
            value: 75.14421475913619
          - type: manhattan_pearson
            value: 73.62463076633642
          - type: manhattan_spearman
            value: 75.01301565104112
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB MMarcoReranking
          revision: None
          split: dev
          type: C-MTEB/Mmarco-reranking
        metrics:
          - type: map
            value: 29.143797057999134
          - type: mrr
            value: 28.08174603174603
        task:
          type: Reranking
      - dataset:
          config: default
          name: MTEB MMarcoRetrieval
          revision: 539bbde593d947e2a124ba72651aafc09eb33fc2
          split: dev
          type: C-MTEB/MMarcoRetrieval
        metrics:
          - type: map_at_1
            value: 70.492
          - type: map_at_10
            value: 79.501
          - type: map_at_100
            value: 79.728
          - type: map_at_1000
            value: 79.735
          - type: map_at_3
            value: 77.77
          - type: map_at_5
            value: 78.851
          - type: mrr_at_1
            value: 72.822
          - type: mrr_at_10
            value: 80.001
          - type: mrr_at_100
            value: 80.19
          - type: mrr_at_1000
            value: 80.197
          - type: mrr_at_3
            value: 78.484
          - type: mrr_at_5
            value: 79.42099999999999
          - type: ndcg_at_1
            value: 72.822
          - type: ndcg_at_10
            value: 83.013
          - type: ndcg_at_100
            value: 84.013
          - type: ndcg_at_1000
            value: 84.20400000000001
          - type: ndcg_at_3
            value: 79.728
          - type: ndcg_at_5
            value: 81.542
          - type: precision_at_1
            value: 72.822
          - type: precision_at_10
            value: 9.917
          - type: precision_at_100
            value: 1.042
          - type: precision_at_1000
            value: 0.106
          - type: precision_at_3
            value: 29.847
          - type: precision_at_5
            value: 18.871
          - type: recall_at_1
            value: 70.492
          - type: recall_at_10
            value: 93.325
          - type: recall_at_100
            value: 97.822
          - type: recall_at_1000
            value: 99.319
          - type: recall_at_3
            value: 84.636
          - type: recall_at_5
            value: 88.93100000000001
        task:
          type: Retrieval
      - dataset:
          config: zh-CN
          name: MTEB MassiveIntentClassification (zh-CN)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 76.88298587760592
          - type: f1
            value: 73.89001762017176
        task:
          type: Classification
      - dataset:
          config: zh-CN
          name: MTEB MassiveScenarioClassification (zh-CN)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 80.76328177538669
          - type: f1
            value: 80.24718532423358
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB MedicalRetrieval
          revision: 2039188fb5800a9803ba5048df7b76e6fb151fc6
          split: dev
          type: C-MTEB/MedicalRetrieval
        metrics:
          - type: map_at_1
            value: 49.6
          - type: map_at_10
            value: 55.620999999999995
          - type: map_at_100
            value: 56.204
          - type: map_at_1000
            value: 56.251
          - type: map_at_3
            value: 54.132999999999996
          - type: map_at_5
            value: 54.933
          - type: mrr_at_1
            value: 49.7
          - type: mrr_at_10
            value: 55.67100000000001
          - type: mrr_at_100
            value: 56.254000000000005
          - type: mrr_at_1000
            value: 56.301
          - type: mrr_at_3
            value: 54.18300000000001
          - type: mrr_at_5
            value: 54.983000000000004
          - type: ndcg_at_1
            value: 49.6
          - type: ndcg_at_10
            value: 58.645
          - type: ndcg_at_100
            value: 61.789
          - type: ndcg_at_1000
            value: 63.219
          - type: ndcg_at_3
            value: 55.567
          - type: ndcg_at_5
            value: 57.008
          - type: precision_at_1
            value: 49.6
          - type: precision_at_10
            value: 6.819999999999999
          - type: precision_at_100
            value: 0.836
          - type: precision_at_1000
            value: 0.095
          - type: precision_at_3
            value: 19.900000000000002
          - type: precision_at_5
            value: 12.64
          - type: recall_at_1
            value: 49.6
          - type: recall_at_10
            value: 68.2
          - type: recall_at_100
            value: 83.6
          - type: recall_at_1000
            value: 95.3
          - type: recall_at_3
            value: 59.699999999999996
          - type: recall_at_5
            value: 63.2
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB MultilingualSentiment
          revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a
          split: validation
          type: C-MTEB/MultilingualSentiment-classification
        metrics:
          - type: accuracy
            value: 74.45666666666666
          - type: f1
            value: 74.32582402190089
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB Ocnli
          revision: 66e76a618a34d6d565d5538088562851e6daa7ec
          split: validation
          type: C-MTEB/OCNLI
        metrics:
          - type: cos_sim_accuracy
            value: 80.67135896047645
          - type: cos_sim_ap
            value: 87.60421240712051
          - type: cos_sim_f1
            value: 82.1304131408661
          - type: cos_sim_precision
            value: 77.68361581920904
          - type: cos_sim_recall
            value: 87.11721224920802
          - type: dot_accuracy
            value: 79.04710341093666
          - type: dot_ap
            value: 85.6370059719336
          - type: dot_f1
            value: 80.763723150358
          - type: dot_precision
            value: 73.69337979094077
          - type: dot_recall
            value: 89.33474128827878
          - type: euclidean_accuracy
            value: 81.05035192203573
          - type: euclidean_ap
            value: 87.7880240053663
          - type: euclidean_f1
            value: 82.50244379276637
          - type: euclidean_precision
            value: 76.7970882620564
          - type: euclidean_recall
            value: 89.1235480464625
          - type: manhattan_accuracy
            value: 80.61721710882512
          - type: manhattan_ap
            value: 87.43568120591175
          - type: manhattan_f1
            value: 81.89526184538653
          - type: manhattan_precision
            value: 77.5992438563327
          - type: manhattan_recall
            value: 86.6948257655755
          - type: max_accuracy
            value: 81.05035192203573
          - type: max_ap
            value: 87.7880240053663
          - type: max_f1
            value: 82.50244379276637
        task:
          type: PairClassification
      - dataset:
          config: default
          name: MTEB OnlineShopping
          revision: e610f2ebd179a8fda30ae534c3878750a96db120
          split: test
          type: C-MTEB/OnlineShopping-classification
        metrics:
          - type: accuracy
            value: 93.5
          - type: ap
            value: 91.31357903446782
          - type: f1
            value: 93.48088994006616
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB PAWSX
          revision: 9c6a90e430ac22b5779fb019a23e820b11a8b5e1
          split: test
          type: C-MTEB/PAWSX
        metrics:
          - type: cos_sim_pearson
            value: 36.93293453538077
          - type: cos_sim_spearman
            value: 42.45972506308574
          - type: euclidean_pearson
            value: 42.34945133152159
          - type: euclidean_spearman
            value: 42.331610303674644
          - type: manhattan_pearson
            value: 42.31455070249498
          - type: manhattan_spearman
            value: 42.19887982891834
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB QBQTC
          revision: 790b0510dc52b1553e8c49f3d2afb48c0e5c48b7
          split: test
          type: C-MTEB/QBQTC
        metrics:
          - type: cos_sim_pearson
            value: 33.683290790043785
          - type: cos_sim_spearman
            value: 35.149171171202994
          - type: euclidean_pearson
            value: 32.33806561267862
          - type: euclidean_spearman
            value: 34.483576387347966
          - type: manhattan_pearson
            value: 32.47629754599608
          - type: manhattan_spearman
            value: 34.66434471867615
        task:
          type: STS
      - dataset:
          config: zh
          name: MTEB STS22 (zh)
          revision: eea2b4fe26a775864c896887d910b76a8098ad3f
          split: test
          type: mteb/sts22-crosslingual-sts
        metrics:
          - type: cos_sim_pearson
            value: 66.46322760516104
          - type: cos_sim_spearman
            value: 67.398478319726
          - type: euclidean_pearson
            value: 64.7223480293625
          - type: euclidean_spearman
            value: 66.83118568812951
          - type: manhattan_pearson
            value: 64.88440039828305
          - type: manhattan_spearman
            value: 66.80429458952257
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB STSB
          revision: 0cde68302b3541bb8b3c340dc0644b0b745b3dc0
          split: test
          type: C-MTEB/STSB
        metrics:
          - type: cos_sim_pearson
            value: 79.08991383232105
          - type: cos_sim_spearman
            value: 79.39715677296854
          - type: euclidean_pearson
            value: 78.63201279320496
          - type: euclidean_spearman
            value: 79.40262660785731
          - type: manhattan_pearson
            value: 78.98138363146906
          - type: manhattan_spearman
            value: 79.79968413014194
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB T2Reranking
          revision: 76631901a18387f85eaa53e5450019b87ad58ef9
          split: dev
          type: C-MTEB/T2Reranking
        metrics:
          - type: map
            value: 67.43289278789972
          - type: mrr
            value: 77.53012460908535
        task:
          type: Reranking
      - dataset:
          config: default
          name: MTEB T2Retrieval
          revision: 8731a845f1bf500a4f111cf1070785c793d10e64
          split: dev
          type: C-MTEB/T2Retrieval
        metrics:
          - type: map_at_1
            value: 27.733999999999998
          - type: map_at_10
            value: 78.24799999999999
          - type: map_at_100
            value: 81.765
          - type: map_at_1000
            value: 81.824
          - type: map_at_3
            value: 54.92
          - type: map_at_5
            value: 67.61399999999999
          - type: mrr_at_1
            value: 90.527
          - type: mrr_at_10
            value: 92.843
          - type: mrr_at_100
            value: 92.927
          - type: mrr_at_1000
            value: 92.93
          - type: mrr_at_3
            value: 92.45100000000001
          - type: mrr_at_5
            value: 92.693
          - type: ndcg_at_1
            value: 90.527
          - type: ndcg_at_10
            value: 85.466
          - type: ndcg_at_100
            value: 88.846
          - type: ndcg_at_1000
            value: 89.415
          - type: ndcg_at_3
            value: 86.768
          - type: ndcg_at_5
            value: 85.46000000000001
          - type: precision_at_1
            value: 90.527
          - type: precision_at_10
            value: 42.488
          - type: precision_at_100
            value: 5.024
          - type: precision_at_1000
            value: 0.516
          - type: precision_at_3
            value: 75.907
          - type: precision_at_5
            value: 63.727000000000004
          - type: recall_at_1
            value: 27.733999999999998
          - type: recall_at_10
            value: 84.346
          - type: recall_at_100
            value: 95.536
          - type: recall_at_1000
            value: 98.42999999999999
          - type: recall_at_3
            value: 56.455
          - type: recall_at_5
            value: 70.755
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB TNews
          revision: 317f262bf1e6126357bbe89e875451e4b0938fe4
          split: validation
          type: C-MTEB/TNews-classification
        metrics:
          - type: accuracy
            value: 49.952000000000005
          - type: f1
            value: 48.264617195258054
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB ThuNewsClusteringP2P
          revision: 5798586b105c0434e4f0fe5e767abe619442cf93
          split: test
          type: C-MTEB/ThuNewsClusteringP2P
        metrics:
          - type: v_measure
            value: 68.23769904483508
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB ThuNewsClusteringS2S
          revision: 8a8b2caeda43f39e13c4bc5bea0f8a667896e10d
          split: test
          type: C-MTEB/ThuNewsClusteringS2S
        metrics:
          - type: v_measure
            value: 62.50294403136556
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB VideoRetrieval
          revision: 58c2597a5943a2ba48f4668c3b90d796283c5639
          split: dev
          type: C-MTEB/VideoRetrieval
        metrics:
          - type: map_at_1
            value: 54
          - type: map_at_10
            value: 63.668
          - type: map_at_100
            value: 64.217
          - type: map_at_1000
            value: 64.23100000000001
          - type: map_at_3
            value: 61.7
          - type: map_at_5
            value: 62.870000000000005
          - type: mrr_at_1
            value: 54
          - type: mrr_at_10
            value: 63.668
          - type: mrr_at_100
            value: 64.217
          - type: mrr_at_1000
            value: 64.23100000000001
          - type: mrr_at_3
            value: 61.7
          - type: mrr_at_5
            value: 62.870000000000005
          - type: ndcg_at_1
            value: 54
          - type: ndcg_at_10
            value: 68.11399999999999
          - type: ndcg_at_100
            value: 70.723
          - type: ndcg_at_1000
            value: 71.123
          - type: ndcg_at_3
            value: 64.074
          - type: ndcg_at_5
            value: 66.178
          - type: precision_at_1
            value: 54
          - type: precision_at_10
            value: 8.200000000000001
          - type: precision_at_100
            value: 0.941
          - type: precision_at_1000
            value: 0.097
          - type: precision_at_3
            value: 23.633000000000003
          - type: precision_at_5
            value: 15.2
          - type: recall_at_1
            value: 54
          - type: recall_at_10
            value: 82
          - type: recall_at_100
            value: 94.1
          - type: recall_at_1000
            value: 97.3
          - type: recall_at_3
            value: 70.89999999999999
          - type: recall_at_5
            value: 76
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB Waimai
          revision: 339287def212450dcaa9df8c22bf93e9980c7023
          split: test
          type: C-MTEB/waimai-classification
        metrics:
          - type: accuracy
            value: 86.63000000000001
          - type: ap
            value: 69.99457882599567
          - type: f1
            value: 85.07735617998541
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB 8TagsClustering
          revision: None
          split: test
          type: PL-MTEB/8tags-clustering
        metrics:
          - type: v_measure
            value: 44.594104491193555
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB AllegroReviews
          revision: None
          split: test
          type: PL-MTEB/allegro-reviews
        metrics:
          - type: accuracy
            value: 63.97614314115309
          - type: f1
            value: 52.15634261679283
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB ArguAna-PL
          revision: 63fc86750af76253e8c760fc9e534bbf24d260a2
          split: test
          type: clarin-knext/arguana-pl
        metrics:
          - type: map_at_1
            value: 32.646
          - type: map_at_10
            value: 47.963
          - type: map_at_100
            value: 48.789
          - type: map_at_1000
            value: 48.797000000000004
          - type: map_at_3
            value: 43.196
          - type: map_at_5
            value: 46.016
          - type: mrr_at_1
            value: 33.073
          - type: mrr_at_10
            value: 48.126000000000005
          - type: mrr_at_100
            value: 48.946
          - type: mrr_at_1000
            value: 48.953
          - type: mrr_at_3
            value: 43.374
          - type: mrr_at_5
            value: 46.147
          - type: ndcg_at_1
            value: 32.646
          - type: ndcg_at_10
            value: 56.481
          - type: ndcg_at_100
            value: 59.922
          - type: ndcg_at_1000
            value: 60.07
          - type: ndcg_at_3
            value: 46.675
          - type: ndcg_at_5
            value: 51.76500000000001
          - type: precision_at_1
            value: 32.646
          - type: precision_at_10
            value: 8.371
          - type: precision_at_100
            value: 0.9860000000000001
          - type: precision_at_1000
            value: 0.1
          - type: precision_at_3
            value: 18.919
          - type: precision_at_5
            value: 13.825999999999999
          - type: recall_at_1
            value: 32.646
          - type: recall_at_10
            value: 83.71300000000001
          - type: recall_at_100
            value: 98.578
          - type: recall_at_1000
            value: 99.644
          - type: recall_at_3
            value: 56.757000000000005
          - type: recall_at_5
            value: 69.132
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB CBD
          revision: None
          split: test
          type: PL-MTEB/cbd
        metrics:
          - type: accuracy
            value: 68.56
          - type: ap
            value: 23.310493680488513
          - type: f1
            value: 58.85369533105693
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB CDSC-E
          revision: None
          split: test
          type: PL-MTEB/cdsce-pairclassification
        metrics:
          - type: cos_sim_accuracy
            value: 88.5
          - type: cos_sim_ap
            value: 72.42140924378361
          - type: cos_sim_f1
            value: 66.0919540229885
          - type: cos_sim_precision
            value: 72.78481012658227
          - type: cos_sim_recall
            value: 60.526315789473685
          - type: dot_accuracy
            value: 88.5
          - type: dot_ap
            value: 72.42140924378361
          - type: dot_f1
            value: 66.0919540229885
          - type: dot_precision
            value: 72.78481012658227
          - type: dot_recall
            value: 60.526315789473685
          - type: euclidean_accuracy
            value: 88.5
          - type: euclidean_ap
            value: 72.42140924378361
          - type: euclidean_f1
            value: 66.0919540229885
          - type: euclidean_precision
            value: 72.78481012658227
          - type: euclidean_recall
            value: 60.526315789473685
          - type: manhattan_accuracy
            value: 88.5
          - type: manhattan_ap
            value: 72.49745515311696
          - type: manhattan_f1
            value: 66.0968660968661
          - type: manhattan_precision
            value: 72.04968944099379
          - type: manhattan_recall
            value: 61.05263157894737
          - type: max_accuracy
            value: 88.5
          - type: max_ap
            value: 72.49745515311696
          - type: max_f1
            value: 66.0968660968661
        task:
          type: PairClassification
      - dataset:
          config: default
          name: MTEB CDSC-R
          revision: None
          split: test
          type: PL-MTEB/cdscr-sts
        metrics:
          - type: cos_sim_pearson
            value: 90.32269765590145
          - type: cos_sim_spearman
            value: 89.73666311491672
          - type: euclidean_pearson
            value: 88.2933868516544
          - type: euclidean_spearman
            value: 89.73666311491672
          - type: manhattan_pearson
            value: 88.33474590219448
          - type: manhattan_spearman
            value: 89.8548364866583
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB DBPedia-PL
          revision: 76afe41d9af165cc40999fcaa92312b8b012064a
          split: test
          type: clarin-knext/dbpedia-pl
        metrics:
          - type: map_at_1
            value: 7.632999999999999
          - type: map_at_10
            value: 16.426
          - type: map_at_100
            value: 22.651
          - type: map_at_1000
            value: 24.372
          - type: map_at_3
            value: 11.706
          - type: map_at_5
            value: 13.529
          - type: mrr_at_1
            value: 60.75000000000001
          - type: mrr_at_10
            value: 68.613
          - type: mrr_at_100
            value: 69.001
          - type: mrr_at_1000
            value: 69.021
          - type: mrr_at_3
            value: 67
          - type: mrr_at_5
            value: 67.925
          - type: ndcg_at_1
            value: 49.875
          - type: ndcg_at_10
            value: 36.978
          - type: ndcg_at_100
            value: 40.031
          - type: ndcg_at_1000
            value: 47.566
          - type: ndcg_at_3
            value: 41.148
          - type: ndcg_at_5
            value: 38.702
          - type: precision_at_1
            value: 60.75000000000001
          - type: precision_at_10
            value: 29.7
          - type: precision_at_100
            value: 9.278
          - type: precision_at_1000
            value: 2.099
          - type: precision_at_3
            value: 44
          - type: precision_at_5
            value: 37.6
          - type: recall_at_1
            value: 7.632999999999999
          - type: recall_at_10
            value: 22.040000000000003
          - type: recall_at_100
            value: 44.024
          - type: recall_at_1000
            value: 67.848
          - type: recall_at_3
            value: 13.093
          - type: recall_at_5
            value: 15.973
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB FiQA-PL
          revision: 2e535829717f8bf9dc829b7f911cc5bbd4e6608e
          split: test
          type: clarin-knext/fiqa-pl
        metrics:
          - type: map_at_1
            value: 15.473
          - type: map_at_10
            value: 24.579
          - type: map_at_100
            value: 26.387
          - type: map_at_1000
            value: 26.57
          - type: map_at_3
            value: 21.278
          - type: map_at_5
            value: 23.179
          - type: mrr_at_1
            value: 30.709999999999997
          - type: mrr_at_10
            value: 38.994
          - type: mrr_at_100
            value: 39.993
          - type: mrr_at_1000
            value: 40.044999999999995
          - type: mrr_at_3
            value: 36.342999999999996
          - type: mrr_at_5
            value: 37.846999999999994
          - type: ndcg_at_1
            value: 30.709999999999997
          - type: ndcg_at_10
            value: 31.608999999999998
          - type: ndcg_at_100
            value: 38.807
          - type: ndcg_at_1000
            value: 42.208
          - type: ndcg_at_3
            value: 28.086
          - type: ndcg_at_5
            value: 29.323
          - type: precision_at_1
            value: 30.709999999999997
          - type: precision_at_10
            value: 8.688
          - type: precision_at_100
            value: 1.608
          - type: precision_at_1000
            value: 0.22100000000000003
          - type: precision_at_3
            value: 18.724
          - type: precision_at_5
            value: 13.950999999999999
          - type: recall_at_1
            value: 15.473
          - type: recall_at_10
            value: 38.361000000000004
          - type: recall_at_100
            value: 65.2
          - type: recall_at_1000
            value: 85.789
          - type: recall_at_3
            value: 25.401
          - type: recall_at_5
            value: 30.875999999999998
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB HotpotQA-PL
          revision: a0bd479ac97b4ccb5bd6ce320c415d0bb4beb907
          split: test
          type: clarin-knext/hotpotqa-pl
        metrics:
          - type: map_at_1
            value: 38.096000000000004
          - type: map_at_10
            value: 51.44499999999999
          - type: map_at_100
            value: 52.325
          - type: map_at_1000
            value: 52.397000000000006
          - type: map_at_3
            value: 48.626999999999995
          - type: map_at_5
            value: 50.342
          - type: mrr_at_1
            value: 76.19200000000001
          - type: mrr_at_10
            value: 81.191
          - type: mrr_at_100
            value: 81.431
          - type: mrr_at_1000
            value: 81.443
          - type: mrr_at_3
            value: 80.30199999999999
          - type: mrr_at_5
            value: 80.85900000000001
          - type: ndcg_at_1
            value: 76.19200000000001
          - type: ndcg_at_10
            value: 60.9
          - type: ndcg_at_100
            value: 64.14699999999999
          - type: ndcg_at_1000
            value: 65.647
          - type: ndcg_at_3
            value: 56.818000000000005
          - type: ndcg_at_5
            value: 59.019999999999996
          - type: precision_at_1
            value: 76.19200000000001
          - type: precision_at_10
            value: 12.203
          - type: precision_at_100
            value: 1.478
          - type: precision_at_1000
            value: 0.168
          - type: precision_at_3
            value: 34.616
          - type: precision_at_5
            value: 22.515
          - type: recall_at_1
            value: 38.096000000000004
          - type: recall_at_10
            value: 61.013
          - type: recall_at_100
            value: 73.90299999999999
          - type: recall_at_1000
            value: 83.91
          - type: recall_at_3
            value: 51.92400000000001
          - type: recall_at_5
            value: 56.286
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB MSMARCO-PL
          revision: 8634c07806d5cce3a6138e260e59b81760a0a640
          split: test
          type: clarin-knext/msmarco-pl
        metrics:
          - type: map_at_1
            value: 1.548
          - type: map_at_10
            value: 11.049000000000001
          - type: map_at_100
            value: 28.874
          - type: map_at_1000
            value: 34.931
          - type: map_at_3
            value: 4.162
          - type: map_at_5
            value: 6.396
          - type: mrr_at_1
            value: 90.69800000000001
          - type: mrr_at_10
            value: 92.093
          - type: mrr_at_100
            value: 92.345
          - type: mrr_at_1000
            value: 92.345
          - type: mrr_at_3
            value: 91.86
          - type: mrr_at_5
            value: 91.86
          - type: ndcg_at_1
            value: 74.031
          - type: ndcg_at_10
            value: 63.978
          - type: ndcg_at_100
            value: 53.101
          - type: ndcg_at_1000
            value: 60.675999999999995
          - type: ndcg_at_3
            value: 71.421
          - type: ndcg_at_5
            value: 68.098
          - type: precision_at_1
            value: 90.69800000000001
          - type: precision_at_10
            value: 71.86
          - type: precision_at_100
            value: 31.395
          - type: precision_at_1000
            value: 5.981
          - type: precision_at_3
            value: 84.49600000000001
          - type: precision_at_5
            value: 79.07
          - type: recall_at_1
            value: 1.548
          - type: recall_at_10
            value: 12.149000000000001
          - type: recall_at_100
            value: 40.794999999999995
          - type: recall_at_1000
            value: 67.974
          - type: recall_at_3
            value: 4.244
          - type: recall_at_5
            value: 6.608
        task:
          type: Retrieval
      - dataset:
          config: pl
          name: MTEB MassiveIntentClassification (pl)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 73.55413584398119
          - type: f1
            value: 69.65610882318181
        task:
          type: Classification
      - dataset:
          config: pl
          name: MTEB MassiveScenarioClassification (pl)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 76.37188971082716
          - type: f1
            value: 75.64847309941361
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB NFCorpus-PL
          revision: 9a6f9567fda928260afed2de480d79c98bf0bec0
          split: test
          type: clarin-knext/nfcorpus-pl
        metrics:
          - type: map_at_1
            value: 4.919
          - type: map_at_10
            value: 10.834000000000001
          - type: map_at_100
            value: 13.38
          - type: map_at_1000
            value: 14.581
          - type: map_at_3
            value: 8.198
          - type: map_at_5
            value: 9.428
          - type: mrr_at_1
            value: 41.176
          - type: mrr_at_10
            value: 50.083
          - type: mrr_at_100
            value: 50.559
          - type: mrr_at_1000
            value: 50.604000000000006
          - type: mrr_at_3
            value: 47.936
          - type: mrr_at_5
            value: 49.407000000000004
          - type: ndcg_at_1
            value: 39.628
          - type: ndcg_at_10
            value: 30.098000000000003
          - type: ndcg_at_100
            value: 27.061
          - type: ndcg_at_1000
            value: 35.94
          - type: ndcg_at_3
            value: 35.135
          - type: ndcg_at_5
            value: 33.335
          - type: precision_at_1
            value: 41.176
          - type: precision_at_10
            value: 22.259999999999998
          - type: precision_at_100
            value: 6.712
          - type: precision_at_1000
            value: 1.9060000000000001
          - type: precision_at_3
            value: 33.23
          - type: precision_at_5
            value: 29.04
          - type: recall_at_1
            value: 4.919
          - type: recall_at_10
            value: 14.196
          - type: recall_at_100
            value: 26.948
          - type: recall_at_1000
            value: 59.211000000000006
          - type: recall_at_3
            value: 9.44
          - type: recall_at_5
            value: 11.569
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB NQ-PL
          revision: f171245712cf85dd4700b06bef18001578d0ca8d
          split: test
          type: clarin-knext/nq-pl
        metrics:
          - type: map_at_1
            value: 25.35
          - type: map_at_10
            value: 37.884
          - type: map_at_100
            value: 38.955
          - type: map_at_1000
            value: 39.007999999999996
          - type: map_at_3
            value: 34.239999999999995
          - type: map_at_5
            value: 36.398
          - type: mrr_at_1
            value: 28.737000000000002
          - type: mrr_at_10
            value: 39.973
          - type: mrr_at_100
            value: 40.844
          - type: mrr_at_1000
            value: 40.885
          - type: mrr_at_3
            value: 36.901
          - type: mrr_at_5
            value: 38.721
          - type: ndcg_at_1
            value: 28.708
          - type: ndcg_at_10
            value: 44.204
          - type: ndcg_at_100
            value: 48.978
          - type: ndcg_at_1000
            value: 50.33
          - type: ndcg_at_3
            value: 37.36
          - type: ndcg_at_5
            value: 40.912
          - type: precision_at_1
            value: 28.708
          - type: precision_at_10
            value: 7.367
          - type: precision_at_100
            value: 1.0030000000000001
          - type: precision_at_1000
            value: 0.11299999999999999
          - type: precision_at_3
            value: 17.034
          - type: precision_at_5
            value: 12.293999999999999
          - type: recall_at_1
            value: 25.35
          - type: recall_at_10
            value: 61.411
          - type: recall_at_100
            value: 82.599
          - type: recall_at_1000
            value: 92.903
          - type: recall_at_3
            value: 43.728
          - type: recall_at_5
            value: 51.854
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB PAC
          revision: None
          split: test
          type: laugustyniak/abusive-clauses-pl
        metrics:
          - type: accuracy
            value: 69.04141326382856
          - type: ap
            value: 77.49422763833996
          - type: f1
            value: 66.73472657783407
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB PPC
          revision: None
          split: test
          type: PL-MTEB/ppc-pairclassification
        metrics:
          - type: cos_sim_accuracy
            value: 81
          - type: cos_sim_ap
            value: 91.47194213011349
          - type: cos_sim_f1
            value: 84.73767885532592
          - type: cos_sim_precision
            value: 81.49847094801224
          - type: cos_sim_recall
            value: 88.24503311258279
          - type: dot_accuracy
            value: 81
          - type: dot_ap
            value: 91.47194213011349
          - type: dot_f1
            value: 84.73767885532592
          - type: dot_precision
            value: 81.49847094801224
          - type: dot_recall
            value: 88.24503311258279
          - type: euclidean_accuracy
            value: 81
          - type: euclidean_ap
            value: 91.47194213011349
          - type: euclidean_f1
            value: 84.73767885532592
          - type: euclidean_precision
            value: 81.49847094801224
          - type: euclidean_recall
            value: 88.24503311258279
          - type: manhattan_accuracy
            value: 81
          - type: manhattan_ap
            value: 91.46464475050571
          - type: manhattan_f1
            value: 84.48687350835321
          - type: manhattan_precision
            value: 81.31699846860643
          - type: manhattan_recall
            value: 87.91390728476821
          - type: max_accuracy
            value: 81
          - type: max_ap
            value: 91.47194213011349
          - type: max_f1
            value: 84.73767885532592
        task:
          type: PairClassification
      - dataset:
          config: default
          name: MTEB PSC
          revision: None
          split: test
          type: PL-MTEB/psc-pairclassification
        metrics:
          - type: cos_sim_accuracy
            value: 97.6808905380334
          - type: cos_sim_ap
            value: 99.27948611836348
          - type: cos_sim_f1
            value: 96.15975422427034
          - type: cos_sim_precision
            value: 96.90402476780186
          - type: cos_sim_recall
            value: 95.42682926829268
          - type: dot_accuracy
            value: 97.6808905380334
          - type: dot_ap
            value: 99.2794861183635
          - type: dot_f1
            value: 96.15975422427034
          - type: dot_precision
            value: 96.90402476780186
          - type: dot_recall
            value: 95.42682926829268
          - type: euclidean_accuracy
            value: 97.6808905380334
          - type: euclidean_ap
            value: 99.2794861183635
          - type: euclidean_f1
            value: 96.15975422427034
          - type: euclidean_precision
            value: 96.90402476780186
          - type: euclidean_recall
            value: 95.42682926829268
          - type: manhattan_accuracy
            value: 97.6808905380334
          - type: manhattan_ap
            value: 99.28715055268721
          - type: manhattan_f1
            value: 96.14791987673343
          - type: manhattan_precision
            value: 97.19626168224299
          - type: manhattan_recall
            value: 95.1219512195122
          - type: max_accuracy
            value: 97.6808905380334
          - type: max_ap
            value: 99.28715055268721
          - type: max_f1
            value: 96.15975422427034
        task:
          type: PairClassification
      - dataset:
          config: default
          name: MTEB PolEmo2.0-IN
          revision: None
          split: test
          type: PL-MTEB/polemo2_in
        metrics:
          - type: accuracy
            value: 86.16343490304708
          - type: f1
            value: 83.3442579486744
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB PolEmo2.0-OUT
          revision: None
          split: test
          type: PL-MTEB/polemo2_out
        metrics:
          - type: accuracy
            value: 68.40080971659918
          - type: f1
            value: 53.13720751142237
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB Quora-PL
          revision: 0be27e93455051e531182b85e85e425aba12e9d4
          split: test
          type: clarin-knext/quora-pl
        metrics:
          - type: map_at_1
            value: 63.322
          - type: map_at_10
            value: 76.847
          - type: map_at_100
            value: 77.616
          - type: map_at_1000
            value: 77.644
          - type: map_at_3
            value: 73.624
          - type: map_at_5
            value: 75.603
          - type: mrr_at_1
            value: 72.88
          - type: mrr_at_10
            value: 80.376
          - type: mrr_at_100
            value: 80.604
          - type: mrr_at_1000
            value: 80.61
          - type: mrr_at_3
            value: 78.92
          - type: mrr_at_5
            value: 79.869
          - type: ndcg_at_1
            value: 72.89999999999999
          - type: ndcg_at_10
            value: 81.43
          - type: ndcg_at_100
            value: 83.394
          - type: ndcg_at_1000
            value: 83.685
          - type: ndcg_at_3
            value: 77.62599999999999
          - type: ndcg_at_5
            value: 79.656
          - type: precision_at_1
            value: 72.89999999999999
          - type: precision_at_10
            value: 12.548
          - type: precision_at_100
            value: 1.4869999999999999
          - type: precision_at_1000
            value: 0.155
          - type: precision_at_3
            value: 34.027
          - type: precision_at_5
            value: 22.654
          - type: recall_at_1
            value: 63.322
          - type: recall_at_10
            value: 90.664
          - type: recall_at_100
            value: 97.974
          - type: recall_at_1000
            value: 99.636
          - type: recall_at_3
            value: 80.067
          - type: recall_at_5
            value: 85.526
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB SCIDOCS-PL
          revision: 45452b03f05560207ef19149545f168e596c9337
          split: test
          type: clarin-knext/scidocs-pl
        metrics:
          - type: map_at_1
            value: 3.95
          - type: map_at_10
            value: 9.658999999999999
          - type: map_at_100
            value: 11.384
          - type: map_at_1000
            value: 11.677
          - type: map_at_3
            value: 7.055
          - type: map_at_5
            value: 8.244
          - type: mrr_at_1
            value: 19.5
          - type: mrr_at_10
            value: 28.777
          - type: mrr_at_100
            value: 29.936
          - type: mrr_at_1000
            value: 30.009999999999998
          - type: mrr_at_3
            value: 25.55
          - type: mrr_at_5
            value: 27.284999999999997
          - type: ndcg_at_1
            value: 19.5
          - type: ndcg_at_10
            value: 16.589000000000002
          - type: ndcg_at_100
            value: 23.879
          - type: ndcg_at_1000
            value: 29.279
          - type: ndcg_at_3
            value: 15.719
          - type: ndcg_at_5
            value: 13.572000000000001
          - type: precision_at_1
            value: 19.5
          - type: precision_at_10
            value: 8.62
          - type: precision_at_100
            value: 1.924
          - type: precision_at_1000
            value: 0.322
          - type: precision_at_3
            value: 14.6
          - type: precision_at_5
            value: 11.78
          - type: recall_at_1
            value: 3.95
          - type: recall_at_10
            value: 17.477999999999998
          - type: recall_at_100
            value: 38.99
          - type: recall_at_1000
            value: 65.417
          - type: recall_at_3
            value: 8.883000000000001
          - type: recall_at_5
            value: 11.933
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB SICK-E-PL
          revision: None
          split: test
          type: PL-MTEB/sicke-pl-pairclassification
        metrics:
          - type: cos_sim_accuracy
            value: 83.48960456583775
          - type: cos_sim_ap
            value: 76.31522115825375
          - type: cos_sim_f1
            value: 70.35573122529645
          - type: cos_sim_precision
            value: 70.9934735315446
          - type: cos_sim_recall
            value: 69.72934472934473
          - type: dot_accuracy
            value: 83.48960456583775
          - type: dot_ap
            value: 76.31522115825373
          - type: dot_f1
            value: 70.35573122529645
          - type: dot_precision
            value: 70.9934735315446
          - type: dot_recall
            value: 69.72934472934473
          - type: euclidean_accuracy
            value: 83.48960456583775
          - type: euclidean_ap
            value: 76.31522115825373
          - type: euclidean_f1
            value: 70.35573122529645
          - type: euclidean_precision
            value: 70.9934735315446
          - type: euclidean_recall
            value: 69.72934472934473
          - type: manhattan_accuracy
            value: 83.46922136159804
          - type: manhattan_ap
            value: 76.18474601388084
          - type: manhattan_f1
            value: 70.34779490856937
          - type: manhattan_precision
            value: 70.83032490974729
          - type: manhattan_recall
            value: 69.87179487179486
          - type: max_accuracy
            value: 83.48960456583775
          - type: max_ap
            value: 76.31522115825375
          - type: max_f1
            value: 70.35573122529645
        task:
          type: PairClassification
      - dataset:
          config: default
          name: MTEB SICK-R-PL
          revision: None
          split: test
          type: PL-MTEB/sickr-pl-sts
        metrics:
          - type: cos_sim_pearson
            value: 77.95374883876302
          - type: cos_sim_spearman
            value: 73.77630219171942
          - type: euclidean_pearson
            value: 75.81927069594934
          - type: euclidean_spearman
            value: 73.7763211303831
          - type: manhattan_pearson
            value: 76.03126859057528
          - type: manhattan_spearman
            value: 73.96528138013369
        task:
          type: STS
      - dataset:
          config: pl
          name: MTEB STS22 (pl)
          revision: eea2b4fe26a775864c896887d910b76a8098ad3f
          split: test
          type: mteb/sts22-crosslingual-sts
        metrics:
          - type: cos_sim_pearson
            value: 37.388282764841826
          - type: cos_sim_spearman
            value: 40.83477184710897
          - type: euclidean_pearson
            value: 26.754737044177805
          - type: euclidean_spearman
            value: 40.83477184710897
          - type: manhattan_pearson
            value: 26.760453110872458
          - type: manhattan_spearman
            value: 41.034477441383856
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB SciFact-PL
          revision: 47932a35f045ef8ed01ba82bf9ff67f6e109207e
          split: test
          type: clarin-knext/scifact-pl
        metrics:
          - type: map_at_1
            value: 49.15
          - type: map_at_10
            value: 61.690999999999995
          - type: map_at_100
            value: 62.348000000000006
          - type: map_at_1000
            value: 62.38
          - type: map_at_3
            value: 58.824
          - type: map_at_5
            value: 60.662000000000006
          - type: mrr_at_1
            value: 51.333
          - type: mrr_at_10
            value: 62.731
          - type: mrr_at_100
            value: 63.245
          - type: mrr_at_1000
            value: 63.275000000000006
          - type: mrr_at_3
            value: 60.667
          - type: mrr_at_5
            value: 61.93300000000001
          - type: ndcg_at_1
            value: 51.333
          - type: ndcg_at_10
            value: 67.168
          - type: ndcg_at_100
            value: 69.833
          - type: ndcg_at_1000
            value: 70.56700000000001
          - type: ndcg_at_3
            value: 62.40599999999999
          - type: ndcg_at_5
            value: 65.029
          - type: precision_at_1
            value: 51.333
          - type: precision_at_10
            value: 9.333
          - type: precision_at_100
            value: 1.0699999999999998
          - type: precision_at_1000
            value: 0.11299999999999999
          - type: precision_at_3
            value: 25.333
          - type: precision_at_5
            value: 17.067
          - type: recall_at_1
            value: 49.15
          - type: recall_at_10
            value: 82.533
          - type: recall_at_100
            value: 94.167
          - type: recall_at_1000
            value: 99.667
          - type: recall_at_3
            value: 69.917
          - type: recall_at_5
            value: 76.356
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB TRECCOVID-PL
          revision: 81bcb408f33366c2a20ac54adafad1ae7e877fdd
          split: test
          type: clarin-knext/trec-covid-pl
        metrics:
          - type: map_at_1
            value: 0.261
          - type: map_at_10
            value: 2.1260000000000003
          - type: map_at_100
            value: 12.171999999999999
          - type: map_at_1000
            value: 26.884999999999998
          - type: map_at_3
            value: 0.695
          - type: map_at_5
            value: 1.134
          - type: mrr_at_1
            value: 96
          - type: mrr_at_10
            value: 96.952
          - type: mrr_at_100
            value: 96.952
          - type: mrr_at_1000
            value: 96.952
          - type: mrr_at_3
            value: 96.667
          - type: mrr_at_5
            value: 96.667
          - type: ndcg_at_1
            value: 92
          - type: ndcg_at_10
            value: 81.193
          - type: ndcg_at_100
            value: 61.129
          - type: ndcg_at_1000
            value: 51.157
          - type: ndcg_at_3
            value: 85.693
          - type: ndcg_at_5
            value: 84.129
          - type: precision_at_1
            value: 96
          - type: precision_at_10
            value: 85.39999999999999
          - type: precision_at_100
            value: 62.03999999999999
          - type: precision_at_1000
            value: 22.224
          - type: precision_at_3
            value: 88
          - type: precision_at_5
            value: 88
          - type: recall_at_1
            value: 0.261
          - type: recall_at_10
            value: 2.262
          - type: recall_at_100
            value: 14.981
          - type: recall_at_1000
            value: 46.837
          - type: recall_at_3
            value: 0.703
          - type: recall_at_5
            value: 1.172
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB AlloProfClusteringP2P
          revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
          split: test
          type: lyon-nlp/alloprof
        metrics:
          - type: v_measure
            value: 70.55290063940157
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB AlloProfClusteringS2S
          revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
          split: test
          type: lyon-nlp/alloprof
        metrics:
          - type: v_measure
            value: 55.41500719337263
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB AlloprofReranking
          revision: 666fdacebe0291776e86f29345663dfaf80a0db9
          split: test
          type: lyon-nlp/mteb-fr-reranking-alloprof-s2p
        metrics:
          - type: map
            value: 73.48697375332002
          - type: mrr
            value: 75.01836585523822
        task:
          type: Reranking
      - dataset:
          config: default
          name: MTEB AlloprofRetrieval
          revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
          split: test
          type: lyon-nlp/alloprof
        metrics:
          - type: map_at_1
            value: 38.454
          - type: map_at_10
            value: 51.605000000000004
          - type: map_at_100
            value: 52.653000000000006
          - type: map_at_1000
            value: 52.697
          - type: map_at_3
            value: 48.304
          - type: map_at_5
            value: 50.073
          - type: mrr_at_1
            value: 43.307
          - type: mrr_at_10
            value: 54.400000000000006
          - type: mrr_at_100
            value: 55.147999999999996
          - type: mrr_at_1000
            value: 55.174
          - type: mrr_at_3
            value: 51.77
          - type: mrr_at_5
            value: 53.166999999999994
          - type: ndcg_at_1
            value: 43.307
          - type: ndcg_at_10
            value: 57.891000000000005
          - type: ndcg_at_100
            value: 62.161
          - type: ndcg_at_1000
            value: 63.083
          - type: ndcg_at_3
            value: 51.851
          - type: ndcg_at_5
            value: 54.605000000000004
          - type: precision_at_1
            value: 43.307
          - type: precision_at_10
            value: 9.033
          - type: precision_at_100
            value: 1.172
          - type: precision_at_1000
            value: 0.127
          - type: precision_at_3
            value: 22.798
          - type: precision_at_5
            value: 15.492
          - type: recall_at_1
            value: 38.454
          - type: recall_at_10
            value: 74.166
          - type: recall_at_100
            value: 92.43599999999999
          - type: recall_at_1000
            value: 99.071
          - type: recall_at_3
            value: 58.087
          - type: recall_at_5
            value: 64.568
        task:
          type: Retrieval
      - dataset:
          config: fr
          name: MTEB AmazonReviewsClassification (fr)
          revision: 1399c76144fd37290681b995c656ef9b2e06e26d
          split: test
          type: mteb/amazon_reviews_multi
        metrics:
          - type: accuracy
            value: 53.474
          - type: f1
            value: 50.38275392350236
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB BSARDRetrieval
          revision: 5effa1b9b5fa3b0f9e12523e6e43e5f86a6e6d59
          split: test
          type: maastrichtlawtech/bsard
        metrics:
          - type: map_at_1
            value: 2.252
          - type: map_at_10
            value: 4.661
          - type: map_at_100
            value: 5.271
          - type: map_at_1000
            value: 5.3629999999999995
          - type: map_at_3
            value: 3.604
          - type: map_at_5
            value: 4.3020000000000005
          - type: mrr_at_1
            value: 2.252
          - type: mrr_at_10
            value: 4.661
          - type: mrr_at_100
            value: 5.271
          - type: mrr_at_1000
            value: 5.3629999999999995
          - type: mrr_at_3
            value: 3.604
          - type: mrr_at_5
            value: 4.3020000000000005
          - type: ndcg_at_1
            value: 2.252
          - type: ndcg_at_10
            value: 6.3020000000000005
          - type: ndcg_at_100
            value: 10.342
          - type: ndcg_at_1000
            value: 13.475999999999999
          - type: ndcg_at_3
            value: 4.0649999999999995
          - type: ndcg_at_5
            value: 5.344
          - type: precision_at_1
            value: 2.252
          - type: precision_at_10
            value: 1.171
          - type: precision_at_100
            value: 0.333
          - type: precision_at_1000
            value: 0.059000000000000004
          - type: precision_at_3
            value: 1.802
          - type: precision_at_5
            value: 1.712
          - type: recall_at_1
            value: 2.252
          - type: recall_at_10
            value: 11.712
          - type: recall_at_100
            value: 33.333
          - type: recall_at_1000
            value: 59.458999999999996
          - type: recall_at_3
            value: 5.405
          - type: recall_at_5
            value: 8.559
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB HALClusteringS2S
          revision: e06ebbbb123f8144bef1a5d18796f3dec9ae2915
          split: test
          type: lyon-nlp/clustering-hal-s2s
        metrics:
          - type: v_measure
            value: 28.301882091023288
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB MLSUMClusteringP2P
          revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
          split: test
          type: mlsum
        metrics:
          - type: v_measure
            value: 45.26992995191701
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB MLSUMClusteringS2S
          revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
          split: test
          type: mlsum
        metrics:
          - type: v_measure
            value: 42.773174876871145
        task:
          type: Clustering
      - dataset:
          config: fr
          name: MTEB MTOPDomainClassification (fr)
          revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
          split: test
          type: mteb/mtop_domain
        metrics:
          - type: accuracy
            value: 93.47635452552458
          - type: f1
            value: 93.19922617577213
        task:
          type: Classification
      - dataset:
          config: fr
          name: MTEB MTOPIntentClassification (fr)
          revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
          split: test
          type: mteb/mtop_intent
        metrics:
          - type: accuracy
            value: 80.2317569683683
          - type: f1
            value: 56.18060418621901
        task:
          type: Classification
      - dataset:
          config: fra
          name: MTEB MasakhaNEWSClassification (fra)
          revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
          split: test
          type: masakhane/masakhanews
        metrics:
          - type: accuracy
            value: 85.18957345971565
          - type: f1
            value: 80.829981537394
        task:
          type: Classification
      - dataset:
          config: fra
          name: MTEB MasakhaNEWSClusteringP2P (fra)
          revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
          split: test
          type: masakhane/masakhanews
        metrics:
          - type: v_measure
            value: 71.04138999801822
        task:
          type: Clustering
      - dataset:
          config: fra
          name: MTEB MasakhaNEWSClusteringS2S (fra)
          revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
          split: test
          type: masakhane/masakhanews
        metrics:
          - type: v_measure
            value: 71.7056263158008
        task:
          type: Clustering
      - dataset:
          config: fr
          name: MTEB MassiveIntentClassification (fr)
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
          split: test
          type: mteb/amazon_massive_intent
        metrics:
          - type: accuracy
            value: 76.65097511768661
          - type: f1
            value: 73.82441070598712
        task:
          type: Classification
      - dataset:
          config: fr
          name: MTEB MassiveScenarioClassification (fr)
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
          split: test
          type: mteb/amazon_massive_scenario
        metrics:
          - type: accuracy
            value: 79.09885675857431
          - type: f1
            value: 78.28407777434224
        task:
          type: Classification
      - dataset:
          config: fr
          name: MTEB MintakaRetrieval (fr)
          revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e
          split: test
          type: jinaai/mintakaqa
        metrics:
          - type: map_at_1
            value: 25.307000000000002
          - type: map_at_10
            value: 36.723
          - type: map_at_100
            value: 37.713
          - type: map_at_1000
            value: 37.769000000000005
          - type: map_at_3
            value: 33.77
          - type: map_at_5
            value: 35.463
          - type: mrr_at_1
            value: 25.307000000000002
          - type: mrr_at_10
            value: 36.723
          - type: mrr_at_100
            value: 37.713
          - type: mrr_at_1000
            value: 37.769000000000005
          - type: mrr_at_3
            value: 33.77
          - type: mrr_at_5
            value: 35.463
          - type: ndcg_at_1
            value: 25.307000000000002
          - type: ndcg_at_10
            value: 42.559999999999995
          - type: ndcg_at_100
            value: 47.457
          - type: ndcg_at_1000
            value: 49.162
          - type: ndcg_at_3
            value: 36.461
          - type: ndcg_at_5
            value: 39.504
          - type: precision_at_1
            value: 25.307000000000002
          - type: precision_at_10
            value: 6.106
          - type: precision_at_100
            value: 0.8420000000000001
          - type: precision_at_1000
            value: 0.098
          - type: precision_at_3
            value: 14.741999999999999
          - type: precision_at_5
            value: 10.319
          - type: recall_at_1
            value: 25.307000000000002
          - type: recall_at_10
            value: 61.056999999999995
          - type: recall_at_100
            value: 84.152
          - type: recall_at_1000
            value: 98.03399999999999
          - type: recall_at_3
            value: 44.226
          - type: recall_at_5
            value: 51.597
        task:
          type: Retrieval
      - dataset:
          config: fr
          name: MTEB OpusparcusPC (fr)
          revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
          split: test
          type: GEM/opusparcus
        metrics:
          - type: cos_sim_accuracy
            value: 99.90069513406156
          - type: cos_sim_ap
            value: 100
          - type: cos_sim_f1
            value: 99.95032290114257
          - type: cos_sim_precision
            value: 100
          - type: cos_sim_recall
            value: 99.90069513406156
          - type: dot_accuracy
            value: 99.90069513406156
          - type: dot_ap
            value: 100
          - type: dot_f1
            value: 99.95032290114257
          - type: dot_precision
            value: 100
          - type: dot_recall
            value: 99.90069513406156
          - type: euclidean_accuracy
            value: 99.90069513406156
          - type: euclidean_ap
            value: 100
          - type: euclidean_f1
            value: 99.95032290114257
          - type: euclidean_precision
            value: 100
          - type: euclidean_recall
            value: 99.90069513406156
          - type: manhattan_accuracy
            value: 99.90069513406156
          - type: manhattan_ap
            value: 100
          - type: manhattan_f1
            value: 99.95032290114257
          - type: manhattan_precision
            value: 100
          - type: manhattan_recall
            value: 99.90069513406156
          - type: max_accuracy
            value: 99.90069513406156
          - type: max_ap
            value: 100
          - type: max_f1
            value: 99.95032290114257
        task:
          type: PairClassification
      - dataset:
          config: fr
          name: MTEB PawsX (fr)
          revision: 8a04d940a42cd40658986fdd8e3da561533a3646
          split: test
          type: paws-x
        metrics:
          - type: cos_sim_accuracy
            value: 70.8
          - type: cos_sim_ap
            value: 73.7671529695957
          - type: cos_sim_f1
            value: 68.80964339527875
          - type: cos_sim_precision
            value: 62.95955882352941
          - type: cos_sim_recall
            value: 75.85825027685493
          - type: dot_accuracy
            value: 70.8
          - type: dot_ap
            value: 73.78345265366947
          - type: dot_f1
            value: 68.80964339527875
          - type: dot_precision
            value: 62.95955882352941
          - type: dot_recall
            value: 75.85825027685493
          - type: euclidean_accuracy
            value: 70.8
          - type: euclidean_ap
            value: 73.7671529695957
          - type: euclidean_f1
            value: 68.80964339527875
          - type: euclidean_precision
            value: 62.95955882352941
          - type: euclidean_recall
            value: 75.85825027685493
          - type: manhattan_accuracy
            value: 70.75
          - type: manhattan_ap
            value: 73.78996383615953
          - type: manhattan_f1
            value: 68.79432624113475
          - type: manhattan_precision
            value: 63.39869281045751
          - type: manhattan_recall
            value: 75.1937984496124
          - type: max_accuracy
            value: 70.8
          - type: max_ap
            value: 73.78996383615953
          - type: max_f1
            value: 68.80964339527875
        task:
          type: PairClassification
      - dataset:
          config: default
          name: MTEB SICKFr
          revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a
          split: test
          type: Lajavaness/SICK-fr
        metrics:
          - type: cos_sim_pearson
            value: 84.03253762760392
          - type: cos_sim_spearman
            value: 79.68280105762004
          - type: euclidean_pearson
            value: 80.98265050044444
          - type: euclidean_spearman
            value: 79.68233242682867
          - type: manhattan_pearson
            value: 80.9678911810704
          - type: manhattan_spearman
            value: 79.70264097683109
        task:
          type: STS
      - dataset:
          config: fr
          name: MTEB STS22 (fr)
          revision: eea2b4fe26a775864c896887d910b76a8098ad3f
          split: test
          type: mteb/sts22-crosslingual-sts
        metrics:
          - type: cos_sim_pearson
            value: 80.56896987572884
          - type: cos_sim_spearman
            value: 81.84352499523287
          - type: euclidean_pearson
            value: 80.40831759421305
          - type: euclidean_spearman
            value: 81.84352499523287
          - type: manhattan_pearson
            value: 80.74333857561238
          - type: manhattan_spearman
            value: 82.41503246733892
        task:
          type: STS
      - dataset:
          config: fr
          name: MTEB STSBenchmarkMultilingualSTS (fr)
          revision: 93d57ef91790589e3ce9c365164337a8a78b7632
          split: test
          type: stsb_multi_mt
        metrics:
          - type: cos_sim_pearson
            value: 82.71826762276979
          - type: cos_sim_spearman
            value: 82.25433354916042
          - type: euclidean_pearson
            value: 81.87115571724316
          - type: euclidean_spearman
            value: 82.25322342890107
          - type: manhattan_pearson
            value: 82.11174867527224
          - type: manhattan_spearman
            value: 82.55905365203084
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB SummEvalFr
          revision: b385812de6a9577b6f4d0f88c6a6e35395a94054
          split: test
          type: lyon-nlp/summarization-summeval-fr-p2p
        metrics:
          - type: cos_sim_pearson
            value: 30.659441623392887
          - type: cos_sim_spearman
            value: 30.501134097353315
          - type: dot_pearson
            value: 30.659444768851056
          - type: dot_spearman
            value: 30.501134097353315
        task:
          type: Summarization
      - dataset:
          config: default
          name: MTEB SyntecReranking
          revision: b205c5084a0934ce8af14338bf03feb19499c84d
          split: test
          type: lyon-nlp/mteb-fr-reranking-syntec-s2p
        metrics:
          - type: map
            value: 94.03333333333333
          - type: mrr
            value: 94.03333333333333
        task:
          type: Reranking
      - dataset:
          config: default
          name: MTEB SyntecRetrieval
          revision: 77f7e271bf4a92b24fce5119f3486b583ca016ff
          split: test
          type: lyon-nlp/mteb-fr-retrieval-syntec-s2p
        metrics:
          - type: map_at_1
            value: 79
          - type: map_at_10
            value: 87.61
          - type: map_at_100
            value: 87.655
          - type: map_at_1000
            value: 87.655
          - type: map_at_3
            value: 87.167
          - type: map_at_5
            value: 87.36699999999999
          - type: mrr_at_1
            value: 79
          - type: mrr_at_10
            value: 87.61
          - type: mrr_at_100
            value: 87.655
          - type: mrr_at_1000
            value: 87.655
          - type: mrr_at_3
            value: 87.167
          - type: mrr_at_5
            value: 87.36699999999999
          - type: ndcg_at_1
            value: 79
          - type: ndcg_at_10
            value: 90.473
          - type: ndcg_at_100
            value: 90.694
          - type: ndcg_at_1000
            value: 90.694
          - type: ndcg_at_3
            value: 89.464
          - type: ndcg_at_5
            value: 89.851
          - type: precision_at_1
            value: 79
          - type: precision_at_10
            value: 9.9
          - type: precision_at_100
            value: 1
          - type: precision_at_1000
            value: 0.1
          - type: precision_at_3
            value: 32
          - type: precision_at_5
            value: 19.400000000000002
          - type: recall_at_1
            value: 79
          - type: recall_at_10
            value: 99
          - type: recall_at_100
            value: 100
          - type: recall_at_1000
            value: 100
          - type: recall_at_3
            value: 96
          - type: recall_at_5
            value: 97
        task:
          type: Retrieval
      - dataset:
          config: fr
          name: MTEB XPQARetrieval (fr)
          revision: c99d599f0a6ab9b85b065da6f9d94f9cf731679f
          split: test
          type: jinaai/xpqa
        metrics:
          - type: map_at_1
            value: 39.395
          - type: map_at_10
            value: 59.123999999999995
          - type: map_at_100
            value: 60.704
          - type: map_at_1000
            value: 60.760000000000005
          - type: map_at_3
            value: 53.187
          - type: map_at_5
            value: 56.863
          - type: mrr_at_1
            value: 62.083
          - type: mrr_at_10
            value: 68.87299999999999
          - type: mrr_at_100
            value: 69.46900000000001
          - type: mrr_at_1000
            value: 69.48299999999999
          - type: mrr_at_3
            value: 66.8
          - type: mrr_at_5
            value: 67.928
          - type: ndcg_at_1
            value: 62.083
          - type: ndcg_at_10
            value: 65.583
          - type: ndcg_at_100
            value: 70.918
          - type: ndcg_at_1000
            value: 71.72800000000001
          - type: ndcg_at_3
            value: 60.428000000000004
          - type: ndcg_at_5
            value: 61.853
          - type: precision_at_1
            value: 62.083
          - type: precision_at_10
            value: 15.033
          - type: precision_at_100
            value: 1.9529999999999998
          - type: precision_at_1000
            value: 0.207
          - type: precision_at_3
            value: 36.315
          - type: precision_at_5
            value: 25.955000000000002
          - type: recall_at_1
            value: 39.395
          - type: recall_at_10
            value: 74.332
          - type: recall_at_100
            value: 94.729
          - type: recall_at_1000
            value: 99.75500000000001
          - type: recall_at_3
            value: 57.679
          - type: recall_at_5
            value: 65.036
        task:
          type: Retrieval

gte-Qwen2-1.5B-instruct

gte-Qwen2-1.5B-instruct is the latest model in the gte (General Text Embedding) model family. The model is built on Qwen2-1.5B LLM model and use the same training data and strategies as the gte-Qwen2-7B-instruct model.

The model incorporates several key advancements:

• Integration of bidirectional attention mechanisms, enriching its contextual understanding.
• Instruction tuning, applied solely on the query side for streamlined efficiency
• Comprehensive training across a vast, multilingual text corpus spanning diverse domains and scenarios. This training leverages both weakly supervised and supervised data, ensuring the model's applicability across numerous languages and a wide array of downstream tasks.

Model Information

• Model Size: 1.5B
• Embedding Dimension: 1536
• Max Input Tokens: 32k

Requirements

transformers>=4.39.2
flash_attn>=2.5.6

Usage

Sentence Transformers

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("Alibaba-NLP/gte-Qwen2-1.5B-instruct", trust_remote_code=True)
# In case you want to reduce the maximum length:
model.max_seq_length = 8192

queries = [
    "how much protein should a female eat",
    "summit define",
]
documents = [
    "As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
    "Definition of summit for English Language Learners. : 1  the highest point of a mountain : the top of a mountain. : 2  the highest level. : 3  a meeting or series of meetings between the leaders of two or more governments.",
]

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

scores = (query_embeddings @ document_embeddings.T) * 100
print(scores.tolist())

Observe the config_sentence_transformers.json to see all pre-built prompt names. Otherwise, you can use model.encode(queries, prompt="Instruct: ...\nQuery: " to use a custom prompt of your choice.

Transformers

import torch
import torch.nn.functional as F

from torch import Tensor
from transformers import AutoTokenizer, AutoModel


def last_token_pool(last_hidden_states: Tensor,
                 attention_mask: Tensor) -> Tensor:
    left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
    if left_padding:
        return last_hidden_states[:, -1]
    else:
        sequence_lengths = attention_mask.sum(dim=1) - 1
        batch_size = last_hidden_states.shape[0]
        return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]


def get_detailed_instruct(task_description: str, query: str) -> str:
    return f'Instruct: {task_description}\nQuery: {query}'


# Each query must come with a one-sentence instruction that describes the task
task = 'Given a web search query, retrieve relevant passages that answer the query'
queries = [
    get_detailed_instruct(task, 'how much protein should a female eat'),
    get_detailed_instruct(task, 'summit define')
]
# No need to add instruction for retrieval documents
documents = [
    "As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.",
    "Definition of summit for English Language Learners. : 1  the highest point of a mountain : the top of a mountain. : 2  the highest level. : 3  a meeting or series of meetings between the leaders of two or more governments."
]
input_texts = queries + documents

tokenizer = AutoTokenizer.from_pretrained('Alibaba-NLP/gte-Qwen2-1.5B-instruct', trust_remote_code=True)
model = AutoModel.from_pretrained('Alibaba-NLP/gte-Qwen2-1.5B-instruct', trust_remote_code=True)

max_length = 8192

# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=max_length, padding=True, truncation=True, return_tensors='pt')
outputs = model(**batch_dict)
embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask'])

# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T) * 100
print(scores.tolist())

Evaluation

MTEB & C-MTEB

You can use the scripts/eval_mteb.py to reproduce the following result of gte-Qwen2-1.5B-instruct on MTEB(English)/C-MTEB(Chinese):

Model Name	MTEB(56)	C-MTEB(35)	MTEB-fr(26)	MTEB-pl(26)
bge-base-en-1.5	64.23	-	-	-
bge-large-en-1.5	63.55	-	-	-
gte-large-en-v1.5	65.39	-	-	-
gte-base-en-v1.5	64.11	-	-	-
mxbai-embed-large-v1	64.68	-	-	-
acge_text_embedding	-	69.07	-	-
stella-mrl-large-zh-v3.5-1792d	-	68.55	-	-
gte-large-zh	-	66.72	-	-
multilingual-e5-base	59.45	56.21	-	-
multilingual-e5-large	61.50	58.81	-	-
e5-mistral-7b-instruct	66.63	60.81	-	-
gte-Qwen1.5-7B-instruct	67.34	69.52	-	-
NV-Embed-v1	69.32	-	-	-
gte-Qwen2-7B-instruct	70.24	72.05	68.25	67.86
gte-Qwen2-1.5B-instruct	67.16	67.65	66.60	64.04

GTE Models

The gte series models have consistently released two types of models: encoder-only models (based on the BERT architecture) and decode-only models (based on the LLM architecture).

Models	Language	Max Sequence Length	Dimension	Model Size (Memory Usage, fp32)
GTE-large-zh	Chinese	512	1024	1.25GB
GTE-base-zh	Chinese	512	512	0.41GB
GTE-small-zh	Chinese	512	512	0.12GB
GTE-large	English	512	1024	1.25GB
GTE-base	English	512	512	0.21GB
GTE-small	English	512	384	0.10GB
GTE-large-en-v1.5	English	8192	1024	1.74GB
GTE-base-en-v1.5	English	8192	768	0.51GB
GTE-Qwen1.5-7B-instruct	Multilingual	32000	4096	26.45GB
GTE-Qwen2-7B-instruct	Multilingual	32000	3584	26.45GB
GTE-Qwen2-1.5B-instruct	Multilingual	32000	1536	6.62GB

Cloud API Services

In addition to the open-source GTE series models, GTE series models are also available as commercial API services on Alibaba Cloud.

• Embedding Models: Rhree versions of the text embedding models are available: text-embedding-v1/v2/v3, with v3 being the latest API service.
• ReRank Models: The gte-rerank model service is available.

Note that the models behind the commercial APIs are not entirely identical to the open-source models.

Citation

If you find our paper or models helpful, please consider cite:

@article{li2023towards,
  title={Towards general text embeddings with multi-stage contrastive learning},
  author={Li, Zehan and Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Pengjun and Zhang, Meishan},
  journal={arXiv preprint arXiv:2308.03281},
  year={2023}
}