README.md

metadata

language:
  - en
library_name: sentence-transformers
license: mit
pipeline_tag: sentence-similarity
tags:
  - feature-extraction
  - mteb
  - sentence-similarity
  - sentence-transformers
model-index:
  - name: GIST-Embedding-v0
    results:
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_counterfactual
          name: MTEB AmazonCounterfactualClassification (en)
          config: en
          split: test
          revision: e8379541af4e31359cca9fbcf4b00f2671dba205
        metrics:
          - type: accuracy
            value: 75.95522388059702
          - type: ap
            value: 38.940434354439276
          - type: f1
            value: 69.88686275888114
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_polarity
          name: MTEB AmazonPolarityClassification
          config: default
          split: test
          revision: e2d317d38cd51312af73b3d32a06d1a08b442046
        metrics:
          - type: accuracy
            value: 93.51357499999999
          - type: ap
            value: 90.30414241486682
          - type: f1
            value: 93.50552829047328
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_reviews_multi
          name: MTEB AmazonReviewsClassification (en)
          config: en
          split: test
          revision: 1399c76144fd37290681b995c656ef9b2e06e26d
        metrics:
          - type: accuracy
            value: 50.446000000000005
          - type: f1
            value: 49.76432659699279
      - task:
          type: Retrieval
        dataset:
          type: arguana
          name: MTEB ArguAna
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 38.265
          - type: map_at_10
            value: 54.236
          - type: map_at_100
            value: 54.81399999999999
          - type: map_at_1000
            value: 54.81700000000001
          - type: map_at_3
            value: 49.881
          - type: map_at_5
            value: 52.431000000000004
          - type: mrr_at_1
            value: 38.265
          - type: mrr_at_10
            value: 54.152
          - type: mrr_at_100
            value: 54.730000000000004
          - type: mrr_at_1000
            value: 54.733
          - type: mrr_at_3
            value: 49.644
          - type: mrr_at_5
            value: 52.32599999999999
          - type: ndcg_at_1
            value: 38.265
          - type: ndcg_at_10
            value: 62.62
          - type: ndcg_at_100
            value: 64.96600000000001
          - type: ndcg_at_1000
            value: 65.035
          - type: ndcg_at_3
            value: 53.691
          - type: ndcg_at_5
            value: 58.303000000000004
          - type: precision_at_1
            value: 38.265
          - type: precision_at_10
            value: 8.919
          - type: precision_at_100
            value: 0.991
          - type: precision_at_1000
            value: 0.1
          - type: precision_at_3
            value: 21.573999999999998
          - type: precision_at_5
            value: 15.192
          - type: recall_at_1
            value: 38.265
          - type: recall_at_10
            value: 89.189
          - type: recall_at_100
            value: 99.14699999999999
          - type: recall_at_1000
            value: 99.644
          - type: recall_at_3
            value: 64.723
          - type: recall_at_5
            value: 75.96000000000001
      - task:
          type: Clustering
        dataset:
          type: mteb/arxiv-clustering-p2p
          name: MTEB ArxivClusteringP2P
          config: default
          split: test
          revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
        metrics:
          - type: v_measure
            value: 48.287087887491744
      - task:
          type: Clustering
        dataset:
          type: mteb/arxiv-clustering-s2s
          name: MTEB ArxivClusteringS2S
          config: default
          split: test
          revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
        metrics:
          - type: v_measure
            value: 42.74244928943812
      - task:
          type: Reranking
        dataset:
          type: mteb/askubuntudupquestions-reranking
          name: MTEB AskUbuntuDupQuestions
          config: default
          split: test
          revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
        metrics:
          - type: map
            value: 62.68814324295771
          - type: mrr
            value: 75.46266983247591
      - task:
          type: STS
        dataset:
          type: mteb/biosses-sts
          name: MTEB BIOSSES
          config: default
          split: test
          revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
        metrics:
          - type: cos_sim_pearson
            value: 90.45240209600391
          - type: cos_sim_spearman
            value: 87.95079919934645
          - type: euclidean_pearson
            value: 88.93438602492702
          - type: euclidean_spearman
            value: 88.28152962682988
          - type: manhattan_pearson
            value: 88.92193964325268
          - type: manhattan_spearman
            value: 88.21466063329498
      - task:
          type: BitextMining
        dataset:
          type: mteb/bucc-bitext-mining
          name: MTEB BUCC (de-en)
          config: de-en
          split: test
          revision: d51519689f32196a32af33b075a01d0e7c51e252
        metrics:
          - type: accuracy
            value: 15.605427974947808
          - type: f1
            value: 14.989877233698866
          - type: precision
            value: 14.77906814441261
          - type: recall
            value: 15.605427974947808
      - task:
          type: BitextMining
        dataset:
          type: mteb/bucc-bitext-mining
          name: MTEB BUCC (fr-en)
          config: fr-en
          split: test
          revision: d51519689f32196a32af33b075a01d0e7c51e252
        metrics:
          - type: accuracy
            value: 33.38102575390711
          - type: f1
            value: 32.41704114719127
          - type: precision
            value: 32.057363829835964
          - type: recall
            value: 33.38102575390711
      - task:
          type: BitextMining
        dataset:
          type: mteb/bucc-bitext-mining
          name: MTEB BUCC (ru-en)
          config: ru-en
          split: test
          revision: d51519689f32196a32af33b075a01d0e7c51e252
        metrics:
          - type: accuracy
            value: 0.1939729823346034
          - type: f1
            value: 0.17832215223820772
          - type: precision
            value: 0.17639155671715423
          - type: recall
            value: 0.1939729823346034
      - task:
          type: BitextMining
        dataset:
          type: mteb/bucc-bitext-mining
          name: MTEB BUCC (zh-en)
          config: zh-en
          split: test
          revision: d51519689f32196a32af33b075a01d0e7c51e252
        metrics:
          - type: accuracy
            value: 3.0542390731964195
          - type: f1
            value: 2.762857644374232
          - type: precision
            value: 2.6505178163945935
          - type: recall
            value: 3.0542390731964195
      - task:
          type: Classification
        dataset:
          type: mteb/banking77
          name: MTEB Banking77Classification
          config: default
          split: test
          revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
        metrics:
          - type: accuracy
            value: 87.29545454545453
          - type: f1
            value: 87.26415991342238
      - task:
          type: Clustering
        dataset:
          type: mteb/biorxiv-clustering-p2p
          name: MTEB BiorxivClusteringP2P
          config: default
          split: test
          revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
        metrics:
          - type: v_measure
            value: 39.035319537839484
      - task:
          type: Clustering
        dataset:
          type: mteb/biorxiv-clustering-s2s
          name: MTEB BiorxivClusteringS2S
          config: default
          split: test
          revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
        metrics:
          - type: v_measure
            value: 36.667313307057285
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackAndroidRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 33.979
          - type: map_at_10
            value: 46.275
          - type: map_at_100
            value: 47.975
          - type: map_at_1000
            value: 48.089
          - type: map_at_3
            value: 42.507
          - type: map_at_5
            value: 44.504
          - type: mrr_at_1
            value: 42.346000000000004
          - type: mrr_at_10
            value: 53.013
          - type: mrr_at_100
            value: 53.717000000000006
          - type: mrr_at_1000
            value: 53.749
          - type: mrr_at_3
            value: 50.405
          - type: mrr_at_5
            value: 51.915
          - type: ndcg_at_1
            value: 42.346000000000004
          - type: ndcg_at_10
            value: 53.179
          - type: ndcg_at_100
            value: 58.458
          - type: ndcg_at_1000
            value: 60.057
          - type: ndcg_at_3
            value: 48.076
          - type: ndcg_at_5
            value: 50.283
          - type: precision_at_1
            value: 42.346000000000004
          - type: precision_at_10
            value: 10.386
          - type: precision_at_100
            value: 1.635
          - type: precision_at_1000
            value: 0.20600000000000002
          - type: precision_at_3
            value: 23.413999999999998
          - type: precision_at_5
            value: 16.624
          - type: recall_at_1
            value: 33.979
          - type: recall_at_10
            value: 65.553
          - type: recall_at_100
            value: 87.18599999999999
          - type: recall_at_1000
            value: 97.25200000000001
          - type: recall_at_3
            value: 50.068999999999996
          - type: recall_at_5
            value: 56.882
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackEnglishRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 31.529
          - type: map_at_10
            value: 42.219
          - type: map_at_100
            value: 43.408
          - type: map_at_1000
            value: 43.544
          - type: map_at_3
            value: 39.178000000000004
          - type: map_at_5
            value: 40.87
          - type: mrr_at_1
            value: 39.873
          - type: mrr_at_10
            value: 48.25
          - type: mrr_at_100
            value: 48.867
          - type: mrr_at_1000
            value: 48.908
          - type: mrr_at_3
            value: 46.03
          - type: mrr_at_5
            value: 47.355000000000004
          - type: ndcg_at_1
            value: 39.873
          - type: ndcg_at_10
            value: 47.933
          - type: ndcg_at_100
            value: 52.156000000000006
          - type: ndcg_at_1000
            value: 54.238
          - type: ndcg_at_3
            value: 43.791999999999994
          - type: ndcg_at_5
            value: 45.678999999999995
          - type: precision_at_1
            value: 39.873
          - type: precision_at_10
            value: 9.032
          - type: precision_at_100
            value: 1.419
          - type: precision_at_1000
            value: 0.192
          - type: precision_at_3
            value: 21.231
          - type: precision_at_5
            value: 14.981
          - type: recall_at_1
            value: 31.529
          - type: recall_at_10
            value: 57.925000000000004
          - type: recall_at_100
            value: 75.89
          - type: recall_at_1000
            value: 89.007
          - type: recall_at_3
            value: 45.363
          - type: recall_at_5
            value: 50.973
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackGamingRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 41.289
          - type: map_at_10
            value: 54.494
          - type: map_at_100
            value: 55.494
          - type: map_at_1000
            value: 55.545
          - type: map_at_3
            value: 51.20099999999999
          - type: map_at_5
            value: 53.147
          - type: mrr_at_1
            value: 47.335
          - type: mrr_at_10
            value: 57.772
          - type: mrr_at_100
            value: 58.428000000000004
          - type: mrr_at_1000
            value: 58.453
          - type: mrr_at_3
            value: 55.434000000000005
          - type: mrr_at_5
            value: 56.8
          - type: ndcg_at_1
            value: 47.335
          - type: ndcg_at_10
            value: 60.382999999999996
          - type: ndcg_at_100
            value: 64.294
          - type: ndcg_at_1000
            value: 65.211
          - type: ndcg_at_3
            value: 55.098
          - type: ndcg_at_5
            value: 57.776
          - type: precision_at_1
            value: 47.335
          - type: precision_at_10
            value: 9.724
          - type: precision_at_100
            value: 1.26
          - type: precision_at_1000
            value: 0.13699999999999998
          - type: precision_at_3
            value: 24.786
          - type: precision_at_5
            value: 16.977999999999998
          - type: recall_at_1
            value: 41.289
          - type: recall_at_10
            value: 74.36399999999999
          - type: recall_at_100
            value: 91.19800000000001
          - type: recall_at_1000
            value: 97.508
          - type: recall_at_3
            value: 60.285
          - type: recall_at_5
            value: 66.814
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackGisRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 28.816999999999997
          - type: map_at_10
            value: 37.856
          - type: map_at_100
            value: 38.824
          - type: map_at_1000
            value: 38.902
          - type: map_at_3
            value: 34.982
          - type: map_at_5
            value: 36.831
          - type: mrr_at_1
            value: 31.073
          - type: mrr_at_10
            value: 39.985
          - type: mrr_at_100
            value: 40.802
          - type: mrr_at_1000
            value: 40.861999999999995
          - type: mrr_at_3
            value: 37.419999999999995
          - type: mrr_at_5
            value: 39.104
          - type: ndcg_at_1
            value: 31.073
          - type: ndcg_at_10
            value: 42.958
          - type: ndcg_at_100
            value: 47.671
          - type: ndcg_at_1000
            value: 49.633
          - type: ndcg_at_3
            value: 37.602000000000004
          - type: ndcg_at_5
            value: 40.688
          - type: precision_at_1
            value: 31.073
          - type: precision_at_10
            value: 6.531000000000001
          - type: precision_at_100
            value: 0.932
          - type: precision_at_1000
            value: 0.11399999999999999
          - type: precision_at_3
            value: 15.857
          - type: precision_at_5
            value: 11.209
          - type: recall_at_1
            value: 28.816999999999997
          - type: recall_at_10
            value: 56.538999999999994
          - type: recall_at_100
            value: 78.17699999999999
          - type: recall_at_1000
            value: 92.92200000000001
          - type: recall_at_3
            value: 42.294
          - type: recall_at_5
            value: 49.842999999999996
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackMathematicaRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 18.397
          - type: map_at_10
            value: 27.256999999999998
          - type: map_at_100
            value: 28.541
          - type: map_at_1000
            value: 28.658
          - type: map_at_3
            value: 24.565
          - type: map_at_5
            value: 26.211000000000002
          - type: mrr_at_1
            value: 22.761
          - type: mrr_at_10
            value: 32.248
          - type: mrr_at_100
            value: 33.171
          - type: mrr_at_1000
            value: 33.227000000000004
          - type: mrr_at_3
            value: 29.498
          - type: mrr_at_5
            value: 31.246000000000002
          - type: ndcg_at_1
            value: 22.761
          - type: ndcg_at_10
            value: 32.879999999999995
          - type: ndcg_at_100
            value: 38.913
          - type: ndcg_at_1000
            value: 41.504999999999995
          - type: ndcg_at_3
            value: 27.988000000000003
          - type: ndcg_at_5
            value: 30.548
          - type: precision_at_1
            value: 22.761
          - type: precision_at_10
            value: 6.045
          - type: precision_at_100
            value: 1.044
          - type: precision_at_1000
            value: 0.13999999999999999
          - type: precision_at_3
            value: 13.433
          - type: precision_at_5
            value: 9.925
          - type: recall_at_1
            value: 18.397
          - type: recall_at_10
            value: 45.14
          - type: recall_at_100
            value: 71.758
          - type: recall_at_1000
            value: 89.854
          - type: recall_at_3
            value: 31.942999999999998
          - type: recall_at_5
            value: 38.249
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackPhysicsRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 30.604
          - type: map_at_10
            value: 42.132
          - type: map_at_100
            value: 43.419000000000004
          - type: map_at_1000
            value: 43.527
          - type: map_at_3
            value: 38.614
          - type: map_at_5
            value: 40.705000000000005
          - type: mrr_at_1
            value: 37.824999999999996
          - type: mrr_at_10
            value: 47.696
          - type: mrr_at_100
            value: 48.483
          - type: mrr_at_1000
            value: 48.53
          - type: mrr_at_3
            value: 45.123999999999995
          - type: mrr_at_5
            value: 46.635
          - type: ndcg_at_1
            value: 37.824999999999996
          - type: ndcg_at_10
            value: 48.421
          - type: ndcg_at_100
            value: 53.568000000000005
          - type: ndcg_at_1000
            value: 55.574999999999996
          - type: ndcg_at_3
            value: 42.89
          - type: ndcg_at_5
            value: 45.683
          - type: precision_at_1
            value: 37.824999999999996
          - type: precision_at_10
            value: 8.758000000000001
          - type: precision_at_100
            value: 1.319
          - type: precision_at_1000
            value: 0.168
          - type: precision_at_3
            value: 20.244
          - type: precision_at_5
            value: 14.533
          - type: recall_at_1
            value: 30.604
          - type: recall_at_10
            value: 61.605
          - type: recall_at_100
            value: 82.787
          - type: recall_at_1000
            value: 95.78
          - type: recall_at_3
            value: 46.303
          - type: recall_at_5
            value: 53.351000000000006
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackProgrammersRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 26.262999999999998
          - type: map_at_10
            value: 36.858999999999995
          - type: map_at_100
            value: 38.241
          - type: map_at_1000
            value: 38.346999999999994
          - type: map_at_3
            value: 33.171
          - type: map_at_5
            value: 35.371
          - type: mrr_at_1
            value: 32.42
          - type: mrr_at_10
            value: 42.361
          - type: mrr_at_100
            value: 43.219
          - type: mrr_at_1000
            value: 43.271
          - type: mrr_at_3
            value: 39.593
          - type: mrr_at_5
            value: 41.248000000000005
          - type: ndcg_at_1
            value: 32.42
          - type: ndcg_at_10
            value: 43.081
          - type: ndcg_at_100
            value: 48.837
          - type: ndcg_at_1000
            value: 50.954
          - type: ndcg_at_3
            value: 37.413000000000004
          - type: ndcg_at_5
            value: 40.239000000000004
          - type: precision_at_1
            value: 32.42
          - type: precision_at_10
            value: 8.071
          - type: precision_at_100
            value: 1.272
          - type: precision_at_1000
            value: 0.163
          - type: precision_at_3
            value: 17.922
          - type: precision_at_5
            value: 13.311
          - type: recall_at_1
            value: 26.262999999999998
          - type: recall_at_10
            value: 56.062999999999995
          - type: recall_at_100
            value: 80.636
          - type: recall_at_1000
            value: 94.707
          - type: recall_at_3
            value: 40.425
          - type: recall_at_5
            value: 47.663
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 27.86616666666667
          - type: map_at_10
            value: 37.584999999999994
          - type: map_at_100
            value: 38.80291666666667
          - type: map_at_1000
            value: 38.91358333333333
          - type: map_at_3
            value: 34.498
          - type: map_at_5
            value: 36.269999999999996
          - type: mrr_at_1
            value: 33.07566666666667
          - type: mrr_at_10
            value: 41.92366666666666
          - type: mrr_at_100
            value: 42.73516666666667
          - type: mrr_at_1000
            value: 42.785666666666664
          - type: mrr_at_3
            value: 39.39075
          - type: mrr_at_5
            value: 40.89133333333334
          - type: ndcg_at_1
            value: 33.07566666666667
          - type: ndcg_at_10
            value: 43.19875
          - type: ndcg_at_100
            value: 48.32083333333334
          - type: ndcg_at_1000
            value: 50.418000000000006
          - type: ndcg_at_3
            value: 38.10308333333333
          - type: ndcg_at_5
            value: 40.5985
          - type: precision_at_1
            value: 33.07566666666667
          - type: precision_at_10
            value: 7.581916666666666
          - type: precision_at_100
            value: 1.1975
          - type: precision_at_1000
            value: 0.15699999999999997
          - type: precision_at_3
            value: 17.49075
          - type: precision_at_5
            value: 12.5135
          - type: recall_at_1
            value: 27.86616666666667
          - type: recall_at_10
            value: 55.449749999999995
          - type: recall_at_100
            value: 77.92516666666666
          - type: recall_at_1000
            value: 92.31358333333333
          - type: recall_at_3
            value: 41.324416666666664
          - type: recall_at_5
            value: 47.72533333333333
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackStatsRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 26.648
          - type: map_at_10
            value: 33.155
          - type: map_at_100
            value: 34.149
          - type: map_at_1000
            value: 34.239000000000004
          - type: map_at_3
            value: 30.959999999999997
          - type: map_at_5
            value: 32.172
          - type: mrr_at_1
            value: 30.061
          - type: mrr_at_10
            value: 36.229
          - type: mrr_at_100
            value: 37.088
          - type: mrr_at_1000
            value: 37.15
          - type: mrr_at_3
            value: 34.254
          - type: mrr_at_5
            value: 35.297
          - type: ndcg_at_1
            value: 30.061
          - type: ndcg_at_10
            value: 37.247
          - type: ndcg_at_100
            value: 42.093
          - type: ndcg_at_1000
            value: 44.45
          - type: ndcg_at_3
            value: 33.211
          - type: ndcg_at_5
            value: 35.083999999999996
          - type: precision_at_1
            value: 30.061
          - type: precision_at_10
            value: 5.7059999999999995
          - type: precision_at_100
            value: 0.8880000000000001
          - type: precision_at_1000
            value: 0.116
          - type: precision_at_3
            value: 13.957
          - type: precision_at_5
            value: 9.663
          - type: recall_at_1
            value: 26.648
          - type: recall_at_10
            value: 46.85
          - type: recall_at_100
            value: 68.87
          - type: recall_at_1000
            value: 86.508
          - type: recall_at_3
            value: 35.756
          - type: recall_at_5
            value: 40.376
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackTexRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 19.058
          - type: map_at_10
            value: 26.722
          - type: map_at_100
            value: 27.863
          - type: map_at_1000
            value: 27.988000000000003
          - type: map_at_3
            value: 24.258
          - type: map_at_5
            value: 25.531
          - type: mrr_at_1
            value: 23.09
          - type: mrr_at_10
            value: 30.711
          - type: mrr_at_100
            value: 31.628
          - type: mrr_at_1000
            value: 31.702
          - type: mrr_at_3
            value: 28.418
          - type: mrr_at_5
            value: 29.685
          - type: ndcg_at_1
            value: 23.09
          - type: ndcg_at_10
            value: 31.643
          - type: ndcg_at_100
            value: 37.047999999999995
          - type: ndcg_at_1000
            value: 39.896
          - type: ndcg_at_3
            value: 27.189999999999998
          - type: ndcg_at_5
            value: 29.112
          - type: precision_at_1
            value: 23.09
          - type: precision_at_10
            value: 5.743
          - type: precision_at_100
            value: 1
          - type: precision_at_1000
            value: 0.14300000000000002
          - type: precision_at_3
            value: 12.790000000000001
          - type: precision_at_5
            value: 9.195
          - type: recall_at_1
            value: 19.058
          - type: recall_at_10
            value: 42.527
          - type: recall_at_100
            value: 66.833
          - type: recall_at_1000
            value: 87.008
          - type: recall_at_3
            value: 29.876
          - type: recall_at_5
            value: 34.922
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackUnixRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 28.066999999999997
          - type: map_at_10
            value: 37.543
          - type: map_at_100
            value: 38.725
          - type: map_at_1000
            value: 38.815
          - type: map_at_3
            value: 34.488
          - type: map_at_5
            value: 36.222
          - type: mrr_at_1
            value: 33.116
          - type: mrr_at_10
            value: 41.743
          - type: mrr_at_100
            value: 42.628
          - type: mrr_at_1000
            value: 42.675999999999995
          - type: mrr_at_3
            value: 39.241
          - type: mrr_at_5
            value: 40.622
          - type: ndcg_at_1
            value: 33.116
          - type: ndcg_at_10
            value: 43.089
          - type: ndcg_at_100
            value: 48.61
          - type: ndcg_at_1000
            value: 50.585
          - type: ndcg_at_3
            value: 37.816
          - type: ndcg_at_5
            value: 40.256
          - type: precision_at_1
            value: 33.116
          - type: precision_at_10
            value: 7.313
          - type: precision_at_100
            value: 1.1320000000000001
          - type: precision_at_1000
            value: 0.14200000000000002
          - type: precision_at_3
            value: 17.102
          - type: precision_at_5
            value: 12.09
          - type: recall_at_1
            value: 28.066999999999997
          - type: recall_at_10
            value: 55.684
          - type: recall_at_100
            value: 80.092
          - type: recall_at_1000
            value: 93.605
          - type: recall_at_3
            value: 41.277
          - type: recall_at_5
            value: 47.46
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackWebmastersRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 27.094
          - type: map_at_10
            value: 35.939
          - type: map_at_100
            value: 37.552
          - type: map_at_1000
            value: 37.771
          - type: map_at_3
            value: 32.414
          - type: map_at_5
            value: 34.505
          - type: mrr_at_1
            value: 32.609
          - type: mrr_at_10
            value: 40.521
          - type: mrr_at_100
            value: 41.479
          - type: mrr_at_1000
            value: 41.524
          - type: mrr_at_3
            value: 37.451
          - type: mrr_at_5
            value: 39.387
          - type: ndcg_at_1
            value: 32.609
          - type: ndcg_at_10
            value: 41.83
          - type: ndcg_at_100
            value: 47.763
          - type: ndcg_at_1000
            value: 50.102999999999994
          - type: ndcg_at_3
            value: 36.14
          - type: ndcg_at_5
            value: 39.153999999999996
          - type: precision_at_1
            value: 32.609
          - type: precision_at_10
            value: 7.925
          - type: precision_at_100
            value: 1.591
          - type: precision_at_1000
            value: 0.246
          - type: precision_at_3
            value: 16.337
          - type: precision_at_5
            value: 12.411
          - type: recall_at_1
            value: 27.094
          - type: recall_at_10
            value: 53.32900000000001
          - type: recall_at_100
            value: 79.52
          - type: recall_at_1000
            value: 93.958
          - type: recall_at_3
            value: 37.773
          - type: recall_at_5
            value: 45.321
      - task:
          type: Retrieval
        dataset:
          type: BeIR/cqadupstack
          name: MTEB CQADupstackWordpressRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 22.649
          - type: map_at_10
            value: 30.569000000000003
          - type: map_at_100
            value: 31.444
          - type: map_at_1000
            value: 31.538
          - type: map_at_3
            value: 27.638
          - type: map_at_5
            value: 29.171000000000003
          - type: mrr_at_1
            value: 24.399
          - type: mrr_at_10
            value: 32.555
          - type: mrr_at_100
            value: 33.312000000000005
          - type: mrr_at_1000
            value: 33.376
          - type: mrr_at_3
            value: 29.820999999999998
          - type: mrr_at_5
            value: 31.402
          - type: ndcg_at_1
            value: 24.399
          - type: ndcg_at_10
            value: 35.741
          - type: ndcg_at_100
            value: 40.439
          - type: ndcg_at_1000
            value: 42.809000000000005
          - type: ndcg_at_3
            value: 30.020999999999997
          - type: ndcg_at_5
            value: 32.68
          - type: precision_at_1
            value: 24.399
          - type: precision_at_10
            value: 5.749
          - type: precision_at_100
            value: 0.878
          - type: precision_at_1000
            value: 0.117
          - type: precision_at_3
            value: 12.815999999999999
          - type: precision_at_5
            value: 9.242
          - type: recall_at_1
            value: 22.649
          - type: recall_at_10
            value: 49.818
          - type: recall_at_100
            value: 72.155
          - type: recall_at_1000
            value: 89.654
          - type: recall_at_3
            value: 34.528999999999996
          - type: recall_at_5
            value: 40.849999999999994
      - task:
          type: Retrieval
        dataset:
          type: climate-fever
          name: MTEB ClimateFEVER
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 13.587
          - type: map_at_10
            value: 23.021
          - type: map_at_100
            value: 25.095
          - type: map_at_1000
            value: 25.295
          - type: map_at_3
            value: 19.463
          - type: map_at_5
            value: 21.389
          - type: mrr_at_1
            value: 29.576999999999998
          - type: mrr_at_10
            value: 41.44
          - type: mrr_at_100
            value: 42.497
          - type: mrr_at_1000
            value: 42.529
          - type: mrr_at_3
            value: 38.284
          - type: mrr_at_5
            value: 40.249
          - type: ndcg_at_1
            value: 29.576999999999998
          - type: ndcg_at_10
            value: 31.491000000000003
          - type: ndcg_at_100
            value: 39.352
          - type: ndcg_at_1000
            value: 42.703
          - type: ndcg_at_3
            value: 26.284999999999997
          - type: ndcg_at_5
            value: 28.218
          - type: precision_at_1
            value: 29.576999999999998
          - type: precision_at_10
            value: 9.713
          - type: precision_at_100
            value: 1.8079999999999998
          - type: precision_at_1000
            value: 0.243
          - type: precision_at_3
            value: 19.608999999999998
          - type: precision_at_5
            value: 14.957999999999998
          - type: recall_at_1
            value: 13.587
          - type: recall_at_10
            value: 37.001
          - type: recall_at_100
            value: 63.617999999999995
          - type: recall_at_1000
            value: 82.207
          - type: recall_at_3
            value: 24.273
          - type: recall_at_5
            value: 29.813000000000002
      - task:
          type: Retrieval
        dataset:
          type: dbpedia-entity
          name: MTEB DBPedia
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 9.98
          - type: map_at_10
            value: 20.447000000000003
          - type: map_at_100
            value: 29.032999999999998
          - type: map_at_1000
            value: 30.8
          - type: map_at_3
            value: 15.126999999999999
          - type: map_at_5
            value: 17.327
          - type: mrr_at_1
            value: 71.25
          - type: mrr_at_10
            value: 78.014
          - type: mrr_at_100
            value: 78.303
          - type: mrr_at_1000
            value: 78.309
          - type: mrr_at_3
            value: 76.375
          - type: mrr_at_5
            value: 77.58699999999999
          - type: ndcg_at_1
            value: 57.99999999999999
          - type: ndcg_at_10
            value: 41.705
          - type: ndcg_at_100
            value: 47.466
          - type: ndcg_at_1000
            value: 55.186
          - type: ndcg_at_3
            value: 47.089999999999996
          - type: ndcg_at_5
            value: 43.974000000000004
          - type: precision_at_1
            value: 71.25
          - type: precision_at_10
            value: 32.65
          - type: precision_at_100
            value: 10.89
          - type: precision_at_1000
            value: 2.197
          - type: precision_at_3
            value: 50.5
          - type: precision_at_5
            value: 42.199999999999996
          - type: recall_at_1
            value: 9.98
          - type: recall_at_10
            value: 25.144
          - type: recall_at_100
            value: 53.754999999999995
          - type: recall_at_1000
            value: 78.56400000000001
          - type: recall_at_3
            value: 15.964
          - type: recall_at_5
            value: 19.186
      - task:
          type: Classification
        dataset:
          type: mteb/emotion
          name: MTEB EmotionClassification
          config: default
          split: test
          revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
        metrics:
          - type: accuracy
            value: 54.67999999999999
          - type: f1
            value: 49.48247525503583
      - task:
          type: Retrieval
        dataset:
          type: fever
          name: MTEB FEVER
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 74.798
          - type: map_at_10
            value: 82.933
          - type: map_at_100
            value: 83.157
          - type: map_at_1000
            value: 83.173
          - type: map_at_3
            value: 81.80199999999999
          - type: map_at_5
            value: 82.55
          - type: mrr_at_1
            value: 80.573
          - type: mrr_at_10
            value: 87.615
          - type: mrr_at_100
            value: 87.69
          - type: mrr_at_1000
            value: 87.69200000000001
          - type: mrr_at_3
            value: 86.86399999999999
          - type: mrr_at_5
            value: 87.386
          - type: ndcg_at_1
            value: 80.573
          - type: ndcg_at_10
            value: 86.64500000000001
          - type: ndcg_at_100
            value: 87.407
          - type: ndcg_at_1000
            value: 87.68299999999999
          - type: ndcg_at_3
            value: 84.879
          - type: ndcg_at_5
            value: 85.921
          - type: precision_at_1
            value: 80.573
          - type: precision_at_10
            value: 10.348
          - type: precision_at_100
            value: 1.093
          - type: precision_at_1000
            value: 0.11399999999999999
          - type: precision_at_3
            value: 32.268
          - type: precision_at_5
            value: 20.084
          - type: recall_at_1
            value: 74.798
          - type: recall_at_10
            value: 93.45400000000001
          - type: recall_at_100
            value: 96.42500000000001
          - type: recall_at_1000
            value: 98.158
          - type: recall_at_3
            value: 88.634
          - type: recall_at_5
            value: 91.295
      - task:
          type: Retrieval
        dataset:
          type: fiqa
          name: MTEB FiQA2018
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 20.567
          - type: map_at_10
            value: 32.967999999999996
          - type: map_at_100
            value: 35.108
          - type: map_at_1000
            value: 35.272999999999996
          - type: map_at_3
            value: 28.701999999999998
          - type: map_at_5
            value: 31.114000000000004
          - type: mrr_at_1
            value: 40.432
          - type: mrr_at_10
            value: 48.956
          - type: mrr_at_100
            value: 49.832
          - type: mrr_at_1000
            value: 49.87
          - type: mrr_at_3
            value: 46.759
          - type: mrr_at_5
            value: 47.886
          - type: ndcg_at_1
            value: 40.432
          - type: ndcg_at_10
            value: 40.644000000000005
          - type: ndcg_at_100
            value: 48.252
          - type: ndcg_at_1000
            value: 51.099000000000004
          - type: ndcg_at_3
            value: 36.992000000000004
          - type: ndcg_at_5
            value: 38.077
          - type: precision_at_1
            value: 40.432
          - type: precision_at_10
            value: 11.296000000000001
          - type: precision_at_100
            value: 1.9009999999999998
          - type: precision_at_1000
            value: 0.241
          - type: precision_at_3
            value: 24.537
          - type: precision_at_5
            value: 17.963
          - type: recall_at_1
            value: 20.567
          - type: recall_at_10
            value: 47.052
          - type: recall_at_100
            value: 75.21600000000001
          - type: recall_at_1000
            value: 92.285
          - type: recall_at_3
            value: 33.488
          - type: recall_at_5
            value: 39.334
      - task:
          type: Retrieval
        dataset:
          type: hotpotqa
          name: MTEB HotpotQA
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 38.196999999999996
          - type: map_at_10
            value: 60.697
          - type: map_at_100
            value: 61.624
          - type: map_at_1000
            value: 61.692
          - type: map_at_3
            value: 57.421
          - type: map_at_5
            value: 59.455000000000005
          - type: mrr_at_1
            value: 76.39399999999999
          - type: mrr_at_10
            value: 82.504
          - type: mrr_at_100
            value: 82.71300000000001
          - type: mrr_at_1000
            value: 82.721
          - type: mrr_at_3
            value: 81.494
          - type: mrr_at_5
            value: 82.137
          - type: ndcg_at_1
            value: 76.39399999999999
          - type: ndcg_at_10
            value: 68.92200000000001
          - type: ndcg_at_100
            value: 72.13199999999999
          - type: ndcg_at_1000
            value: 73.392
          - type: ndcg_at_3
            value: 64.226
          - type: ndcg_at_5
            value: 66.815
          - type: precision_at_1
            value: 76.39399999999999
          - type: precision_at_10
            value: 14.442
          - type: precision_at_100
            value: 1.694
          - type: precision_at_1000
            value: 0.186
          - type: precision_at_3
            value: 41.211
          - type: precision_at_5
            value: 26.766000000000002
          - type: recall_at_1
            value: 38.196999999999996
          - type: recall_at_10
            value: 72.208
          - type: recall_at_100
            value: 84.71300000000001
          - type: recall_at_1000
            value: 92.971
          - type: recall_at_3
            value: 61.816
          - type: recall_at_5
            value: 66.914
      - task:
          type: Classification
        dataset:
          type: mteb/imdb
          name: MTEB ImdbClassification
          config: default
          split: test
          revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
        metrics:
          - type: accuracy
            value: 89.6556
          - type: ap
            value: 85.27600392682054
          - type: f1
            value: 89.63353655386406
      - task:
          type: Retrieval
        dataset:
          type: msmarco
          name: MTEB MSMARCO
          config: default
          split: dev
          revision: None
        metrics:
          - type: map_at_1
            value: 21.482
          - type: map_at_10
            value: 33.701
          - type: map_at_100
            value: 34.861
          - type: map_at_1000
            value: 34.914
          - type: map_at_3
            value: 29.793999999999997
          - type: map_at_5
            value: 32.072
          - type: mrr_at_1
            value: 22.163
          - type: mrr_at_10
            value: 34.371
          - type: mrr_at_100
            value: 35.471000000000004
          - type: mrr_at_1000
            value: 35.518
          - type: mrr_at_3
            value: 30.554
          - type: mrr_at_5
            value: 32.799
          - type: ndcg_at_1
            value: 22.163
          - type: ndcg_at_10
            value: 40.643
          - type: ndcg_at_100
            value: 46.239999999999995
          - type: ndcg_at_1000
            value: 47.526
          - type: ndcg_at_3
            value: 32.714999999999996
          - type: ndcg_at_5
            value: 36.791000000000004
          - type: precision_at_1
            value: 22.163
          - type: precision_at_10
            value: 6.4799999999999995
          - type: precision_at_100
            value: 0.928
          - type: precision_at_1000
            value: 0.104
          - type: precision_at_3
            value: 14.002
          - type: precision_at_5
            value: 10.453
          - type: recall_at_1
            value: 21.482
          - type: recall_at_10
            value: 61.953
          - type: recall_at_100
            value: 87.86500000000001
          - type: recall_at_1000
            value: 97.636
          - type: recall_at_3
            value: 40.441
          - type: recall_at_5
            value: 50.27
      - task:
          type: Classification
        dataset:
          type: mteb/mtop_domain
          name: MTEB MTOPDomainClassification (en)
          config: en
          split: test
          revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
        metrics:
          - type: accuracy
            value: 95.3032375740994
          - type: f1
            value: 95.01515022686607
      - task:
          type: Classification
        dataset:
          type: mteb/mtop_intent
          name: MTEB MTOPIntentClassification (en)
          config: en
          split: test
          revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
        metrics:
          - type: accuracy
            value: 78.10077519379846
          - type: f1
            value: 58.240739725625644
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_intent
          name: MTEB MassiveIntentClassification (en)
          config: en
          split: test
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
        metrics:
          - type: accuracy
            value: 76.0053799596503
          - type: f1
            value: 74.11733965804146
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_scenario
          name: MTEB MassiveScenarioClassification (en)
          config: en
          split: test
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
        metrics:
          - type: accuracy
            value: 79.64021519838602
          - type: f1
            value: 79.8513960091438
      - task:
          type: Clustering
        dataset:
          type: mteb/medrxiv-clustering-p2p
          name: MTEB MedrxivClusteringP2P
          config: default
          split: test
          revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
        metrics:
          - type: v_measure
            value: 33.92425767945184
      - task:
          type: Clustering
        dataset:
          type: mteb/medrxiv-clustering-s2s
          name: MTEB MedrxivClusteringS2S
          config: default
          split: test
          revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
        metrics:
          - type: v_measure
            value: 32.249612382060754
      - task:
          type: Reranking
        dataset:
          type: mteb/mind_small
          name: MTEB MindSmallReranking
          config: default
          split: test
          revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
        metrics:
          - type: map
            value: 32.35584955492918
          - type: mrr
            value: 33.545865224584674
      - task:
          type: Retrieval
        dataset:
          type: nfcorpus
          name: MTEB NFCorpus
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 6.978
          - type: map_at_10
            value: 14.749
          - type: map_at_100
            value: 19.192
          - type: map_at_1000
            value: 20.815
          - type: map_at_3
            value: 10.927000000000001
          - type: map_at_5
            value: 12.726
          - type: mrr_at_1
            value: 49.536
          - type: mrr_at_10
            value: 57.806999999999995
          - type: mrr_at_100
            value: 58.373
          - type: mrr_at_1000
            value: 58.407
          - type: mrr_at_3
            value: 55.779
          - type: mrr_at_5
            value: 57.095
          - type: ndcg_at_1
            value: 46.749
          - type: ndcg_at_10
            value: 37.644
          - type: ndcg_at_100
            value: 35.559000000000005
          - type: ndcg_at_1000
            value: 44.375
          - type: ndcg_at_3
            value: 43.354
          - type: ndcg_at_5
            value: 41.022999999999996
          - type: precision_at_1
            value: 48.607
          - type: precision_at_10
            value: 28.08
          - type: precision_at_100
            value: 9.155000000000001
          - type: precision_at_1000
            value: 2.2270000000000003
          - type: precision_at_3
            value: 40.764
          - type: precision_at_5
            value: 35.728
          - type: recall_at_1
            value: 6.978
          - type: recall_at_10
            value: 17.828
          - type: recall_at_100
            value: 36.010999999999996
          - type: recall_at_1000
            value: 68.34700000000001
          - type: recall_at_3
            value: 11.645999999999999
          - type: recall_at_5
            value: 14.427000000000001
      - task:
          type: Retrieval
        dataset:
          type: nq
          name: MTEB NQ
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 30.219
          - type: map_at_10
            value: 45.633
          - type: map_at_100
            value: 46.752
          - type: map_at_1000
            value: 46.778999999999996
          - type: map_at_3
            value: 41.392
          - type: map_at_5
            value: 43.778
          - type: mrr_at_1
            value: 34.327999999999996
          - type: mrr_at_10
            value: 48.256
          - type: mrr_at_100
            value: 49.076
          - type: mrr_at_1000
            value: 49.092999999999996
          - type: mrr_at_3
            value: 44.786
          - type: mrr_at_5
            value: 46.766000000000005
          - type: ndcg_at_1
            value: 34.299
          - type: ndcg_at_10
            value: 53.434000000000005
          - type: ndcg_at_100
            value: 58.03
          - type: ndcg_at_1000
            value: 58.633
          - type: ndcg_at_3
            value: 45.433
          - type: ndcg_at_5
            value: 49.379
          - type: precision_at_1
            value: 34.299
          - type: precision_at_10
            value: 8.911
          - type: precision_at_100
            value: 1.145
          - type: precision_at_1000
            value: 0.12
          - type: precision_at_3
            value: 20.896
          - type: precision_at_5
            value: 14.832
          - type: recall_at_1
            value: 30.219
          - type: recall_at_10
            value: 74.59400000000001
          - type: recall_at_100
            value: 94.392
          - type: recall_at_1000
            value: 98.832
          - type: recall_at_3
            value: 53.754000000000005
          - type: recall_at_5
            value: 62.833000000000006
      - task:
          type: Retrieval
        dataset:
          type: quora
          name: MTEB QuoraRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 71.139
          - type: map_at_10
            value: 85.141
          - type: map_at_100
            value: 85.78099999999999
          - type: map_at_1000
            value: 85.795
          - type: map_at_3
            value: 82.139
          - type: map_at_5
            value: 84.075
          - type: mrr_at_1
            value: 81.98
          - type: mrr_at_10
            value: 88.056
          - type: mrr_at_100
            value: 88.152
          - type: mrr_at_1000
            value: 88.152
          - type: mrr_at_3
            value: 87.117
          - type: mrr_at_5
            value: 87.78099999999999
          - type: ndcg_at_1
            value: 82.02000000000001
          - type: ndcg_at_10
            value: 88.807
          - type: ndcg_at_100
            value: 89.99000000000001
          - type: ndcg_at_1000
            value: 90.068
          - type: ndcg_at_3
            value: 85.989
          - type: ndcg_at_5
            value: 87.627
          - type: precision_at_1
            value: 82.02000000000001
          - type: precision_at_10
            value: 13.472999999999999
          - type: precision_at_100
            value: 1.534
          - type: precision_at_1000
            value: 0.157
          - type: precision_at_3
            value: 37.553
          - type: precision_at_5
            value: 24.788
          - type: recall_at_1
            value: 71.139
          - type: recall_at_10
            value: 95.707
          - type: recall_at_100
            value: 99.666
          - type: recall_at_1000
            value: 99.983
          - type: recall_at_3
            value: 87.64699999999999
          - type: recall_at_5
            value: 92.221
      - task:
          type: Clustering
        dataset:
          type: mteb/reddit-clustering
          name: MTEB RedditClustering
          config: default
          split: test
          revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
        metrics:
          - type: v_measure
            value: 59.11035509193503
      - task:
          type: Clustering
        dataset:
          type: mteb/reddit-clustering-p2p
          name: MTEB RedditClusteringP2P
          config: default
          split: test
          revision: 282350215ef01743dc01b456c7f5241fa8937f16
        metrics:
          - type: v_measure
            value: 62.44241881422526
      - task:
          type: Retrieval
        dataset:
          type: scidocs
          name: MTEB SCIDOCS
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 5.122999999999999
          - type: map_at_10
            value: 14.45
          - type: map_at_100
            value: 17.108999999999998
          - type: map_at_1000
            value: 17.517
          - type: map_at_3
            value: 10.213999999999999
          - type: map_at_5
            value: 12.278
          - type: mrr_at_1
            value: 25.3
          - type: mrr_at_10
            value: 37.791999999999994
          - type: mrr_at_100
            value: 39.086
          - type: mrr_at_1000
            value: 39.121
          - type: mrr_at_3
            value: 34.666999999999994
          - type: mrr_at_5
            value: 36.472
          - type: ndcg_at_1
            value: 25.3
          - type: ndcg_at_10
            value: 23.469
          - type: ndcg_at_100
            value: 33.324
          - type: ndcg_at_1000
            value: 39.357
          - type: ndcg_at_3
            value: 22.478
          - type: ndcg_at_5
            value: 19.539
          - type: precision_at_1
            value: 25.3
          - type: precision_at_10
            value: 12.3
          - type: precision_at_100
            value: 2.654
          - type: precision_at_1000
            value: 0.40800000000000003
          - type: precision_at_3
            value: 21.667
          - type: precision_at_5
            value: 17.5
          - type: recall_at_1
            value: 5.122999999999999
          - type: recall_at_10
            value: 24.937
          - type: recall_at_100
            value: 53.833
          - type: recall_at_1000
            value: 82.85
          - type: recall_at_3
            value: 13.178
          - type: recall_at_5
            value: 17.747
      - task:
          type: STS
        dataset:
          type: mteb/sickr-sts
          name: MTEB SICK-R
          config: default
          split: test
          revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
        metrics:
          - type: cos_sim_pearson
            value: 86.76549431206278
          - type: cos_sim_spearman
            value: 81.28563534883214
          - type: euclidean_pearson
            value: 84.17180713818567
          - type: euclidean_spearman
            value: 81.1684082302606
          - type: manhattan_pearson
            value: 84.12189753972959
          - type: manhattan_spearman
            value: 81.1134998997958
      - task:
          type: STS
        dataset:
          type: mteb/sts12-sts
          name: MTEB STS12
          config: default
          split: test
          revision: a0d554a64d88156834ff5ae9920b964011b16384
        metrics:
          - type: cos_sim_pearson
            value: 85.75137587182017
          - type: cos_sim_spearman
            value: 76.155337187325
          - type: euclidean_pearson
            value: 83.54551546726665
          - type: euclidean_spearman
            value: 76.30324990565346
          - type: manhattan_pearson
            value: 83.52192617483797
          - type: manhattan_spearman
            value: 76.30017227216015
      - task:
          type: STS
        dataset:
          type: mteb/sts13-sts
          name: MTEB STS13
          config: default
          split: test
          revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
        metrics:
          - type: cos_sim_pearson
            value: 87.13890050398628
          - type: cos_sim_spearman
            value: 87.84898360302155
          - type: euclidean_pearson
            value: 86.89491809082031
          - type: euclidean_spearman
            value: 87.99935689905651
          - type: manhattan_pearson
            value: 86.86526424376366
          - type: manhattan_spearman
            value: 87.96850732980495
      - task:
          type: STS
        dataset:
          type: mteb/sts14-sts
          name: MTEB STS14
          config: default
          split: test
          revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
        metrics:
          - type: cos_sim_pearson
            value: 86.01978753231558
          - type: cos_sim_spearman
            value: 83.38989083933329
          - type: euclidean_pearson
            value: 85.28405032045376
          - type: euclidean_spearman
            value: 83.51703914276501
          - type: manhattan_pearson
            value: 85.25775133078966
          - type: manhattan_spearman
            value: 83.52815667821727
      - task:
          type: STS
        dataset:
          type: mteb/sts15-sts
          name: MTEB STS15
          config: default
          split: test
          revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
        metrics:
          - type: cos_sim_pearson
            value: 88.28482294437876
          - type: cos_sim_spearman
            value: 89.42976214499576
          - type: euclidean_pearson
            value: 88.72677957272468
          - type: euclidean_spearman
            value: 89.30001736116229
          - type: manhattan_pearson
            value: 88.64119331622562
          - type: manhattan_spearman
            value: 89.21771022634893
      - task:
          type: STS
        dataset:
          type: mteb/sts16-sts
          name: MTEB STS16
          config: default
          split: test
          revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
        metrics:
          - type: cos_sim_pearson
            value: 83.79810159351987
          - type: cos_sim_spearman
            value: 85.34918402034273
          - type: euclidean_pearson
            value: 84.76058606229002
          - type: euclidean_spearman
            value: 85.45159829941214
          - type: manhattan_pearson
            value: 84.73926491888156
          - type: manhattan_spearman
            value: 85.42568221985898
      - task:
          type: STS
        dataset:
          type: mteb/sts17-crosslingual-sts
          name: MTEB STS17 (en-en)
          config: en-en
          split: test
          revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
        metrics:
          - type: cos_sim_pearson
            value: 88.92796712570272
          - type: cos_sim_spearman
            value: 88.58925922945812
          - type: euclidean_pearson
            value: 88.97231215531797
          - type: euclidean_spearman
            value: 88.27036385068719
          - type: manhattan_pearson
            value: 88.95761469412228
          - type: manhattan_spearman
            value: 88.23980432487681
      - task:
          type: STS
        dataset:
          type: mteb/sts22-crosslingual-sts
          name: MTEB STS22 (en)
          config: en
          split: test
          revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
        metrics:
          - type: cos_sim_pearson
            value: 66.85679810182282
          - type: cos_sim_spearman
            value: 67.80696709003128
          - type: euclidean_pearson
            value: 68.77524185947989
          - type: euclidean_spearman
            value: 68.032438075422
          - type: manhattan_pearson
            value: 68.60489100404182
          - type: manhattan_spearman
            value: 67.75418889226138
      - task:
          type: STS
        dataset:
          type: mteb/stsbenchmark-sts
          name: MTEB STSBenchmark
          config: default
          split: test
          revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
        metrics:
          - type: cos_sim_pearson
            value: 86.33287880999367
          - type: cos_sim_spearman
            value: 87.32401087204754
          - type: euclidean_pearson
            value: 87.27961069148029
          - type: euclidean_spearman
            value: 87.3547683085868
          - type: manhattan_pearson
            value: 87.24405442789622
          - type: manhattan_spearman
            value: 87.32896271166672
      - task:
          type: Reranking
        dataset:
          type: mteb/scidocs-reranking
          name: MTEB SciDocsRR
          config: default
          split: test
          revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
        metrics:
          - type: map
            value: 87.71553665286558
          - type: mrr
            value: 96.42436176749902
      - task:
          type: Retrieval
        dataset:
          type: scifact
          name: MTEB SciFact
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 61.094
          - type: map_at_10
            value: 71.066
          - type: map_at_100
            value: 71.608
          - type: map_at_1000
            value: 71.629
          - type: map_at_3
            value: 68.356
          - type: map_at_5
            value: 70.15
          - type: mrr_at_1
            value: 64
          - type: mrr_at_10
            value: 71.82300000000001
          - type: mrr_at_100
            value: 72.251
          - type: mrr_at_1000
            value: 72.269
          - type: mrr_at_3
            value: 69.833
          - type: mrr_at_5
            value: 71.11699999999999
          - type: ndcg_at_1
            value: 64
          - type: ndcg_at_10
            value: 75.286
          - type: ndcg_at_100
            value: 77.40700000000001
          - type: ndcg_at_1000
            value: 77.806
          - type: ndcg_at_3
            value: 70.903
          - type: ndcg_at_5
            value: 73.36399999999999
          - type: precision_at_1
            value: 64
          - type: precision_at_10
            value: 9.9
          - type: precision_at_100
            value: 1.093
          - type: precision_at_1000
            value: 0.11199999999999999
          - type: precision_at_3
            value: 27.667
          - type: precision_at_5
            value: 18.333
          - type: recall_at_1
            value: 61.094
          - type: recall_at_10
            value: 87.256
          - type: recall_at_100
            value: 96.5
          - type: recall_at_1000
            value: 99.333
          - type: recall_at_3
            value: 75.6
          - type: recall_at_5
            value: 81.789
      - task:
          type: PairClassification
        dataset:
          type: mteb/sprintduplicatequestions-pairclassification
          name: MTEB SprintDuplicateQuestions
          config: default
          split: test
          revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
        metrics:
          - type: cos_sim_accuracy
            value: 99.82871287128712
          - type: cos_sim_ap
            value: 95.9325677692287
          - type: cos_sim_f1
            value: 91.13924050632912
          - type: cos_sim_precision
            value: 92.3076923076923
          - type: cos_sim_recall
            value: 90
          - type: dot_accuracy
            value: 99.7980198019802
          - type: dot_ap
            value: 94.56107207796
          - type: dot_f1
            value: 89.41908713692946
          - type: dot_precision
            value: 92.88793103448276
          - type: dot_recall
            value: 86.2
          - type: euclidean_accuracy
            value: 99.82871287128712
          - type: euclidean_ap
            value: 95.94390332507025
          - type: euclidean_f1
            value: 91.17797042325346
          - type: euclidean_precision
            value: 93.02809573361083
          - type: euclidean_recall
            value: 89.4
          - type: manhattan_accuracy
            value: 99.82871287128712
          - type: manhattan_ap
            value: 95.97587114452257
          - type: manhattan_f1
            value: 91.25821121778675
          - type: manhattan_precision
            value: 92.23697650663942
          - type: manhattan_recall
            value: 90.3
          - type: max_accuracy
            value: 99.82871287128712
          - type: max_ap
            value: 95.97587114452257
          - type: max_f1
            value: 91.25821121778675
      - task:
          type: Clustering
        dataset:
          type: mteb/stackexchange-clustering
          name: MTEB StackExchangeClustering
          config: default
          split: test
          revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
        metrics:
          - type: v_measure
            value: 66.13974351708839
      - task:
          type: Clustering
        dataset:
          type: mteb/stackexchange-clustering-p2p
          name: MTEB StackExchangeClusteringP2P
          config: default
          split: test
          revision: 815ca46b2622cec33ccafc3735d572c266efdb44
        metrics:
          - type: v_measure
            value: 35.594544722932234
      - task:
          type: Reranking
        dataset:
          type: mteb/stackoverflowdupquestions-reranking
          name: MTEB StackOverflowDupQuestions
          config: default
          split: test
          revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
        metrics:
          - type: map
            value: 54.718738983377726
          - type: mrr
            value: 55.61655154486037
      - task:
          type: Summarization
        dataset:
          type: mteb/summeval
          name: MTEB SummEval
          config: default
          split: test
          revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
        metrics:
          - type: cos_sim_pearson
            value: 30.37028359646597
          - type: cos_sim_spearman
            value: 30.866534307244443
          - type: dot_pearson
            value: 29.89037691541816
          - type: dot_spearman
            value: 29.941267567971718
      - task:
          type: Retrieval
        dataset:
          type: trec-covid
          name: MTEB TRECCOVID
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 0.20400000000000001
          - type: map_at_10
            value: 1.7340000000000002
          - type: map_at_100
            value: 9.966
          - type: map_at_1000
            value: 25.119000000000003
          - type: map_at_3
            value: 0.596
          - type: map_at_5
            value: 0.941
          - type: mrr_at_1
            value: 76
          - type: mrr_at_10
            value: 85.85199999999999
          - type: mrr_at_100
            value: 85.85199999999999
          - type: mrr_at_1000
            value: 85.85199999999999
          - type: mrr_at_3
            value: 84.667
          - type: mrr_at_5
            value: 85.56700000000001
          - type: ndcg_at_1
            value: 71
          - type: ndcg_at_10
            value: 69.60300000000001
          - type: ndcg_at_100
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          - type: ndcg_at_1000
            value: 51.085
          - type: ndcg_at_3
            value: 71.95
          - type: ndcg_at_5
            value: 71.17599999999999
          - type: precision_at_1
            value: 76
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            value: 74.2
          - type: precision_at_100
            value: 55.96
          - type: precision_at_1000
            value: 22.584
          - type: precision_at_3
            value: 77.333
          - type: precision_at_5
            value: 75.6
          - type: recall_at_1
            value: 0.20400000000000001
          - type: recall_at_10
            value: 1.992
          - type: recall_at_100
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          - type: recall_at_1000
            value: 48.732
          - type: recall_at_3
            value: 0.635
          - type: recall_at_5
            value: 1.034
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          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (sqi-eng)
          config: sqi-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 8
          - type: f1
            value: 6.298401229470593
          - type: precision
            value: 5.916991709050532
          - type: recall
            value: 8
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        dataset:
          type: mteb/tatoeba-bitext-mining
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          config: fry-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 17.341040462427745
          - type: f1
            value: 14.621650026274303
          - type: precision
            value: 13.9250609139035
          - type: recall
            value: 17.341040462427745
      - task:
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        dataset:
          type: mteb/tatoeba-bitext-mining
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          config: kur-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 8.536585365853659
          - type: f1
            value: 6.30972482801751
          - type: precision
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          - type: recall
            value: 8.536585365853659
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        dataset:
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          config: tur-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 6.4
          - type: f1
            value: 4.221126743626743
          - type: precision
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          - type: recall
            value: 6.4
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          type: BitextMining
        dataset:
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          config: deu-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 19.8
          - type: f1
            value: 18.13768093781855
          - type: precision
            value: 17.54646004378763
          - type: recall
            value: 19.8
      - task:
          type: BitextMining
        dataset:
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          config: nld-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 13.700000000000001
          - type: f1
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          - type: precision
            value: 11.934237966189185
          - type: recall
            value: 13.700000000000001
      - task:
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        dataset:
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          config: ron-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 14.299999999999999
          - type: f1
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          - type: precision
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          - type: recall
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        dataset:
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          config: ang-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 22.388059701492537
          - type: f1
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          - type: precision
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          - type: recall
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      - task:
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        dataset:
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          config: ido-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 22
          - type: f1
            value: 17.4576947358322
          - type: precision
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          - type: recall
            value: 22
      - task:
          type: BitextMining
        dataset:
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          config: jav-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 8.292682926829269
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          - type: recall
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      - task:
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        dataset:
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          config: isl-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 5.4
          - type: f1
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          - type: recall
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        dataset:
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          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
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          - type: accuracy
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          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
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          config: kaz-eng
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          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
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        dataset:
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          config: est-eng
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          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 5.1
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          - type: recall
            value: 5.1
      - task:
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        dataset:
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          config: heb-eng
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          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
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        dataset:
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          config: gla-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
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        dataset:
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          config: mar-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 0
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            value: 0
      - task:
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        dataset:
          type: mteb/tatoeba-bitext-mining
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          config: lat-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
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      - task:
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        dataset:
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          config: bel-eng
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          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
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        dataset:
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          config: pms-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
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          config: gle-eng
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        dataset:
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          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
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        dataset:
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          config: bul-eng
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          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
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          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
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          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
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          config: rus-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
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          config: spa-eng
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        metrics:
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            value: 21
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      - task:
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        dataset:
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          config: hye-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
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        dataset:
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          config: tel-eng
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          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
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      - task:
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          config: afr-eng
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          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
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            value: 10.7
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          - type: recall
            value: 10.7
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        dataset:
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          name: MTEB Tatoeba (mon-eng)
          config: mon-eng
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          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
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        dataset:
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          config: arz-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
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      - task:
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        dataset:
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          config: hrv-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 7.1
          - type: f1
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          - type: recall
            value: 7.1
      - task:
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        dataset:
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          name: MTEB Tatoeba (nov-eng)
          config: nov-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 36.57587548638132
          - type: f1
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          - type: precision
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          - type: recall
            value: 36.57587548638132
      - task:
          type: BitextMining
        dataset:
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          config: gsw-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
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          - type: precision
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          - type: recall
            value: 16.23931623931624
      - task:
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        dataset:
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          config: nds-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 16.3
          - type: f1
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          - type: recall
            value: 16.3
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        dataset:
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          config: ukr-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 1
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            value: 1
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        dataset:
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          config: uzb-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
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            value: 3.9719626168224296
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          - type: recall
            value: 3.9719626168224296
      - task:
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        dataset:
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          name: MTEB Tatoeba (lit-eng)
          config: lit-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 3.4000000000000004
          - type: f1
            value: 2.4847456001574653
          - type: precision
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          - type: recall
            value: 3.4000000000000004
      - task:
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        dataset:
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          name: MTEB Tatoeba (ina-eng)
          config: ina-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 36.9
          - type: f1
            value: 31.390407955063697
          - type: precision
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          - type: recall
            value: 36.9
      - task:
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        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (lfn-eng)
          config: lfn-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 14.2
          - type: f1
            value: 12.551591810861895
          - type: precision
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          - type: recall
            value: 14.2
      - task:
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        dataset:
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          config: zsm-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 9.2
          - type: f1
            value: 7.5561895648211435
          - type: precision
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          - type: recall
            value: 9.2
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          type: BitextMining
        dataset:
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          config: ita-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 21.2
          - type: f1
            value: 18.498268429117875
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          - type: recall
            value: 21.2
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          type: BitextMining
        dataset:
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          config: cmn-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 4.2
          - type: f1
            value: 2.886572782530936
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          - type: recall
            value: 4.2
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        dataset:
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          config: lvs-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 6.800000000000001
          - type: f1
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          - type: recall
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        dataset:
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          config: glg-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 22.1
          - type: f1
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          - type: precision
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          - type: recall
            value: 22.1
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          type: BitextMining
        dataset:
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          config: ceb-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 6
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          - type: precision
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          - type: recall
            value: 6
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          type: BitextMining
        dataset:
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          config: bre-eng
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          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 5.5
          - type: f1
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          - type: precision
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          - type: recall
            value: 5.5
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          type: BitextMining
        dataset:
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          config: ben-eng
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          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 0.2
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            value: 0.2
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        dataset:
          type: mteb/tatoeba-bitext-mining
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          config: swg-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 12.5
          - type: f1
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          - type: recall
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        dataset:
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          config: arq-eng
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          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 0.43907793633369924
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          - type: recall
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          config: kab-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
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        dataset:
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          config: fra-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 24.6
          - type: f1
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          - type: recall
            value: 24.6
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          type: BitextMining
        dataset:
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          config: por-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 23.400000000000002
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          - type: recall
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          config: tat-eng
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          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 1.3
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          - type: recall
            value: 1.3
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        dataset:
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          config: oci-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 14.399999999999999
          - type: f1
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          - type: precision
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          - type: recall
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      - task:
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          config: pol-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 8.7
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          - type: recall
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        dataset:
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          config: war-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 7.1
          - type: f1
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          - type: recall
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      - task:
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        dataset:
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          config: aze-eng
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          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
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            value: 4.3999999999999995
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      - task:
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        dataset:
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          config: vie-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 7.1
          - type: f1
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          - type: recall
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      - task:
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        dataset:
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          config: nno-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 8.5
          - type: f1
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          - type: recall
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        dataset:
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          config: cha-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 17.51824817518248
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          - type: recall
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          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 0.3
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        dataset:
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          config: dan-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 11.3
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          - type: recall
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        dataset:
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          config: ell-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 0.7000000000000001
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          - type: recall
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        dataset:
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          config: amh-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 1.7857142857142856
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          - type: recall
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        dataset:
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          config: pam-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
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          - type: recall
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        dataset:
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          config: hsb-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
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            value: 5.383022774327122
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          - type: recall
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        dataset:
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          config: srp-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 3.9
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          - type: recall
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        dataset:
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          config: epo-eng
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          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 13.8
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          - type: recall
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        dataset:
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          config: kzj-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
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            value: 4.9
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        dataset:
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          config: awa-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 2.1645021645021645
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          - type: recall
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        dataset:
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          config: fao-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
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            value: 9.541984732824428
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      - task:
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        dataset:
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          config: mal-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
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            value: 0.5822416302765648
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          - type: recall
            value: 0.5822416302765648
      - task:
          type: BitextMining
        dataset:
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          config: ile-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 25.900000000000002
          - type: f1
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          - type: precision
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          - type: recall
            value: 25.900000000000002
      - task:
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        dataset:
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          config: bos-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
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            value: 9.322033898305085
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          - type: recall
            value: 9.322033898305085
      - task:
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        dataset:
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          config: cor-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
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            value: 4.3999999999999995
          - type: f1
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          - type: recall
            value: 4.3999999999999995
      - task:
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        dataset:
          type: mteb/tatoeba-bitext-mining
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          config: cat-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 18.8
          - type: f1
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          - type: precision
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          - type: recall
            value: 18.8
      - task:
          type: BitextMining
        dataset:
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          config: eus-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 10
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          - type: precision
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          - type: recall
            value: 10
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (yue-eng)
          config: yue-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 3.1
          - type: f1
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          - type: recall
            value: 3.1
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        dataset:
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          name: MTEB Tatoeba (swe-eng)
          config: swe-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 10.6
          - type: f1
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          - type: precision
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          - type: recall
            value: 10.6
      - task:
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        dataset:
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          config: dtp-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 4.3
          - type: f1
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          - type: precision
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          - type: recall
            value: 4.3
      - task:
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        dataset:
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          name: MTEB Tatoeba (kat-eng)
          config: kat-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 0.40214477211796246
          - type: f1
            value: 0.28150134048257375
          - type: precision
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          - type: recall
            value: 0.40214477211796246
      - task:
          type: BitextMining
        dataset:
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          config: jpn-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 3
          - type: f1
            value: 1.5834901411814404
          - type: precision
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          - type: recall
            value: 3
      - task:
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        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (csb-eng)
          config: csb-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 7.905138339920949
          - type: f1
            value: 6.6397047981096735
          - type: precision
            value: 6.32664437012263
          - type: recall
            value: 7.905138339920949
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (xho-eng)
          config: xho-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 3.5211267605633805
          - type: f1
            value: 2.173419196807775
          - type: precision
            value: 2.14388897487489
          - type: recall
            value: 3.5211267605633805
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (orv-eng)
          config: orv-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 0.23952095808383234
          - type: f1
            value: 0.001262128032547595
          - type: precision
            value: 0.0006327654461278806
          - type: recall
            value: 0.23952095808383234
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (ind-eng)
          config: ind-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 10.4
          - type: f1
            value: 8.370422351826372
          - type: precision
            value: 7.943809523809523
          - type: recall
            value: 10.4
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (tuk-eng)
          config: tuk-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 5.41871921182266
          - type: f1
            value: 3.4763895108722696
          - type: precision
            value: 3.1331846246882176
          - type: recall
            value: 5.41871921182266
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (max-eng)
          config: max-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 9.15492957746479
          - type: f1
            value: 7.267458920187794
          - type: precision
            value: 6.893803787858966
          - type: recall
            value: 9.15492957746479
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (swh-eng)
          config: swh-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 9.487179487179487
          - type: f1
            value: 6.902767160316073
          - type: precision
            value: 6.450346503818517
          - type: recall
            value: 9.487179487179487
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (hin-eng)
          config: hin-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 0.1
          - type: f1
            value: 0.0002042900919305414
          - type: precision
            value: 0.00010224948875255625
          - type: recall
            value: 0.1
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (dsb-eng)
          config: dsb-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 5.010438413361169
          - type: f1
            value: 3.8116647214505277
          - type: precision
            value: 3.5454644309619634
          - type: recall
            value: 5.010438413361169
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (ber-eng)
          config: ber-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 6.2
          - type: f1
            value: 5.213158915433869
          - type: precision
            value: 5.080398110661268
          - type: recall
            value: 6.2
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (tam-eng)
          config: tam-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 0.9771986970684038
          - type: f1
            value: 0.5061388123277374
          - type: precision
            value: 0.43431053203040165
          - type: recall
            value: 0.9771986970684038
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (slk-eng)
          config: slk-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 7.3
          - type: f1
            value: 5.6313180921027755
          - type: precision
            value: 5.303887400540395
          - type: recall
            value: 7.3
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (tgl-eng)
          config: tgl-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 3.5999999999999996
          - type: f1
            value: 3.2180089485458607
          - type: precision
            value: 3.1006756756756753
          - type: recall
            value: 3.5999999999999996
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (ast-eng)
          config: ast-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 22.04724409448819
          - type: f1
            value: 17.92525934258218
          - type: precision
            value: 16.48251629836593
          - type: recall
            value: 22.04724409448819
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (mkd-eng)
          config: mkd-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 0.5
          - type: f1
            value: 0.1543743186232414
          - type: precision
            value: 0.13554933572174951
          - type: recall
            value: 0.5
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (khm-eng)
          config: khm-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 0.8310249307479225
          - type: f1
            value: 0.5102255597841558
          - type: precision
            value: 0.4859595744731704
          - type: recall
            value: 0.8310249307479225
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (ces-eng)
          config: ces-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 6.9
          - type: f1
            value: 4.7258390633390635
          - type: precision
            value: 4.288366570275279
          - type: recall
            value: 6.9
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (tzl-eng)
          config: tzl-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 17.307692307692307
          - type: f1
            value: 14.763313609467454
          - type: precision
            value: 14.129273504273504
          - type: recall
            value: 17.307692307692307
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (urd-eng)
          config: urd-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 0.3
          - type: f1
            value: 0.0022196828248667185
          - type: precision
            value: 0.0011148527298850575
          - type: recall
            value: 0.3
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (ara-eng)
          config: ara-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 0.3
          - type: f1
            value: 0.3
          - type: precision
            value: 0.3
          - type: recall
            value: 0.3
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (kor-eng)
          config: kor-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 0.6
          - type: f1
            value: 0.500206611570248
          - type: precision
            value: 0.5001034126163392
          - type: recall
            value: 0.6
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (yid-eng)
          config: yid-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 0.4716981132075472
          - type: f1
            value: 0.2953377695417789
          - type: precision
            value: 0.2754210459668228
          - type: recall
            value: 0.4716981132075472
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (fin-eng)
          config: fin-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 4.3999999999999995
          - type: f1
            value: 3.6228414442700156
          - type: precision
            value: 3.4318238993710692
          - type: recall
            value: 4.3999999999999995
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (tha-eng)
          config: tha-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 1.2773722627737227
          - type: f1
            value: 1.0043318098096732
          - type: precision
            value: 0.9735777358593729
          - type: recall
            value: 1.2773722627737227
      - task:
          type: BitextMining
        dataset:
          type: mteb/tatoeba-bitext-mining
          name: MTEB Tatoeba (wuu-eng)
          config: wuu-eng
          split: test
          revision: 9080400076fbadbb4c4dcb136ff4eddc40b42553
        metrics:
          - type: accuracy
            value: 3.9
          - type: f1
            value: 2.6164533097276226
          - type: precision
            value: 2.3558186153594085
          - type: recall
            value: 3.9
      - task:
          type: Retrieval
        dataset:
          type: webis-touche2020
          name: MTEB Touche2020
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 1.5779999999999998
          - type: map_at_10
            value: 8.339
          - type: map_at_100
            value: 14.601
          - type: map_at_1000
            value: 16.104
          - type: map_at_3
            value: 4.06
          - type: map_at_5
            value: 6.049
          - type: mrr_at_1
            value: 18.367
          - type: mrr_at_10
            value: 35.178
          - type: mrr_at_100
            value: 36.464999999999996
          - type: mrr_at_1000
            value: 36.464999999999996
          - type: mrr_at_3
            value: 29.932
          - type: mrr_at_5
            value: 34.32
          - type: ndcg_at_1
            value: 16.326999999999998
          - type: ndcg_at_10
            value: 20.578
          - type: ndcg_at_100
            value: 34.285
          - type: ndcg_at_1000
            value: 45.853
          - type: ndcg_at_3
            value: 19.869999999999997
          - type: ndcg_at_5
            value: 22.081999999999997
          - type: precision_at_1
            value: 18.367
          - type: precision_at_10
            value: 19.796
          - type: precision_at_100
            value: 7.714
          - type: precision_at_1000
            value: 1.547
          - type: precision_at_3
            value: 23.128999999999998
          - type: precision_at_5
            value: 24.898
          - type: recall_at_1
            value: 1.5779999999999998
          - type: recall_at_10
            value: 14.801
          - type: recall_at_100
            value: 48.516999999999996
          - type: recall_at_1000
            value: 83.30300000000001
          - type: recall_at_3
            value: 5.267
          - type: recall_at_5
            value: 9.415999999999999
      - task:
          type: Classification
        dataset:
          type: mteb/toxic_conversations_50k
          name: MTEB ToxicConversationsClassification
          config: default
          split: test
          revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
        metrics:
          - type: accuracy
            value: 72.4186
          - type: ap
            value: 14.536282543597242
          - type: f1
            value: 55.47661372005608
      - task:
          type: Classification
        dataset:
          type: mteb/tweet_sentiment_extraction
          name: MTEB TweetSentimentExtractionClassification
          config: default
          split: test
          revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
        metrics:
          - type: accuracy
            value: 59.318053197509904
          - type: f1
            value: 59.68272481532353
      - task:
          type: Clustering
        dataset:
          type: mteb/twentynewsgroups-clustering
          name: MTEB TwentyNewsgroupsClustering
          config: default
          split: test
          revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
        metrics:
          - type: v_measure
            value: 52.155753554312
      - task:
          type: PairClassification
        dataset:
          type: mteb/twittersemeval2015-pairclassification
          name: MTEB TwitterSemEval2015
          config: default
          split: test
          revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
        metrics:
          - type: cos_sim_accuracy
            value: 86.99409906419503
          - type: cos_sim_ap
            value: 76.91824322304332
          - type: cos_sim_f1
            value: 70.97865694950546
          - type: cos_sim_precision
            value: 70.03081664098613
          - type: cos_sim_recall
            value: 71.95250659630607
          - type: dot_accuracy
            value: 85.37879239434942
          - type: dot_ap
            value: 71.86454698478344
          - type: dot_f1
            value: 66.48115355426259
          - type: dot_precision
            value: 63.84839650145773
          - type: dot_recall
            value: 69.34036939313984
          - type: euclidean_accuracy
            value: 87.00005960541218
          - type: euclidean_ap
            value: 76.9165913835565
          - type: euclidean_f1
            value: 71.23741557283039
          - type: euclidean_precision
            value: 68.89327088982007
          - type: euclidean_recall
            value: 73.7467018469657
          - type: manhattan_accuracy
            value: 87.06562555880075
          - type: manhattan_ap
            value: 76.85445703747546
          - type: manhattan_f1
            value: 70.95560571858539
          - type: manhattan_precision
            value: 67.61472275334609
          - type: manhattan_recall
            value: 74.64379947229551
          - type: max_accuracy
            value: 87.06562555880075
          - type: max_ap
            value: 76.91824322304332
          - type: max_f1
            value: 71.23741557283039
      - task:
          type: PairClassification
        dataset:
          type: mteb/twitterurlcorpus-pairclassification
          name: MTEB TwitterURLCorpus
          config: default
          split: test
          revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
        metrics:
          - type: cos_sim_accuracy
            value: 88.93934101758063
          - type: cos_sim_ap
            value: 86.1071528049007
          - type: cos_sim_f1
            value: 78.21588263552714
          - type: cos_sim_precision
            value: 75.20073900376609
          - type: cos_sim_recall
            value: 81.48290729904527
          - type: dot_accuracy
            value: 88.2504754142896
          - type: dot_ap
            value: 84.19709379723844
          - type: dot_f1
            value: 76.92307692307693
          - type: dot_precision
            value: 71.81969949916528
          - type: dot_recall
            value: 82.80720665229443
          - type: euclidean_accuracy
            value: 88.97232894787906
          - type: euclidean_ap
            value: 86.02763993294909
          - type: euclidean_f1
            value: 78.18372741427383
          - type: euclidean_precision
            value: 73.79861918107868
          - type: euclidean_recall
            value: 83.12288266091777
          - type: manhattan_accuracy
            value: 88.86948422400745
          - type: manhattan_ap
            value: 86.0009157821563
          - type: manhattan_f1
            value: 78.10668017659404
          - type: manhattan_precision
            value: 73.68564795848695
          - type: manhattan_recall
            value: 83.09208500153989
          - type: max_accuracy
            value: 88.97232894787906
          - type: max_ap
            value: 86.1071528049007
          - type: max_f1
            value: 78.21588263552714

GIST Embedding v0

GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning

The model is fine-tuned on top of the BAAI/bge-base-en-v1.5 using the MEDI dataset augmented with mined triplets from the MTEB Classification training dataset (excluding data from the Amazon Polarity Classification task).

The model does not require any instruction for generating embeddings. This means that queries for retrieval tasks can be directly encoded without crafting instructions.

Technical paper: GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning

Data

The dataset used is a compilation of the MEDI and MTEB Classification training datasets. Third-party datasets may be subject to additional terms and conditions under their associated licenses. A HuggingFace Dataset version of the compiled dataset, and the specific revision used to train the model, is available:

• Dataset: avsolatorio/medi-data-mteb_avs_triplets
• Revision: 238a0499b6e6b690cc64ea56fde8461daa8341bb

The dataset contains a task_type key, which can be used to select only the mteb classification tasks (prefixed with mteb_).

The MEDI Dataset is published in the following paper: One Embedder, Any Task: Instruction-Finetuned Text Embeddings.

The MTEB Benchmark results of the GIST embedding model, compared with the base model, suggest that the fine-tuning dataset has perturbed the model considerably, which resulted in significant improvements in certain tasks while adversely degrading performance in some.

The retrieval performance for the TRECCOVID task is of note. The fine-tuning dataset does not contain significant knowledge about COVID-19, which could have caused the observed performance degradation. We found some evidence, detailed in the paper, that thematic coverage of the fine-tuning data can affect downstream performance.

Usage

The model can be easily loaded using the Sentence Transformers library.

import torch.nn.functional as F
from sentence_transformers import SentenceTransformer

revision = None  # Replace with the specific revision to ensure reproducibility if the model is updated.

model = SentenceTransformer("avsolatorio/GIST-Embedding-v0", revision=revision)

texts = [
    "Illustration of the REaLTabFormer model. The left block shows the non-relational tabular data model using GPT-2 with a causal LM head. In contrast, the right block shows how a relational dataset's child table is modeled using a sequence-to-sequence (Seq2Seq) model. The Seq2Seq model uses the observations in the parent table to condition the generation of the observations in the child table. The trained GPT-2 model on the parent table, with weights frozen, is also used as the encoder in the Seq2Seq model.",
    "Predicting human mobility holds significant practical value, with applications ranging from enhancing disaster risk planning to simulating epidemic spread. In this paper, we present the GeoFormer, a decoder-only transformer model adapted from the GPT architecture to forecast human mobility.",
    "As the economies of Southeast Asia continue adopting digital technologies, policy makers increasingly ask how to prepare the workforce for emerging labor demands. However, little is known about the skills that workers need to adapt to these changes"
]

# Compute embeddings
embeddings = model.encode(texts, convert_to_tensor=True)

# Compute cosine-similarity for each pair of sentences
scores = F.cosine_similarity(embeddings.unsqueeze(1), embeddings.unsqueeze(0), dim=-1)

print(scores.cpu().numpy())

Training Parameters

Below are the training parameters used to fine-tune the model:

Epochs = 80
Warmup ratio = 0.1
Learning rate = 5e-6
Batch size = 32
Checkpoint step = 103500
Contrastive loss temperature = 0.01

Evaluation

The model was evaluated using the MTEB Evaluation suite.

Citation

Please cite our work if you use GISTEmbed or the datasets we published in your projects or research. 🤗

@article{solatorio2024gistembed,
    title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning},
    author={Aivin V. Solatorio},
    journal={arXiv preprint arXiv:2402.16829},
    year={2024},
    URL={https://arxiv.org/abs/2402.16829}
    eprint={2402.16829},
    archivePrefix={arXiv},
    primaryClass={cs.LG}
}

Acknowledgements

This work is supported by the "KCP IV - Exploring Data Use in the Development Economics Literature using Large Language Models (AI and LLMs)" project funded by the Knowledge for Change Program (KCP) of the World Bank - RA-P503405-RESE-TF0C3444.

The findings, interpretations, and conclusions expressed in this material are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.