gte-large-en-v1.5 / README.md
thenlper's picture
Update README.md
104333d verified
metadata
datasets:
  - allenai/c4
library_name: transformers
tags:
  - sentence-transformers
  - gte
  - mteb
  - transformers.js
  - sentence-similarity
license: apache-2.0
language:
  - en
model-index:
  - name: gte-large-en-v1.5
    results:
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_counterfactual
          name: MTEB AmazonCounterfactualClassification (en)
          config: en
          split: test
          revision: e8379541af4e31359cca9fbcf4b00f2671dba205
        metrics:
          - type: accuracy
            value: 73.01492537313432
          - type: ap
            value: 35.05341696659522
          - type: f1
            value: 66.71270310883853
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_polarity
          name: MTEB AmazonPolarityClassification
          config: default
          split: test
          revision: e2d317d38cd51312af73b3d32a06d1a08b442046
        metrics:
          - type: accuracy
            value: 93.97189999999999
          - type: ap
            value: 90.5952493948908
          - type: f1
            value: 93.95848137716877
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_reviews_multi
          name: MTEB AmazonReviewsClassification (en)
          config: en
          split: test
          revision: 1399c76144fd37290681b995c656ef9b2e06e26d
        metrics:
          - type: accuracy
            value: 54.196
          - type: f1
            value: 53.80122334012787
      - task:
          type: Retrieval
        dataset:
          type: mteb/arguana
          name: MTEB ArguAna
          config: default
          split: test
          revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
        metrics:
          - type: map_at_1
            value: 47.297
          - type: map_at_10
            value: 64.303
          - type: map_at_100
            value: 64.541
          - type: map_at_1000
            value: 64.541
          - type: map_at_3
            value: 60.728
          - type: map_at_5
            value: 63.114000000000004
          - type: mrr_at_1
            value: 48.435
          - type: mrr_at_10
            value: 64.657
          - type: mrr_at_100
            value: 64.901
          - type: mrr_at_1000
            value: 64.901
          - type: mrr_at_3
            value: 61.06
          - type: mrr_at_5
            value: 63.514
          - type: ndcg_at_1
            value: 47.297
          - type: ndcg_at_10
            value: 72.107
          - type: ndcg_at_100
            value: 72.963
          - type: ndcg_at_1000
            value: 72.963
          - type: ndcg_at_3
            value: 65.063
          - type: ndcg_at_5
            value: 69.352
          - type: precision_at_1
            value: 47.297
          - type: precision_at_10
            value: 9.623
          - type: precision_at_100
            value: 0.996
          - type: precision_at_1000
            value: 0.1
          - type: precision_at_3
            value: 25.865
          - type: precision_at_5
            value: 17.596
          - type: recall_at_1
            value: 47.297
          - type: recall_at_10
            value: 96.23
          - type: recall_at_100
            value: 99.644
          - type: recall_at_1000
            value: 99.644
          - type: recall_at_3
            value: 77.596
          - type: recall_at_5
            value: 87.98
      - task:
          type: Clustering
        dataset:
          type: mteb/arxiv-clustering-p2p
          name: MTEB ArxivClusteringP2P
          config: default
          split: test
          revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
        metrics:
          - type: v_measure
            value: 48.467787861077475
      - task:
          type: Clustering
        dataset:
          type: mteb/arxiv-clustering-s2s
          name: MTEB ArxivClusteringS2S
          config: default
          split: test
          revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
        metrics:
          - type: v_measure
            value: 43.39198391914257
      - task:
          type: Reranking
        dataset:
          type: mteb/askubuntudupquestions-reranking
          name: MTEB AskUbuntuDupQuestions
          config: default
          split: test
          revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
        metrics:
          - type: map
            value: 63.12794820591384
          - type: mrr
            value: 75.9331442641692
      - task:
          type: STS
        dataset:
          type: mteb/biosses-sts
          name: MTEB BIOSSES
          config: default
          split: test
          revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
        metrics:
          - type: cos_sim_pearson
            value: 87.85062993863319
          - type: cos_sim_spearman
            value: 85.39049989733459
          - type: euclidean_pearson
            value: 86.00222680278333
          - type: euclidean_spearman
            value: 85.45556162077396
          - type: manhattan_pearson
            value: 85.88769871785621
          - type: manhattan_spearman
            value: 85.11760211290839
      - task:
          type: Classification
        dataset:
          type: mteb/banking77
          name: MTEB Banking77Classification
          config: default
          split: test
          revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
        metrics:
          - type: accuracy
            value: 87.32792207792208
          - type: f1
            value: 87.29132945999555
      - task:
          type: Clustering
        dataset:
          type: mteb/biorxiv-clustering-p2p
          name: MTEB BiorxivClusteringP2P
          config: default
          split: test
          revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
        metrics:
          - type: v_measure
            value: 40.5779328301945
      - task:
          type: Clustering
        dataset:
          type: mteb/biorxiv-clustering-s2s
          name: MTEB BiorxivClusteringS2S
          config: default
          split: test
          revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
        metrics:
          - type: v_measure
            value: 37.94425623865118
      - task:
          type: Retrieval
        dataset:
          type: mteb/cqadupstack-android
          name: MTEB CQADupstackAndroidRetrieval
          config: default
          split: test
          revision: f46a197baaae43b4f621051089b82a364682dfeb
        metrics:
          - type: map_at_1
            value: 32.978
          - type: map_at_10
            value: 44.45
          - type: map_at_100
            value: 46.19
          - type: map_at_1000
            value: 46.303
          - type: map_at_3
            value: 40.849000000000004
          - type: map_at_5
            value: 42.55
          - type: mrr_at_1
            value: 40.629
          - type: mrr_at_10
            value: 50.848000000000006
          - type: mrr_at_100
            value: 51.669
          - type: mrr_at_1000
            value: 51.705
          - type: mrr_at_3
            value: 47.997
          - type: mrr_at_5
            value: 49.506
          - type: ndcg_at_1
            value: 40.629
          - type: ndcg_at_10
            value: 51.102000000000004
          - type: ndcg_at_100
            value: 57.159000000000006
          - type: ndcg_at_1000
            value: 58.669000000000004
          - type: ndcg_at_3
            value: 45.738
          - type: ndcg_at_5
            value: 47.632999999999996
          - type: precision_at_1
            value: 40.629
          - type: precision_at_10
            value: 9.700000000000001
          - type: precision_at_100
            value: 1.5970000000000002
          - type: precision_at_1000
            value: 0.202
          - type: precision_at_3
            value: 21.698
          - type: precision_at_5
            value: 15.393
          - type: recall_at_1
            value: 32.978
          - type: recall_at_10
            value: 63.711
          - type: recall_at_100
            value: 88.39399999999999
          - type: recall_at_1000
            value: 97.513
          - type: recall_at_3
            value: 48.025
          - type: recall_at_5
            value: 53.52
      - task:
          type: Retrieval
        dataset:
          type: mteb/cqadupstack-english
          name: MTEB CQADupstackEnglishRetrieval
          config: default
          split: test
          revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
        metrics:
          - type: map_at_1
            value: 30.767
          - type: map_at_10
            value: 42.195
          - type: map_at_100
            value: 43.541999999999994
          - type: map_at_1000
            value: 43.673
          - type: map_at_3
            value: 38.561
          - type: map_at_5
            value: 40.532000000000004
          - type: mrr_at_1
            value: 38.79
          - type: mrr_at_10
            value: 48.021
          - type: mrr_at_100
            value: 48.735
          - type: mrr_at_1000
            value: 48.776
          - type: mrr_at_3
            value: 45.594
          - type: mrr_at_5
            value: 46.986
          - type: ndcg_at_1
            value: 38.79
          - type: ndcg_at_10
            value: 48.468
          - type: ndcg_at_100
            value: 53.037
          - type: ndcg_at_1000
            value: 55.001999999999995
          - type: ndcg_at_3
            value: 43.409
          - type: ndcg_at_5
            value: 45.654
          - type: precision_at_1
            value: 38.79
          - type: precision_at_10
            value: 9.452
          - type: precision_at_100
            value: 1.518
          - type: precision_at_1000
            value: 0.201
          - type: precision_at_3
            value: 21.21
          - type: precision_at_5
            value: 15.171999999999999
          - type: recall_at_1
            value: 30.767
          - type: recall_at_10
            value: 60.118
          - type: recall_at_100
            value: 79.271
          - type: recall_at_1000
            value: 91.43299999999999
          - type: recall_at_3
            value: 45.36
          - type: recall_at_5
            value: 51.705
      - task:
          type: Retrieval
        dataset:
          type: mteb/cqadupstack-gaming
          name: MTEB CQADupstackGamingRetrieval
          config: default
          split: test
          revision: 4885aa143210c98657558c04aaf3dc47cfb54340
        metrics:
          - type: map_at_1
            value: 40.007
          - type: map_at_10
            value: 53.529
          - type: map_at_100
            value: 54.602
          - type: map_at_1000
            value: 54.647
          - type: map_at_3
            value: 49.951
          - type: map_at_5
            value: 52.066
          - type: mrr_at_1
            value: 45.705
          - type: mrr_at_10
            value: 56.745000000000005
          - type: mrr_at_100
            value: 57.43899999999999
          - type: mrr_at_1000
            value: 57.462999999999994
          - type: mrr_at_3
            value: 54.25299999999999
          - type: mrr_at_5
            value: 55.842000000000006
          - type: ndcg_at_1
            value: 45.705
          - type: ndcg_at_10
            value: 59.809
          - type: ndcg_at_100
            value: 63.837999999999994
          - type: ndcg_at_1000
            value: 64.729
          - type: ndcg_at_3
            value: 53.994
          - type: ndcg_at_5
            value: 57.028
          - type: precision_at_1
            value: 45.705
          - type: precision_at_10
            value: 9.762
          - type: precision_at_100
            value: 1.275
          - type: precision_at_1000
            value: 0.13899999999999998
          - type: precision_at_3
            value: 24.368000000000002
          - type: precision_at_5
            value: 16.84
          - type: recall_at_1
            value: 40.007
          - type: recall_at_10
            value: 75.017
          - type: recall_at_100
            value: 91.99000000000001
          - type: recall_at_1000
            value: 98.265
          - type: recall_at_3
            value: 59.704
          - type: recall_at_5
            value: 67.109
      - task:
          type: Retrieval
        dataset:
          type: mteb/cqadupstack-gis
          name: MTEB CQADupstackGisRetrieval
          config: default
          split: test
          revision: 5003b3064772da1887988e05400cf3806fe491f2
        metrics:
          - type: map_at_1
            value: 26.639000000000003
          - type: map_at_10
            value: 35.926
          - type: map_at_100
            value: 37.126999999999995
          - type: map_at_1000
            value: 37.202
          - type: map_at_3
            value: 32.989000000000004
          - type: map_at_5
            value: 34.465
          - type: mrr_at_1
            value: 28.475
          - type: mrr_at_10
            value: 37.7
          - type: mrr_at_100
            value: 38.753
          - type: mrr_at_1000
            value: 38.807
          - type: mrr_at_3
            value: 35.066
          - type: mrr_at_5
            value: 36.512
          - type: ndcg_at_1
            value: 28.475
          - type: ndcg_at_10
            value: 41.245
          - type: ndcg_at_100
            value: 46.814
          - type: ndcg_at_1000
            value: 48.571
          - type: ndcg_at_3
            value: 35.528999999999996
          - type: ndcg_at_5
            value: 38.066
          - type: precision_at_1
            value: 28.475
          - type: precision_at_10
            value: 6.497
          - type: precision_at_100
            value: 0.9650000000000001
          - type: precision_at_1000
            value: 0.11499999999999999
          - type: precision_at_3
            value: 15.065999999999999
          - type: precision_at_5
            value: 10.599
          - type: recall_at_1
            value: 26.639000000000003
          - type: recall_at_10
            value: 55.759
          - type: recall_at_100
            value: 80.913
          - type: recall_at_1000
            value: 93.929
          - type: recall_at_3
            value: 40.454
          - type: recall_at_5
            value: 46.439
      - task:
          type: Retrieval
        dataset:
          type: mteb/cqadupstack-mathematica
          name: MTEB CQADupstackMathematicaRetrieval
          config: default
          split: test
          revision: 90fceea13679c63fe563ded68f3b6f06e50061de
        metrics:
          - type: map_at_1
            value: 15.767999999999999
          - type: map_at_10
            value: 24.811
          - type: map_at_100
            value: 26.064999999999998
          - type: map_at_1000
            value: 26.186999999999998
          - type: map_at_3
            value: 21.736
          - type: map_at_5
            value: 23.283
          - type: mrr_at_1
            value: 19.527
          - type: mrr_at_10
            value: 29.179
          - type: mrr_at_100
            value: 30.153999999999996
          - type: mrr_at_1000
            value: 30.215999999999998
          - type: mrr_at_3
            value: 26.223000000000003
          - type: mrr_at_5
            value: 27.733999999999998
          - type: ndcg_at_1
            value: 19.527
          - type: ndcg_at_10
            value: 30.786
          - type: ndcg_at_100
            value: 36.644
          - type: ndcg_at_1000
            value: 39.440999999999995
          - type: ndcg_at_3
            value: 24.958
          - type: ndcg_at_5
            value: 27.392
          - type: precision_at_1
            value: 19.527
          - type: precision_at_10
            value: 5.995
          - type: precision_at_100
            value: 1.03
          - type: precision_at_1000
            value: 0.14100000000000001
          - type: precision_at_3
            value: 12.520999999999999
          - type: precision_at_5
            value: 9.129
          - type: recall_at_1
            value: 15.767999999999999
          - type: recall_at_10
            value: 44.824000000000005
          - type: recall_at_100
            value: 70.186
          - type: recall_at_1000
            value: 89.934
          - type: recall_at_3
            value: 28.607
          - type: recall_at_5
            value: 34.836
      - task:
          type: Retrieval
        dataset:
          type: mteb/cqadupstack-physics
          name: MTEB CQADupstackPhysicsRetrieval
          config: default
          split: test
          revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
        metrics:
          - type: map_at_1
            value: 31.952
          - type: map_at_10
            value: 44.438
          - type: map_at_100
            value: 45.778
          - type: map_at_1000
            value: 45.883
          - type: map_at_3
            value: 41.044000000000004
          - type: map_at_5
            value: 42.986000000000004
          - type: mrr_at_1
            value: 39.172000000000004
          - type: mrr_at_10
            value: 49.76
          - type: mrr_at_100
            value: 50.583999999999996
          - type: mrr_at_1000
            value: 50.621
          - type: mrr_at_3
            value: 47.353
          - type: mrr_at_5
            value: 48.739
          - type: ndcg_at_1
            value: 39.172000000000004
          - type: ndcg_at_10
            value: 50.760000000000005
          - type: ndcg_at_100
            value: 56.084
          - type: ndcg_at_1000
            value: 57.865
          - type: ndcg_at_3
            value: 45.663
          - type: ndcg_at_5
            value: 48.178
          - type: precision_at_1
            value: 39.172000000000004
          - type: precision_at_10
            value: 9.22
          - type: precision_at_100
            value: 1.387
          - type: precision_at_1000
            value: 0.17099999999999999
          - type: precision_at_3
            value: 21.976000000000003
          - type: precision_at_5
            value: 15.457
          - type: recall_at_1
            value: 31.952
          - type: recall_at_10
            value: 63.900999999999996
          - type: recall_at_100
            value: 85.676
          - type: recall_at_1000
            value: 97.03699999999999
          - type: recall_at_3
            value: 49.781
          - type: recall_at_5
            value: 56.330000000000005
      - task:
          type: Retrieval
        dataset:
          type: mteb/cqadupstack-programmers
          name: MTEB CQADupstackProgrammersRetrieval
          config: default
          split: test
          revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
        metrics:
          - type: map_at_1
            value: 25.332
          - type: map_at_10
            value: 36.874
          - type: map_at_100
            value: 38.340999999999994
          - type: map_at_1000
            value: 38.452
          - type: map_at_3
            value: 33.068
          - type: map_at_5
            value: 35.324
          - type: mrr_at_1
            value: 30.822
          - type: mrr_at_10
            value: 41.641
          - type: mrr_at_100
            value: 42.519
          - type: mrr_at_1000
            value: 42.573
          - type: mrr_at_3
            value: 38.413000000000004
          - type: mrr_at_5
            value: 40.542
          - type: ndcg_at_1
            value: 30.822
          - type: ndcg_at_10
            value: 43.414
          - type: ndcg_at_100
            value: 49.196
          - type: ndcg_at_1000
            value: 51.237
          - type: ndcg_at_3
            value: 37.230000000000004
          - type: ndcg_at_5
            value: 40.405
          - type: precision_at_1
            value: 30.822
          - type: precision_at_10
            value: 8.379
          - type: precision_at_100
            value: 1.315
          - type: precision_at_1000
            value: 0.168
          - type: precision_at_3
            value: 18.417
          - type: precision_at_5
            value: 13.744
          - type: recall_at_1
            value: 25.332
          - type: recall_at_10
            value: 57.774
          - type: recall_at_100
            value: 82.071
          - type: recall_at_1000
            value: 95.60600000000001
          - type: recall_at_3
            value: 40.722
          - type: recall_at_5
            value: 48.754999999999995
      - task:
          type: Retrieval
        dataset:
          type: mteb/cqadupstack
          name: MTEB CQADupstackRetrieval
          config: default
          split: test
          revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
        metrics:
          - type: map_at_1
            value: 25.91033333333334
          - type: map_at_10
            value: 36.23225000000001
          - type: map_at_100
            value: 37.55766666666667
          - type: map_at_1000
            value: 37.672583333333336
          - type: map_at_3
            value: 32.95666666666667
          - type: map_at_5
            value: 34.73375
          - type: mrr_at_1
            value: 30.634
          - type: mrr_at_10
            value: 40.19449999999999
          - type: mrr_at_100
            value: 41.099250000000005
          - type: mrr_at_1000
            value: 41.15091666666667
          - type: mrr_at_3
            value: 37.4615
          - type: mrr_at_5
            value: 39.00216666666667
          - type: ndcg_at_1
            value: 30.634
          - type: ndcg_at_10
            value: 42.162166666666664
          - type: ndcg_at_100
            value: 47.60708333333333
          - type: ndcg_at_1000
            value: 49.68616666666666
          - type: ndcg_at_3
            value: 36.60316666666666
          - type: ndcg_at_5
            value: 39.15616666666668
          - type: precision_at_1
            value: 30.634
          - type: precision_at_10
            value: 7.6193333333333335
          - type: precision_at_100
            value: 1.2198333333333333
          - type: precision_at_1000
            value: 0.15975000000000003
          - type: precision_at_3
            value: 17.087
          - type: precision_at_5
            value: 12.298333333333334
          - type: recall_at_1
            value: 25.91033333333334
          - type: recall_at_10
            value: 55.67300000000001
          - type: recall_at_100
            value: 79.20608333333334
          - type: recall_at_1000
            value: 93.34866666666667
          - type: recall_at_3
            value: 40.34858333333333
          - type: recall_at_5
            value: 46.834083333333325
      - task:
          type: Retrieval
        dataset:
          type: mteb/cqadupstack-stats
          name: MTEB CQADupstackStatsRetrieval
          config: default
          split: test
          revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
        metrics:
          - type: map_at_1
            value: 25.006
          - type: map_at_10
            value: 32.177
          - type: map_at_100
            value: 33.324999999999996
          - type: map_at_1000
            value: 33.419
          - type: map_at_3
            value: 29.952
          - type: map_at_5
            value: 31.095
          - type: mrr_at_1
            value: 28.066999999999997
          - type: mrr_at_10
            value: 34.995
          - type: mrr_at_100
            value: 35.978
          - type: mrr_at_1000
            value: 36.042
          - type: mrr_at_3
            value: 33.103
          - type: mrr_at_5
            value: 34.001
          - type: ndcg_at_1
            value: 28.066999999999997
          - type: ndcg_at_10
            value: 36.481
          - type: ndcg_at_100
            value: 42.022999999999996
          - type: ndcg_at_1000
            value: 44.377
          - type: ndcg_at_3
            value: 32.394
          - type: ndcg_at_5
            value: 34.108
          - type: precision_at_1
            value: 28.066999999999997
          - type: precision_at_10
            value: 5.736
          - type: precision_at_100
            value: 0.9259999999999999
          - type: precision_at_1000
            value: 0.12
          - type: precision_at_3
            value: 13.804
          - type: precision_at_5
            value: 9.508999999999999
          - type: recall_at_1
            value: 25.006
          - type: recall_at_10
            value: 46.972
          - type: recall_at_100
            value: 72.138
          - type: recall_at_1000
            value: 89.479
          - type: recall_at_3
            value: 35.793
          - type: recall_at_5
            value: 39.947
      - task:
          type: Retrieval
        dataset:
          type: mteb/cqadupstack-tex
          name: MTEB CQADupstackTexRetrieval
          config: default
          split: test
          revision: 46989137a86843e03a6195de44b09deda022eec7
        metrics:
          - type: map_at_1
            value: 16.07
          - type: map_at_10
            value: 24.447
          - type: map_at_100
            value: 25.685999999999996
          - type: map_at_1000
            value: 25.813999999999997
          - type: map_at_3
            value: 21.634
          - type: map_at_5
            value: 23.133
          - type: mrr_at_1
            value: 19.580000000000002
          - type: mrr_at_10
            value: 28.127999999999997
          - type: mrr_at_100
            value: 29.119
          - type: mrr_at_1000
            value: 29.192
          - type: mrr_at_3
            value: 25.509999999999998
          - type: mrr_at_5
            value: 26.878
          - type: ndcg_at_1
            value: 19.580000000000002
          - type: ndcg_at_10
            value: 29.804000000000002
          - type: ndcg_at_100
            value: 35.555
          - type: ndcg_at_1000
            value: 38.421
          - type: ndcg_at_3
            value: 24.654999999999998
          - type: ndcg_at_5
            value: 26.881
          - type: precision_at_1
            value: 19.580000000000002
          - type: precision_at_10
            value: 5.736
          - type: precision_at_100
            value: 1.005
          - type: precision_at_1000
            value: 0.145
          - type: precision_at_3
            value: 12.033000000000001
          - type: precision_at_5
            value: 8.871
          - type: recall_at_1
            value: 16.07
          - type: recall_at_10
            value: 42.364000000000004
          - type: recall_at_100
            value: 68.01899999999999
          - type: recall_at_1000
            value: 88.122
          - type: recall_at_3
            value: 27.846
          - type: recall_at_5
            value: 33.638
      - task:
          type: Retrieval
        dataset:
          type: mteb/cqadupstack-unix
          name: MTEB CQADupstackUnixRetrieval
          config: default
          split: test
          revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
        metrics:
          - type: map_at_1
            value: 26.365
          - type: map_at_10
            value: 36.591
          - type: map_at_100
            value: 37.730000000000004
          - type: map_at_1000
            value: 37.84
          - type: map_at_3
            value: 33.403
          - type: map_at_5
            value: 35.272999999999996
          - type: mrr_at_1
            value: 30.503999999999998
          - type: mrr_at_10
            value: 39.940999999999995
          - type: mrr_at_100
            value: 40.818
          - type: mrr_at_1000
            value: 40.876000000000005
          - type: mrr_at_3
            value: 37.065
          - type: mrr_at_5
            value: 38.814
          - type: ndcg_at_1
            value: 30.503999999999998
          - type: ndcg_at_10
            value: 42.185
          - type: ndcg_at_100
            value: 47.416000000000004
          - type: ndcg_at_1000
            value: 49.705
          - type: ndcg_at_3
            value: 36.568
          - type: ndcg_at_5
            value: 39.416000000000004
          - type: precision_at_1
            value: 30.503999999999998
          - type: precision_at_10
            value: 7.276000000000001
          - type: precision_at_100
            value: 1.118
          - type: precision_at_1000
            value: 0.14300000000000002
          - type: precision_at_3
            value: 16.729
          - type: precision_at_5
            value: 12.107999999999999
          - type: recall_at_1
            value: 26.365
          - type: recall_at_10
            value: 55.616
          - type: recall_at_100
            value: 78.129
          - type: recall_at_1000
            value: 93.95599999999999
          - type: recall_at_3
            value: 40.686
          - type: recall_at_5
            value: 47.668
      - task:
          type: Retrieval
        dataset:
          type: mteb/cqadupstack-webmasters
          name: MTEB CQADupstackWebmastersRetrieval
          config: default
          split: test
          revision: 160c094312a0e1facb97e55eeddb698c0abe3571
        metrics:
          - type: map_at_1
            value: 22.750999999999998
          - type: map_at_10
            value: 33.446
          - type: map_at_100
            value: 35.235
          - type: map_at_1000
            value: 35.478
          - type: map_at_3
            value: 29.358
          - type: map_at_5
            value: 31.525
          - type: mrr_at_1
            value: 27.668
          - type: mrr_at_10
            value: 37.694
          - type: mrr_at_100
            value: 38.732
          - type: mrr_at_1000
            value: 38.779
          - type: mrr_at_3
            value: 34.223
          - type: mrr_at_5
            value: 36.08
          - type: ndcg_at_1
            value: 27.668
          - type: ndcg_at_10
            value: 40.557
          - type: ndcg_at_100
            value: 46.605999999999995
          - type: ndcg_at_1000
            value: 48.917
          - type: ndcg_at_3
            value: 33.677
          - type: ndcg_at_5
            value: 36.85
          - type: precision_at_1
            value: 27.668
          - type: precision_at_10
            value: 8.3
          - type: precision_at_100
            value: 1.6260000000000001
          - type: precision_at_1000
            value: 0.253
          - type: precision_at_3
            value: 16.008
          - type: precision_at_5
            value: 12.292
          - type: recall_at_1
            value: 22.750999999999998
          - type: recall_at_10
            value: 55.643
          - type: recall_at_100
            value: 82.151
          - type: recall_at_1000
            value: 95.963
          - type: recall_at_3
            value: 36.623
          - type: recall_at_5
            value: 44.708
      - task:
          type: Retrieval
        dataset:
          type: mteb/cqadupstack-wordpress
          name: MTEB CQADupstackWordpressRetrieval
          config: default
          split: test
          revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
        metrics:
          - type: map_at_1
            value: 17.288999999999998
          - type: map_at_10
            value: 25.903
          - type: map_at_100
            value: 27.071
          - type: map_at_1000
            value: 27.173000000000002
          - type: map_at_3
            value: 22.935
          - type: map_at_5
            value: 24.573
          - type: mrr_at_1
            value: 18.669
          - type: mrr_at_10
            value: 27.682000000000002
          - type: mrr_at_100
            value: 28.691
          - type: mrr_at_1000
            value: 28.761
          - type: mrr_at_3
            value: 24.738
          - type: mrr_at_5
            value: 26.392
          - type: ndcg_at_1
            value: 18.669
          - type: ndcg_at_10
            value: 31.335
          - type: ndcg_at_100
            value: 36.913000000000004
          - type: ndcg_at_1000
            value: 39.300000000000004
          - type: ndcg_at_3
            value: 25.423000000000002
          - type: ndcg_at_5
            value: 28.262999999999998
          - type: precision_at_1
            value: 18.669
          - type: precision_at_10
            value: 5.379
          - type: precision_at_100
            value: 0.876
          - type: precision_at_1000
            value: 0.11900000000000001
          - type: precision_at_3
            value: 11.214
          - type: precision_at_5
            value: 8.466
          - type: recall_at_1
            value: 17.288999999999998
          - type: recall_at_10
            value: 46.377
          - type: recall_at_100
            value: 71.53500000000001
          - type: recall_at_1000
            value: 88.947
          - type: recall_at_3
            value: 30.581999999999997
          - type: recall_at_5
            value: 37.354
      - task:
          type: Retrieval
        dataset:
          type: mteb/climate-fever
          name: MTEB ClimateFEVER
          config: default
          split: test
          revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
        metrics:
          - type: map_at_1
            value: 21.795
          - type: map_at_10
            value: 37.614999999999995
          - type: map_at_100
            value: 40.037
          - type: map_at_1000
            value: 40.184999999999995
          - type: map_at_3
            value: 32.221
          - type: map_at_5
            value: 35.154999999999994
          - type: mrr_at_1
            value: 50.358000000000004
          - type: mrr_at_10
            value: 62.129
          - type: mrr_at_100
            value: 62.613
          - type: mrr_at_1000
            value: 62.62
          - type: mrr_at_3
            value: 59.272999999999996
          - type: mrr_at_5
            value: 61.138999999999996
          - type: ndcg_at_1
            value: 50.358000000000004
          - type: ndcg_at_10
            value: 48.362
          - type: ndcg_at_100
            value: 55.932
          - type: ndcg_at_1000
            value: 58.062999999999995
          - type: ndcg_at_3
            value: 42.111
          - type: ndcg_at_5
            value: 44.063
          - type: precision_at_1
            value: 50.358000000000004
          - type: precision_at_10
            value: 14.677999999999999
          - type: precision_at_100
            value: 2.2950000000000004
          - type: precision_at_1000
            value: 0.271
          - type: precision_at_3
            value: 31.77
          - type: precision_at_5
            value: 23.375
          - type: recall_at_1
            value: 21.795
          - type: recall_at_10
            value: 53.846000000000004
          - type: recall_at_100
            value: 78.952
          - type: recall_at_1000
            value: 90.41900000000001
          - type: recall_at_3
            value: 37.257
          - type: recall_at_5
            value: 44.661
      - task:
          type: Retrieval
        dataset:
          type: mteb/dbpedia
          name: MTEB DBPedia
          config: default
          split: test
          revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
        metrics:
          - type: map_at_1
            value: 9.728
          - type: map_at_10
            value: 22.691
          - type: map_at_100
            value: 31.734
          - type: map_at_1000
            value: 33.464
          - type: map_at_3
            value: 16.273
          - type: map_at_5
            value: 19.016
          - type: mrr_at_1
            value: 73.25
          - type: mrr_at_10
            value: 80.782
          - type: mrr_at_100
            value: 81.01899999999999
          - type: mrr_at_1000
            value: 81.021
          - type: mrr_at_3
            value: 79.583
          - type: mrr_at_5
            value: 80.146
          - type: ndcg_at_1
            value: 59.62499999999999
          - type: ndcg_at_10
            value: 46.304
          - type: ndcg_at_100
            value: 51.23
          - type: ndcg_at_1000
            value: 58.048
          - type: ndcg_at_3
            value: 51.541000000000004
          - type: ndcg_at_5
            value: 48.635
          - type: precision_at_1
            value: 73.25
          - type: precision_at_10
            value: 36.375
          - type: precision_at_100
            value: 11.53
          - type: precision_at_1000
            value: 2.23
          - type: precision_at_3
            value: 55.583000000000006
          - type: precision_at_5
            value: 47.15
          - type: recall_at_1
            value: 9.728
          - type: recall_at_10
            value: 28.793999999999997
          - type: recall_at_100
            value: 57.885
          - type: recall_at_1000
            value: 78.759
          - type: recall_at_3
            value: 17.79
          - type: recall_at_5
            value: 21.733
      - task:
          type: Classification
        dataset:
          type: mteb/emotion
          name: MTEB EmotionClassification
          config: default
          split: test
          revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
        metrics:
          - type: accuracy
            value: 46.775
          - type: f1
            value: 41.89794273264891
      - task:
          type: Retrieval
        dataset:
          type: mteb/fever
          name: MTEB FEVER
          config: default
          split: test
          revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
        metrics:
          - type: map_at_1
            value: 85.378
          - type: map_at_10
            value: 91.51
          - type: map_at_100
            value: 91.666
          - type: map_at_1000
            value: 91.676
          - type: map_at_3
            value: 90.757
          - type: map_at_5
            value: 91.277
          - type: mrr_at_1
            value: 91.839
          - type: mrr_at_10
            value: 95.49
          - type: mrr_at_100
            value: 95.493
          - type: mrr_at_1000
            value: 95.493
          - type: mrr_at_3
            value: 95.345
          - type: mrr_at_5
            value: 95.47200000000001
          - type: ndcg_at_1
            value: 91.839
          - type: ndcg_at_10
            value: 93.806
          - type: ndcg_at_100
            value: 94.255
          - type: ndcg_at_1000
            value: 94.399
          - type: ndcg_at_3
            value: 93.027
          - type: ndcg_at_5
            value: 93.51
          - type: precision_at_1
            value: 91.839
          - type: precision_at_10
            value: 10.93
          - type: precision_at_100
            value: 1.1400000000000001
          - type: precision_at_1000
            value: 0.117
          - type: precision_at_3
            value: 34.873
          - type: precision_at_5
            value: 21.44
          - type: recall_at_1
            value: 85.378
          - type: recall_at_10
            value: 96.814
          - type: recall_at_100
            value: 98.386
          - type: recall_at_1000
            value: 99.21600000000001
          - type: recall_at_3
            value: 94.643
          - type: recall_at_5
            value: 95.976
      - task:
          type: Retrieval
        dataset:
          type: mteb/fiqa
          name: MTEB FiQA2018
          config: default
          split: test
          revision: 27a168819829fe9bcd655c2df245fb19452e8e06
        metrics:
          - type: map_at_1
            value: 32.190000000000005
          - type: map_at_10
            value: 53.605000000000004
          - type: map_at_100
            value: 55.550999999999995
          - type: map_at_1000
            value: 55.665
          - type: map_at_3
            value: 46.62
          - type: map_at_5
            value: 50.517999999999994
          - type: mrr_at_1
            value: 60.34
          - type: mrr_at_10
            value: 70.775
          - type: mrr_at_100
            value: 71.238
          - type: mrr_at_1000
            value: 71.244
          - type: mrr_at_3
            value: 68.72399999999999
          - type: mrr_at_5
            value: 69.959
          - type: ndcg_at_1
            value: 60.34
          - type: ndcg_at_10
            value: 63.226000000000006
          - type: ndcg_at_100
            value: 68.60300000000001
          - type: ndcg_at_1000
            value: 69.901
          - type: ndcg_at_3
            value: 58.048
          - type: ndcg_at_5
            value: 59.789
          - type: precision_at_1
            value: 60.34
          - type: precision_at_10
            value: 17.130000000000003
          - type: precision_at_100
            value: 2.29
          - type: precision_at_1000
            value: 0.256
          - type: precision_at_3
            value: 38.323
          - type: precision_at_5
            value: 27.87
          - type: recall_at_1
            value: 32.190000000000005
          - type: recall_at_10
            value: 73.041
          - type: recall_at_100
            value: 91.31
          - type: recall_at_1000
            value: 98.104
          - type: recall_at_3
            value: 53.70399999999999
          - type: recall_at_5
            value: 62.358999999999995
      - task:
          type: Retrieval
        dataset:
          type: mteb/hotpotqa
          name: MTEB HotpotQA
          config: default
          split: test
          revision: ab518f4d6fcca38d87c25209f94beba119d02014
        metrics:
          - type: map_at_1
            value: 43.511
          - type: map_at_10
            value: 58.15
          - type: map_at_100
            value: 58.95399999999999
          - type: map_at_1000
            value: 59.018
          - type: map_at_3
            value: 55.31700000000001
          - type: map_at_5
            value: 57.04900000000001
          - type: mrr_at_1
            value: 87.022
          - type: mrr_at_10
            value: 91.32000000000001
          - type: mrr_at_100
            value: 91.401
          - type: mrr_at_1000
            value: 91.403
          - type: mrr_at_3
            value: 90.77
          - type: mrr_at_5
            value: 91.156
          - type: ndcg_at_1
            value: 87.022
          - type: ndcg_at_10
            value: 68.183
          - type: ndcg_at_100
            value: 70.781
          - type: ndcg_at_1000
            value: 72.009
          - type: ndcg_at_3
            value: 64.334
          - type: ndcg_at_5
            value: 66.449
          - type: precision_at_1
            value: 87.022
          - type: precision_at_10
            value: 13.406
          - type: precision_at_100
            value: 1.542
          - type: precision_at_1000
            value: 0.17099999999999999
          - type: precision_at_3
            value: 39.023
          - type: precision_at_5
            value: 25.080000000000002
          - type: recall_at_1
            value: 43.511
          - type: recall_at_10
            value: 67.02900000000001
          - type: recall_at_100
            value: 77.11
          - type: recall_at_1000
            value: 85.294
          - type: recall_at_3
            value: 58.535000000000004
          - type: recall_at_5
            value: 62.70099999999999
      - task:
          type: Classification
        dataset:
          type: mteb/imdb
          name: MTEB ImdbClassification
          config: default
          split: test
          revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
        metrics:
          - type: accuracy
            value: 92.0996
          - type: ap
            value: 87.86206089096373
          - type: f1
            value: 92.07554547510763
      - task:
          type: Retrieval
        dataset:
          type: mteb/msmarco
          name: MTEB MSMARCO
          config: default
          split: dev
          revision: c5a29a104738b98a9e76336939199e264163d4a0
        metrics:
          - type: map_at_1
            value: 23.179
          - type: map_at_10
            value: 35.86
          - type: map_at_100
            value: 37.025999999999996
          - type: map_at_1000
            value: 37.068
          - type: map_at_3
            value: 31.921
          - type: map_at_5
            value: 34.172000000000004
          - type: mrr_at_1
            value: 23.926
          - type: mrr_at_10
            value: 36.525999999999996
          - type: mrr_at_100
            value: 37.627
          - type: mrr_at_1000
            value: 37.665
          - type: mrr_at_3
            value: 32.653
          - type: mrr_at_5
            value: 34.897
          - type: ndcg_at_1
            value: 23.910999999999998
          - type: ndcg_at_10
            value: 42.927
          - type: ndcg_at_100
            value: 48.464
          - type: ndcg_at_1000
            value: 49.533
          - type: ndcg_at_3
            value: 34.910000000000004
          - type: ndcg_at_5
            value: 38.937
          - type: precision_at_1
            value: 23.910999999999998
          - type: precision_at_10
            value: 6.758
          - type: precision_at_100
            value: 0.9520000000000001
          - type: precision_at_1000
            value: 0.104
          - type: precision_at_3
            value: 14.838000000000001
          - type: precision_at_5
            value: 10.934000000000001
          - type: recall_at_1
            value: 23.179
          - type: recall_at_10
            value: 64.622
          - type: recall_at_100
            value: 90.135
          - type: recall_at_1000
            value: 98.301
          - type: recall_at_3
            value: 42.836999999999996
          - type: recall_at_5
            value: 52.512
      - task:
          type: Classification
        dataset:
          type: mteb/mtop_domain
          name: MTEB MTOPDomainClassification (en)
          config: en
          split: test
          revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
        metrics:
          - type: accuracy
            value: 96.59598723210215
          - type: f1
            value: 96.41913500001952
      - task:
          type: Classification
        dataset:
          type: mteb/mtop_intent
          name: MTEB MTOPIntentClassification (en)
          config: en
          split: test
          revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
        metrics:
          - type: accuracy
            value: 82.89557683538533
          - type: f1
            value: 63.379319722356264
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_intent
          name: MTEB MassiveIntentClassification (en)
          config: en
          split: test
          revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
        metrics:
          - type: accuracy
            value: 78.93745796906524
          - type: f1
            value: 75.71616541785902
      - task:
          type: Classification
        dataset:
          type: mteb/amazon_massive_scenario
          name: MTEB MassiveScenarioClassification (en)
          config: en
          split: test
          revision: 7d571f92784cd94a019292a1f45445077d0ef634
        metrics:
          - type: accuracy
            value: 81.41223940820443
          - type: f1
            value: 81.2877893719078
      - task:
          type: Clustering
        dataset:
          type: mteb/medrxiv-clustering-p2p
          name: MTEB MedrxivClusteringP2P
          config: default
          split: test
          revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
        metrics:
          - type: v_measure
            value: 35.03682528325662
      - task:
          type: Clustering
        dataset:
          type: mteb/medrxiv-clustering-s2s
          name: MTEB MedrxivClusteringS2S
          config: default
          split: test
          revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
        metrics:
          - type: v_measure
            value: 32.942529406124
      - task:
          type: Reranking
        dataset:
          type: mteb/mind_small
          name: MTEB MindSmallReranking
          config: default
          split: test
          revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
        metrics:
          - type: map
            value: 31.459949660460317
          - type: mrr
            value: 32.70509582031616
      - task:
          type: Retrieval
        dataset:
          type: mteb/nfcorpus
          name: MTEB NFCorpus
          config: default
          split: test
          revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
        metrics:
          - type: map_at_1
            value: 6.497
          - type: map_at_10
            value: 13.843
          - type: map_at_100
            value: 17.713
          - type: map_at_1000
            value: 19.241
          - type: map_at_3
            value: 10.096
          - type: map_at_5
            value: 11.85
          - type: mrr_at_1
            value: 48.916
          - type: mrr_at_10
            value: 57.764
          - type: mrr_at_100
            value: 58.251
          - type: mrr_at_1000
            value: 58.282999999999994
          - type: mrr_at_3
            value: 55.623999999999995
          - type: mrr_at_5
            value: 57.018
          - type: ndcg_at_1
            value: 46.594
          - type: ndcg_at_10
            value: 36.945
          - type: ndcg_at_100
            value: 34.06
          - type: ndcg_at_1000
            value: 43.05
          - type: ndcg_at_3
            value: 41.738
          - type: ndcg_at_5
            value: 39.330999999999996
          - type: precision_at_1
            value: 48.916
          - type: precision_at_10
            value: 27.43
          - type: precision_at_100
            value: 8.616
          - type: precision_at_1000
            value: 2.155
          - type: precision_at_3
            value: 39.112
          - type: precision_at_5
            value: 33.808
          - type: recall_at_1
            value: 6.497
          - type: recall_at_10
            value: 18.163
          - type: recall_at_100
            value: 34.566
          - type: recall_at_1000
            value: 67.15
          - type: recall_at_3
            value: 11.100999999999999
          - type: recall_at_5
            value: 14.205000000000002
      - task:
          type: Retrieval
        dataset:
          type: mteb/nq
          name: MTEB NQ
          config: default
          split: test
          revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
        metrics:
          - type: map_at_1
            value: 31.916
          - type: map_at_10
            value: 48.123
          - type: map_at_100
            value: 49.103
          - type: map_at_1000
            value: 49.131
          - type: map_at_3
            value: 43.711
          - type: map_at_5
            value: 46.323
          - type: mrr_at_1
            value: 36.181999999999995
          - type: mrr_at_10
            value: 50.617999999999995
          - type: mrr_at_100
            value: 51.329
          - type: mrr_at_1000
            value: 51.348000000000006
          - type: mrr_at_3
            value: 47.010999999999996
          - type: mrr_at_5
            value: 49.175000000000004
          - type: ndcg_at_1
            value: 36.181999999999995
          - type: ndcg_at_10
            value: 56.077999999999996
          - type: ndcg_at_100
            value: 60.037
          - type: ndcg_at_1000
            value: 60.63499999999999
          - type: ndcg_at_3
            value: 47.859
          - type: ndcg_at_5
            value: 52.178999999999995
          - type: precision_at_1
            value: 36.181999999999995
          - type: precision_at_10
            value: 9.284
          - type: precision_at_100
            value: 1.149
          - type: precision_at_1000
            value: 0.121
          - type: precision_at_3
            value: 22.006999999999998
          - type: precision_at_5
            value: 15.695
          - type: recall_at_1
            value: 31.916
          - type: recall_at_10
            value: 77.771
          - type: recall_at_100
            value: 94.602
          - type: recall_at_1000
            value: 98.967
          - type: recall_at_3
            value: 56.528
          - type: recall_at_5
            value: 66.527
      - task:
          type: Retrieval
        dataset:
          type: mteb/quora
          name: MTEB QuoraRetrieval
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 71.486
          - type: map_at_10
            value: 85.978
          - type: map_at_100
            value: 86.587
          - type: map_at_1000
            value: 86.598
          - type: map_at_3
            value: 83.04899999999999
          - type: map_at_5
            value: 84.857
          - type: mrr_at_1
            value: 82.32000000000001
          - type: mrr_at_10
            value: 88.64
          - type: mrr_at_100
            value: 88.702
          - type: mrr_at_1000
            value: 88.702
          - type: mrr_at_3
            value: 87.735
          - type: mrr_at_5
            value: 88.36
          - type: ndcg_at_1
            value: 82.34
          - type: ndcg_at_10
            value: 89.67
          - type: ndcg_at_100
            value: 90.642
          - type: ndcg_at_1000
            value: 90.688
          - type: ndcg_at_3
            value: 86.932
          - type: ndcg_at_5
            value: 88.408
          - type: precision_at_1
            value: 82.34
          - type: precision_at_10
            value: 13.675999999999998
          - type: precision_at_100
            value: 1.544
          - type: precision_at_1000
            value: 0.157
          - type: precision_at_3
            value: 38.24
          - type: precision_at_5
            value: 25.068
          - type: recall_at_1
            value: 71.486
          - type: recall_at_10
            value: 96.844
          - type: recall_at_100
            value: 99.843
          - type: recall_at_1000
            value: 99.996
          - type: recall_at_3
            value: 88.92099999999999
          - type: recall_at_5
            value: 93.215
      - task:
          type: Clustering
        dataset:
          type: mteb/reddit-clustering
          name: MTEB RedditClustering
          config: default
          split: test
          revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
        metrics:
          - type: v_measure
            value: 59.75758437908334
      - task:
          type: Clustering
        dataset:
          type: mteb/reddit-clustering-p2p
          name: MTEB RedditClusteringP2P
          config: default
          split: test
          revision: 282350215ef01743dc01b456c7f5241fa8937f16
        metrics:
          - type: v_measure
            value: 68.03497914092789
      - task:
          type: Retrieval
        dataset:
          type: mteb/scidocs
          name: MTEB SCIDOCS
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 5.808
          - type: map_at_10
            value: 16.059
          - type: map_at_100
            value: 19.048000000000002
          - type: map_at_1000
            value: 19.43
          - type: map_at_3
            value: 10.953
          - type: map_at_5
            value: 13.363
          - type: mrr_at_1
            value: 28.7
          - type: mrr_at_10
            value: 42.436
          - type: mrr_at_100
            value: 43.599
          - type: mrr_at_1000
            value: 43.62
          - type: mrr_at_3
            value: 38.45
          - type: mrr_at_5
            value: 40.89
          - type: ndcg_at_1
            value: 28.7
          - type: ndcg_at_10
            value: 26.346000000000004
          - type: ndcg_at_100
            value: 36.758
          - type: ndcg_at_1000
            value: 42.113
          - type: ndcg_at_3
            value: 24.254
          - type: ndcg_at_5
            value: 21.506
          - type: precision_at_1
            value: 28.7
          - type: precision_at_10
            value: 13.969999999999999
          - type: precision_at_100
            value: 2.881
          - type: precision_at_1000
            value: 0.414
          - type: precision_at_3
            value: 22.933
          - type: precision_at_5
            value: 19.220000000000002
          - type: recall_at_1
            value: 5.808
          - type: recall_at_10
            value: 28.310000000000002
          - type: recall_at_100
            value: 58.475
          - type: recall_at_1000
            value: 84.072
          - type: recall_at_3
            value: 13.957
          - type: recall_at_5
            value: 19.515
      - task:
          type: STS
        dataset:
          type: mteb/sickr-sts
          name: MTEB SICK-R
          config: default
          split: test
          revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
        metrics:
          - type: cos_sim_pearson
            value: 82.39274129958557
          - type: cos_sim_spearman
            value: 79.78021235170053
          - type: euclidean_pearson
            value: 79.35335401300166
          - type: euclidean_spearman
            value: 79.7271870968275
          - type: manhattan_pearson
            value: 79.35256263340601
          - type: manhattan_spearman
            value: 79.76036386976321
      - task:
          type: STS
        dataset:
          type: mteb/sts12-sts
          name: MTEB STS12
          config: default
          split: test
          revision: a0d554a64d88156834ff5ae9920b964011b16384
        metrics:
          - type: cos_sim_pearson
            value: 83.99130429246708
          - type: cos_sim_spearman
            value: 73.88322811171203
          - type: euclidean_pearson
            value: 80.7569419170376
          - type: euclidean_spearman
            value: 73.82542155409597
          - type: manhattan_pearson
            value: 80.79468183847625
          - type: manhattan_spearman
            value: 73.87027144047784
      - task:
          type: STS
        dataset:
          type: mteb/sts13-sts
          name: MTEB STS13
          config: default
          split: test
          revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
        metrics:
          - type: cos_sim_pearson
            value: 84.88548789489907
          - type: cos_sim_spearman
            value: 85.07535893847255
          - type: euclidean_pearson
            value: 84.6637222061494
          - type: euclidean_spearman
            value: 85.14200626702456
          - type: manhattan_pearson
            value: 84.75327892344734
          - type: manhattan_spearman
            value: 85.24406181838596
      - task:
          type: STS
        dataset:
          type: mteb/sts14-sts
          name: MTEB STS14
          config: default
          split: test
          revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
        metrics:
          - type: cos_sim_pearson
            value: 82.88140039325008
          - type: cos_sim_spearman
            value: 79.61211268112362
          - type: euclidean_pearson
            value: 81.29639728816458
          - type: euclidean_spearman
            value: 79.51284578041442
          - type: manhattan_pearson
            value: 81.3381797137111
          - type: manhattan_spearman
            value: 79.55683684039808
      - task:
          type: STS
        dataset:
          type: mteb/sts15-sts
          name: MTEB STS15
          config: default
          split: test
          revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
        metrics:
          - type: cos_sim_pearson
            value: 85.16716737270485
          - type: cos_sim_spearman
            value: 86.14823841857738
          - type: euclidean_pearson
            value: 85.36325733440725
          - type: euclidean_spearman
            value: 86.04919691402029
          - type: manhattan_pearson
            value: 85.3147511385052
          - type: manhattan_spearman
            value: 86.00676205857764
      - task:
          type: STS
        dataset:
          type: mteb/sts16-sts
          name: MTEB STS16
          config: default
          split: test
          revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
        metrics:
          - type: cos_sim_pearson
            value: 80.34266645861588
          - type: cos_sim_spearman
            value: 81.59914035005882
          - type: euclidean_pearson
            value: 81.15053076245988
          - type: euclidean_spearman
            value: 81.52776915798489
          - type: manhattan_pearson
            value: 81.1819647418673
          - type: manhattan_spearman
            value: 81.57479527353556
      - 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: 89.38263326821439
          - type: cos_sim_spearman
            value: 89.10946308202642
          - type: euclidean_pearson
            value: 88.87831312540068
          - type: euclidean_spearman
            value: 89.03615865973664
          - type: manhattan_pearson
            value: 88.79835539970384
          - type: manhattan_spearman
            value: 88.9766156339753
      - task:
          type: STS
        dataset:
          type: mteb/sts22-crosslingual-sts
          name: MTEB STS22 (en)
          config: en
          split: test
          revision: eea2b4fe26a775864c896887d910b76a8098ad3f
        metrics:
          - type: cos_sim_pearson
            value: 70.1574915581685
          - type: cos_sim_spearman
            value: 70.59144980004054
          - type: euclidean_pearson
            value: 71.43246306918755
          - type: euclidean_spearman
            value: 70.5544189562984
          - type: manhattan_pearson
            value: 71.4071414609503
          - type: manhattan_spearman
            value: 70.31799126163712
      - task:
          type: STS
        dataset:
          type: mteb/stsbenchmark-sts
          name: MTEB STSBenchmark
          config: default
          split: test
          revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
        metrics:
          - type: cos_sim_pearson
            value: 83.36215796635351
          - type: cos_sim_spearman
            value: 83.07276756467208
          - type: euclidean_pearson
            value: 83.06690453635584
          - type: euclidean_spearman
            value: 82.9635366303289
          - type: manhattan_pearson
            value: 83.04994049700815
          - type: manhattan_spearman
            value: 82.98120125356036
      - task:
          type: Reranking
        dataset:
          type: mteb/scidocs-reranking
          name: MTEB SciDocsRR
          config: default
          split: test
          revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
        metrics:
          - type: map
            value: 86.92530011616722
          - type: mrr
            value: 96.21826793395421
      - task:
          type: Retrieval
        dataset:
          type: mteb/scifact
          name: MTEB SciFact
          config: default
          split: test
          revision: 0228b52cf27578f30900b9e5271d331663a030d7
        metrics:
          - type: map_at_1
            value: 65.75
          - type: map_at_10
            value: 77.701
          - type: map_at_100
            value: 78.005
          - type: map_at_1000
            value: 78.006
          - type: map_at_3
            value: 75.48
          - type: map_at_5
            value: 76.927
          - type: mrr_at_1
            value: 68.333
          - type: mrr_at_10
            value: 78.511
          - type: mrr_at_100
            value: 78.704
          - type: mrr_at_1000
            value: 78.704
          - type: mrr_at_3
            value: 77
          - type: mrr_at_5
            value: 78.083
          - type: ndcg_at_1
            value: 68.333
          - type: ndcg_at_10
            value: 82.42699999999999
          - type: ndcg_at_100
            value: 83.486
          - type: ndcg_at_1000
            value: 83.511
          - type: ndcg_at_3
            value: 78.96300000000001
          - type: ndcg_at_5
            value: 81.028
          - type: precision_at_1
            value: 68.333
          - type: precision_at_10
            value: 10.667
          - type: precision_at_100
            value: 1.127
          - type: precision_at_1000
            value: 0.11299999999999999
          - type: precision_at_3
            value: 31.333
          - type: precision_at_5
            value: 20.133000000000003
          - type: recall_at_1
            value: 65.75
          - type: recall_at_10
            value: 95.578
          - type: recall_at_100
            value: 99.833
          - type: recall_at_1000
            value: 100
          - type: recall_at_3
            value: 86.506
          - type: recall_at_5
            value: 91.75
      - task:
          type: PairClassification
        dataset:
          type: mteb/sprintduplicatequestions-pairclassification
          name: MTEB SprintDuplicateQuestions
          config: default
          split: test
          revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
        metrics:
          - type: cos_sim_accuracy
            value: 99.75247524752476
          - type: cos_sim_ap
            value: 94.16065078045173
          - type: cos_sim_f1
            value: 87.22986247544205
          - type: cos_sim_precision
            value: 85.71428571428571
          - type: cos_sim_recall
            value: 88.8
          - type: dot_accuracy
            value: 99.74554455445545
          - type: dot_ap
            value: 93.90633887037264
          - type: dot_f1
            value: 86.9873417721519
          - type: dot_precision
            value: 88.1025641025641
          - type: dot_recall
            value: 85.9
          - type: euclidean_accuracy
            value: 99.75247524752476
          - type: euclidean_ap
            value: 94.17466319018055
          - type: euclidean_f1
            value: 87.3405299313052
          - type: euclidean_precision
            value: 85.74181117533719
          - type: euclidean_recall
            value: 89
          - type: manhattan_accuracy
            value: 99.75445544554455
          - type: manhattan_ap
            value: 94.27688371923577
          - type: manhattan_f1
            value: 87.74002954209749
          - type: manhattan_precision
            value: 86.42095053346266
          - type: manhattan_recall
            value: 89.1
          - type: max_accuracy
            value: 99.75445544554455
          - type: max_ap
            value: 94.27688371923577
          - type: max_f1
            value: 87.74002954209749
      - task:
          type: Clustering
        dataset:
          type: mteb/stackexchange-clustering
          name: MTEB StackExchangeClustering
          config: default
          split: test
          revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
        metrics:
          - type: v_measure
            value: 71.26500637517056
      - task:
          type: Clustering
        dataset:
          type: mteb/stackexchange-clustering-p2p
          name: MTEB StackExchangeClusteringP2P
          config: default
          split: test
          revision: 815ca46b2622cec33ccafc3735d572c266efdb44
        metrics:
          - type: v_measure
            value: 39.17507906280528
      - task:
          type: Reranking
        dataset:
          type: mteb/stackoverflowdupquestions-reranking
          name: MTEB StackOverflowDupQuestions
          config: default
          split: test
          revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
        metrics:
          - type: map
            value: 52.4848744828509
          - type: mrr
            value: 53.33678168236992
      - task:
          type: Summarization
        dataset:
          type: mteb/summeval
          name: MTEB SummEval
          config: default
          split: test
          revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
        metrics:
          - type: cos_sim_pearson
            value: 30.599864323827887
          - type: cos_sim_spearman
            value: 30.91116204665598
          - type: dot_pearson
            value: 30.82637894269936
          - type: dot_spearman
            value: 30.957573868416066
      - task:
          type: Retrieval
        dataset:
          type: mteb/trec-covid
          name: MTEB TRECCOVID
          config: default
          split: test
          revision: None
        metrics:
          - type: map_at_1
            value: 0.23600000000000002
          - type: map_at_10
            value: 1.892
          - type: map_at_100
            value: 11.586
          - type: map_at_1000
            value: 27.761999999999997
          - type: map_at_3
            value: 0.653
          - type: map_at_5
            value: 1.028
          - type: mrr_at_1
            value: 88
          - type: mrr_at_10
            value: 94
          - type: mrr_at_100
            value: 94
          - type: mrr_at_1000
            value: 94
          - type: mrr_at_3
            value: 94
          - type: mrr_at_5
            value: 94
          - type: ndcg_at_1
            value: 82
          - type: ndcg_at_10
            value: 77.48899999999999
          - type: ndcg_at_100
            value: 60.141
          - type: ndcg_at_1000
            value: 54.228
          - type: ndcg_at_3
            value: 82.358
          - type: ndcg_at_5
            value: 80.449
          - type: precision_at_1
            value: 88
          - type: precision_at_10
            value: 82.19999999999999
          - type: precision_at_100
            value: 61.760000000000005
          - type: precision_at_1000
            value: 23.684
          - type: precision_at_3
            value: 88
          - type: precision_at_5
            value: 85.6
          - type: recall_at_1
            value: 0.23600000000000002
          - type: recall_at_10
            value: 2.117
          - type: recall_at_100
            value: 14.985000000000001
          - type: recall_at_1000
            value: 51.107
          - type: recall_at_3
            value: 0.688
          - type: recall_at_5
            value: 1.1039999999999999
      - task:
          type: Retrieval
        dataset:
          type: mteb/touche2020
          name: MTEB Touche2020
          config: default
          split: test
          revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
        metrics:
          - type: map_at_1
            value: 2.3040000000000003
          - type: map_at_10
            value: 9.025
          - type: map_at_100
            value: 15.312999999999999
          - type: map_at_1000
            value: 16.954
          - type: map_at_3
            value: 4.981
          - type: map_at_5
            value: 6.32
          - type: mrr_at_1
            value: 24.490000000000002
          - type: mrr_at_10
            value: 39.835
          - type: mrr_at_100
            value: 40.8
          - type: mrr_at_1000
            value: 40.8
          - type: mrr_at_3
            value: 35.034
          - type: mrr_at_5
            value: 37.687
          - type: ndcg_at_1
            value: 22.448999999999998
          - type: ndcg_at_10
            value: 22.545
          - type: ndcg_at_100
            value: 35.931999999999995
          - type: ndcg_at_1000
            value: 47.665
          - type: ndcg_at_3
            value: 23.311
          - type: ndcg_at_5
            value: 22.421
          - type: precision_at_1
            value: 24.490000000000002
          - type: precision_at_10
            value: 20.408
          - type: precision_at_100
            value: 7.815999999999999
          - type: precision_at_1000
            value: 1.553
          - type: precision_at_3
            value: 25.169999999999998
          - type: precision_at_5
            value: 23.265
          - type: recall_at_1
            value: 2.3040000000000003
          - type: recall_at_10
            value: 15.693999999999999
          - type: recall_at_100
            value: 48.917
          - type: recall_at_1000
            value: 84.964
          - type: recall_at_3
            value: 6.026
          - type: recall_at_5
            value: 9.066
      - task:
          type: Classification
        dataset:
          type: mteb/toxic_conversations_50k
          name: MTEB ToxicConversationsClassification
          config: default
          split: test
          revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
        metrics:
          - type: accuracy
            value: 82.6074
          - type: ap
            value: 23.187467098602013
          - type: f1
            value: 65.36829506379657
      - task:
          type: Classification
        dataset:
          type: mteb/tweet_sentiment_extraction
          name: MTEB TweetSentimentExtractionClassification
          config: default
          split: test
          revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
        metrics:
          - type: accuracy
            value: 63.16355404640635
          - type: f1
            value: 63.534725639863346
      - task:
          type: Clustering
        dataset:
          type: mteb/twentynewsgroups-clustering
          name: MTEB TwentyNewsgroupsClustering
          config: default
          split: test
          revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
        metrics:
          - type: v_measure
            value: 50.91004094411276
      - task:
          type: PairClassification
        dataset:
          type: mteb/twittersemeval2015-pairclassification
          name: MTEB TwitterSemEval2015
          config: default
          split: test
          revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
        metrics:
          - type: cos_sim_accuracy
            value: 86.55301901412649
          - type: cos_sim_ap
            value: 75.25312618556728
          - type: cos_sim_f1
            value: 68.76561719140429
          - type: cos_sim_precision
            value: 65.3061224489796
          - type: cos_sim_recall
            value: 72.61213720316623
          - type: dot_accuracy
            value: 86.29671574178936
          - type: dot_ap
            value: 75.11910195501207
          - type: dot_f1
            value: 68.44048376830045
          - type: dot_precision
            value: 66.12546125461255
          - type: dot_recall
            value: 70.92348284960423
          - type: euclidean_accuracy
            value: 86.5828217202122
          - type: euclidean_ap
            value: 75.22986344900924
          - type: euclidean_f1
            value: 68.81267797449549
          - type: euclidean_precision
            value: 64.8238861674831
          - type: euclidean_recall
            value: 73.3245382585752
          - type: manhattan_accuracy
            value: 86.61262442629791
          - type: manhattan_ap
            value: 75.24401608557328
          - type: manhattan_f1
            value: 68.80473982483257
          - type: manhattan_precision
            value: 67.21187720181177
          - type: manhattan_recall
            value: 70.47493403693932
          - type: max_accuracy
            value: 86.61262442629791
          - type: max_ap
            value: 75.25312618556728
          - type: max_f1
            value: 68.81267797449549
      - task:
          type: PairClassification
        dataset:
          type: mteb/twitterurlcorpus-pairclassification
          name: MTEB TwitterURLCorpus
          config: default
          split: test
          revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
        metrics:
          - type: cos_sim_accuracy
            value: 88.10688089416696
          - type: cos_sim_ap
            value: 84.17862178779863
          - type: cos_sim_f1
            value: 76.17305208781748
          - type: cos_sim_precision
            value: 71.31246641590543
          - type: cos_sim_recall
            value: 81.74468740375731
          - type: dot_accuracy
            value: 88.1844995536927
          - type: dot_ap
            value: 84.33816725235876
          - type: dot_f1
            value: 76.43554032918746
          - type: dot_precision
            value: 74.01557767200346
          - type: dot_recall
            value: 79.0190945488143
          - type: euclidean_accuracy
            value: 88.07001203089223
          - type: euclidean_ap
            value: 84.12267000814985
          - type: euclidean_f1
            value: 76.12232600180778
          - type: euclidean_precision
            value: 74.50604541433205
          - type: euclidean_recall
            value: 77.81028641823221
          - type: manhattan_accuracy
            value: 88.06419063142779
          - type: manhattan_ap
            value: 84.11648917164187
          - type: manhattan_f1
            value: 76.20579953925474
          - type: manhattan_precision
            value: 72.56772755762935
          - type: manhattan_recall
            value: 80.22790267939637
          - type: max_accuracy
            value: 88.1844995536927
          - type: max_ap
            value: 84.33816725235876
          - type: max_f1
            value: 76.43554032918746

gte-large-en-v1.5

We introduce gte-v1.5 series, upgraded gte embeddings that support the context length of up to 8192, while further enhancing model performance. The models are built upon the transformer++ encoder backbone (BERT + RoPE + GLU).

The gte-v1.5 series achieve state-of-the-art scores on the MTEB benchmark within the same model size category and prodvide competitive on the LoCo long-context retrieval tests (refer to Evaluation).

We also present the gte-Qwen1.5-7B-instruct, a SOTA instruction-tuned multi-lingual embedding model that ranked 2nd in MTEB and 1st in C-MTEB.

Model list

Models Language Model Size Max Seq. Length Dimension MTEB-en LoCo
gte-Qwen1.5-7B-instruct Multiple 7720 32768 4096 67.34 87.57
gte-large-en-v1.5 English 434 8192 1024 65.39 86.71
gte-base-en-v1.5 English 137 8192 768 64.11 87.44

How to Get Started with the Model

Use the code below to get started with the model.

# Requires transformers>=4.36.0

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

input_texts = [
    "what is the capital of China?",
    "how to implement quick sort in python?",
    "Beijing",
    "sorting algorithms"
]

model_path = 'Alibaba-NLP/gte-large-en-v1.5'
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModel.from_pretrained(model_path, trust_remote_code=True)

# Tokenize the input texts
batch_dict = tokenizer(input_texts, max_length=8192, padding=True, truncation=True, return_tensors='pt')

outputs = model(**batch_dict)
embeddings = outputs.last_hidden_state[:, 0]
 
# (Optionally) normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:1] @ embeddings[1:].T) * 100
print(scores.tolist())

It is recommended to install xformers and enable unpadding for acceleration, refer to enable-unpadding-and-xformers.

Use with sentence-transformers:

# Requires sentence_transformers>=2.7.0

from sentence_transformers import SentenceTransformer
from sentence_transformers.util import cos_sim

sentences = ['That is a happy person', 'That is a very happy person']

model = SentenceTransformer('Alibaba-NLP/gte-large-en-v1.5', trust_remote_code=True)
embeddings = model.encode(sentences)
print(cos_sim(embeddings[0], embeddings[1]))

Use with transformers.js:

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

// Create feature extraction pipeline
const extractor = await pipeline('feature-extraction', 'Alibaba-NLP/gte-large-en-v1.5', {
    quantized: false, // Comment out this line to use the quantized version
});

// Generate sentence embeddings
const sentences = [
    "what is the capital of China?",
    "how to implement quick sort in python?",
    "Beijing",
    "sorting algorithms"
]
const output = await extractor(sentences, { normalize: true, pooling: 'cls' });

// Compute similarity scores
const [source_embeddings, ...document_embeddings ] = output.tolist();
const similarities = document_embeddings.map(x => 100 * dot(source_embeddings, x));
console.log(similarities); // [41.86354093370361, 77.07076371259589, 37.02981979677899]

Training Details

Training Data

  • Masked language modeling (MLM): c4-en
  • Weak-supervised contrastive pre-training (CPT): GTE pre-training data
  • Supervised contrastive fine-tuning: GTE fine-tuning data

Training Procedure

To enable the backbone model to support a context length of 8192, we adopted a multi-stage training strategy. The model first undergoes preliminary MLM pre-training on shorter lengths. And then, we resample the data, reducing the proportion of short texts, and continue the MLM pre-training.

The entire training process is as follows:

  • MLM-512: lr 2e-4, mlm_probability 0.3, batch_size 4096, num_steps 300000, rope_base 10000
  • MLM-2048: lr 5e-5, mlm_probability 0.3, batch_size 4096, num_steps 30000, rope_base 10000
  • MLM-8192: lr 5e-5, mlm_probability 0.3, batch_size 1024, num_steps 30000, rope_base 160000
  • CPT: max_len 512, lr 5e-5, batch_size 28672, num_steps 100000
  • Fine-tuning: TODO

Evaluation

MTEB

The results of other models are retrieved from MTEB leaderboard.

The gte evaluation setting: mteb==1.2.0, fp16 auto mix precision, max_length=8192, and set ntk scaling factor to 2 (equivalent to rope_base * 2).

Model Name Param Size (M) Dimension Sequence Length Average (56) Class. (12) Clust. (11) Pair Class. (3) Reran. (4) Retr. (15) STS (10) Summ. (1)
gte-large-en-v1.5 409 1024 8192 65.39 77.75 47.95 84.63 58.50 57.91 81.43 30.91
mxbai-embed-large-v1 335 1024 512 64.68 75.64 46.71 87.2 60.11 54.39 85 32.71
multilingual-e5-large-instruct 560 1024 514 64.41 77.56 47.1 86.19 58.58 52.47 84.78 30.39
bge-large-en-v1.5 335 1024 512 64.23 75.97 46.08 87.12 60.03 54.29 83.11 31.61
gte-base-en-v1.5 137 768 8192 64.11 77.17 46.82 85.33 57.66 54.09 81.97 31.17
bge-base-en-v1.5 109 768 512 63.55 75.53 45.77 86.55 58.86 53.25 82.4 31.07

LoCo

Model Name Dimension Sequence Length Average (5) QsmsumRetrieval SummScreenRetrieval QasperAbastractRetrieval QasperTitleRetrieval GovReportRetrieval
gte-qwen1.5-7b 4096 32768 87.57 49.37 93.10 99.67 97.54 98.21
gte-large-v1.5 1024 8192 86.71 44.55 92.61 99.82 97.81 98.74
gte-base-v1.5 768 8192 87.44 49.91 91.78 99.82 97.13 98.58

Citation

If you find our paper or models helpful, please consider citing them as follows:

@article{zhang2024mgte,
  title={mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval},
  author={Zhang, Xin and Zhang, Yanzhao and Long, Dingkun and Xie, Wen and Dai, Ziqi and Tang, Jialong and Lin, Huan and Yang, Baosong and Xie, Pengjun and Huang, Fei and others},
  journal={arXiv preprint arXiv:2407.19669},
  year={2024}
}

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