Gte Qwen2 7B Instruct GGUF

tags:

• mteb
• sentence-transformers
• transformers
• Qwen2
• sentence-similarity license: apache-2.0 model-index:
• name: gte-qwen2-7B-instruct results:
  • task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics:
    • type: accuracy value: 91.31343283582089
    • type: ap value: 67.64251402604096
    • type: f1 value: 87.53372530755692
  • task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics:
    • type: accuracy value: 97.497825
    • type: ap value: 96.30329547047529
    • type: f1 value: 97.49769793778039
  • task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics:
    • type: accuracy value: 62.564
    • type: f1 value: 60.975777935041066
  • task: type: Retrieval dataset: type: mteb/arguana name: MTEB ArguAna config: default split: test revision: c22ab2a51041ffd869aaddef7af8d8215647e41a metrics:
    • type: map_at_1 value: 36.486000000000004
    • type: map_at_10 value: 54.842
    • type: map_at_100 value: 55.206999999999994
    • type: map_at_1000 value: 55.206999999999994
    • type: map_at_3 value: 49.893
    • type: map_at_5 value: 53.105000000000004
    • type: mrr_at_1 value: 37.34
    • type: mrr_at_10 value: 55.143
    • type: mrr_at_100 value: 55.509
    • type: mrr_at_1000 value: 55.509
    • type: mrr_at_3 value: 50.212999999999994
    • type: mrr_at_5 value: 53.432
    • type: ndcg_at_1 value: 36.486000000000004
    • type: ndcg_at_10 value: 64.273
    • type: ndcg_at_100 value: 65.66199999999999
    • type: ndcg_at_1000 value: 65.66199999999999
    • type: ndcg_at_3 value: 54.352999999999994
    • type: ndcg_at_5 value: 60.131
    • type: precision_at_1 value: 36.486000000000004
    • type: precision_at_10 value: 9.395000000000001
    • type: precision_at_100 value: 0.996
    • type: precision_at_1000 value: 0.1
    • type: precision_at_3 value: 22.428
    • type: precision_at_5 value: 16.259
    • type: recall_at_1 value: 36.486000000000004
    • type: recall_at_10 value: 93.95400000000001
    • type: recall_at_100 value: 99.644
    • type: recall_at_1000 value: 99.644
    • type: recall_at_3 value: 67.283
    • type: recall_at_5 value: 81.294
  • task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics:
    • type: v_measure value: 56.461169803700564
  • task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics:
    • type: v_measure value: 51.73600434466286
  • task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics:
    • type: map value: 67.57827065898053
    • type: mrr value: 79.08136569493911
  • task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics:
    • type: cos_sim_pearson value: 83.53324575999243
    • type: cos_sim_spearman value: 81.37173362822374
    • type: euclidean_pearson value: 82.19243335103444
    • type: euclidean_spearman value: 81.33679307304334
    • type: manhattan_pearson value: 82.38752665975699
    • type: manhattan_spearman value: 81.31510583189689
  • task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics:
    • type: accuracy value: 87.56818181818181
    • type: f1 value: 87.25826722019875
  • task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics:
    • type: v_measure value: 50.09239610327673
  • task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics:
    • type: v_measure value: 46.64733054606282
  • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: f46a197baaae43b4f621051089b82a364682dfeb metrics:
    • type: map_at_1 value: 33.997
    • type: map_at_10 value: 48.176
    • type: map_at_100 value: 49.82
    • type: map_at_1000 value: 49.924
    • type: map_at_3 value: 43.626
    • type: map_at_5 value: 46.275
    • type: mrr_at_1 value: 42.059999999999995
    • type: mrr_at_10 value: 53.726
    • type: mrr_at_100 value: 54.398
    • type: mrr_at_1000 value: 54.416
    • type: mrr_at_3 value: 50.714999999999996
    • type: mrr_at_5 value: 52.639
    • type: ndcg_at_1 value: 42.059999999999995
    • type: ndcg_at_10 value: 55.574999999999996
    • type: ndcg_at_100 value: 60.744
    • type: ndcg_at_1000 value: 61.85699999999999
    • type: ndcg_at_3 value: 49.363
    • type: ndcg_at_5 value: 52.44
    • type: precision_at_1 value: 42.059999999999995
    • type: precision_at_10 value: 11.101999999999999
    • type: precision_at_100 value: 1.73
    • type: precision_at_1000 value: 0.218
    • type: precision_at_3 value: 24.464
    • type: precision_at_5 value: 18.026
    • type: recall_at_1 value: 33.997
    • type: recall_at_10 value: 70.35900000000001
    • type: recall_at_100 value: 91.642
    • type: recall_at_1000 value: 97.977
    • type: recall_at_3 value: 52.76
    • type: recall_at_5 value: 61.148
  • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 metrics:
    • type: map_at_1 value: 35.884
    • type: map_at_10 value: 48.14
    • type: map_at_100 value: 49.5
    • type: map_at_1000 value: 49.63
    • type: map_at_3 value: 44.646
    • type: map_at_5 value: 46.617999999999995
    • type: mrr_at_1 value: 44.458999999999996
    • type: mrr_at_10 value: 53.751000000000005
    • type: mrr_at_100 value: 54.37800000000001
    • type: mrr_at_1000 value: 54.415
    • type: mrr_at_3 value: 51.815
    • type: mrr_at_5 value: 52.882
    • type: ndcg_at_1 value: 44.458999999999996
    • type: ndcg_at_10 value: 54.157
    • type: ndcg_at_100 value: 58.362
    • type: ndcg_at_1000 value: 60.178
    • type: ndcg_at_3 value: 49.661
    • type: ndcg_at_5 value: 51.74999999999999
    • type: precision_at_1 value: 44.458999999999996
    • type: precision_at_10 value: 10.248
    • type: precision_at_100 value: 1.5890000000000002
    • type: precision_at_1000 value: 0.207
    • type: precision_at_3 value: 23.928
    • type: precision_at_5 value: 16.878999999999998
    • type: recall_at_1 value: 35.884
    • type: recall_at_10 value: 64.798
    • type: recall_at_100 value: 82.345
    • type: recall_at_1000 value: 93.267
    • type: recall_at_3 value: 51.847
    • type: recall_at_5 value: 57.601
  • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: 4885aa143210c98657558c04aaf3dc47cfb54340 metrics:
    • type: map_at_1 value: 39.383
    • type: map_at_10 value: 53.714
    • type: map_at_100 value: 54.838
    • type: map_at_1000 value: 54.87800000000001
    • type: map_at_3 value: 50.114999999999995
    • type: map_at_5 value: 52.153000000000006
    • type: mrr_at_1 value: 45.016
    • type: mrr_at_10 value: 56.732000000000006
    • type: mrr_at_100 value: 57.411
    • type: mrr_at_1000 value: 57.431
    • type: mrr_at_3 value: 54.044000000000004
    • type: mrr_at_5 value: 55.639
    • type: ndcg_at_1 value: 45.016
    • type: ndcg_at_10 value: 60.228
    • type: ndcg_at_100 value: 64.277
    • type: ndcg_at_1000 value: 65.07
    • type: ndcg_at_3 value: 54.124
    • type: ndcg_at_5 value: 57.147000000000006
    • type: precision_at_1 value: 45.016
    • type: precision_at_10 value: 9.937
    • type: precision_at_100 value: 1.288
    • type: precision_at_1000 value: 0.13899999999999998
    • type: precision_at_3 value: 24.471999999999998
    • type: precision_at_5 value: 16.991
    • type: recall_at_1 value: 39.383
    • type: recall_at_10 value: 76.175
    • type: recall_at_100 value: 93.02
    • type: recall_at_1000 value: 98.60900000000001
    • type: recall_at_3 value: 60.265
    • type: recall_at_5 value: 67.46600000000001
  • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: 5003b3064772da1887988e05400cf3806fe491f2 metrics:
    • type: map_at_1 value: 27.426000000000002
    • type: map_at_10 value: 37.397000000000006
    • type: map_at_100 value: 38.61
    • type: map_at_1000 value: 38.678000000000004
    • type: map_at_3 value: 34.150999999999996
    • type: map_at_5 value: 36.137
    • type: mrr_at_1 value: 29.944
    • type: mrr_at_10 value: 39.654
    • type: mrr_at_100 value: 40.638000000000005
    • type: mrr_at_1000 value: 40.691
    • type: mrr_at_3 value: 36.817
    • type: mrr_at_5 value: 38.524
    • type: ndcg_at_1 value: 29.944
    • type: ndcg_at_10 value: 43.094
    • type: ndcg_at_100 value: 48.789
    • type: ndcg_at_1000 value: 50.339999999999996
    • type: ndcg_at_3 value: 36.984
    • type: ndcg_at_5 value: 40.248
    • type: precision_at_1 value: 29.944
    • type: precision_at_10 value: 6.78
    • type: precision_at_100 value: 1.024
    • type: precision_at_1000 value: 0.11800000000000001
    • type: precision_at_3 value: 15.895000000000001
    • type: precision_at_5 value: 11.39
    • type: recall_at_1 value: 27.426000000000002
    • type: recall_at_10 value: 58.464000000000006
    • type: recall_at_100 value: 84.193
    • type: recall_at_1000 value: 95.52000000000001
    • type: recall_at_3 value: 42.172
    • type: recall_at_5 value: 50.101
  • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: 90fceea13679c63fe563ded68f3b6f06e50061de metrics:
    • type: map_at_1 value: 19.721
    • type: map_at_10 value: 31.604
    • type: map_at_100 value: 32.972
    • type: map_at_1000 value: 33.077
    • type: map_at_3 value: 27.218999999999998
    • type: map_at_5 value: 29.53
    • type: mrr_at_1 value: 25.0
    • type: mrr_at_10 value: 35.843
    • type: mrr_at_100 value: 36.785000000000004
    • type: mrr_at_1000 value: 36.842000000000006
    • type: mrr_at_3 value: 32.193
    • type: mrr_at_5 value: 34.264
    • type: ndcg_at_1 value: 25.0
    • type: ndcg_at_10 value: 38.606
    • type: ndcg_at_100 value: 44.272
    • type: ndcg_at_1000 value: 46.527
    • type: ndcg_at_3 value: 30.985000000000003
    • type: ndcg_at_5 value: 34.43
    • type: precision_at_1 value: 25.0
    • type: precision_at_10 value: 7.811
    • type: precision_at_100 value: 1.203
    • type: precision_at_1000 value: 0.15
    • type: precision_at_3 value: 15.423
    • type: precision_at_5 value: 11.791
    • type: recall_at_1 value: 19.721
    • type: recall_at_10 value: 55.625
    • type: recall_at_100 value: 79.34400000000001
    • type: recall_at_1000 value: 95.208
    • type: recall_at_3 value: 35.19
    • type: recall_at_5 value: 43.626
  • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 metrics:
    • type: map_at_1 value: 33.784
    • type: map_at_10 value: 47.522
    • type: map_at_100 value: 48.949999999999996
    • type: map_at_1000 value: 49.038
    • type: map_at_3 value: 43.284
    • type: map_at_5 value: 45.629
    • type: mrr_at_1 value: 41.482
    • type: mrr_at_10 value: 52.830999999999996
    • type: mrr_at_100 value: 53.559999999999995
    • type: mrr_at_1000 value: 53.588
    • type: mrr_at_3 value: 50.016000000000005
    • type: mrr_at_5 value: 51.614000000000004
    • type: ndcg_at_1 value: 41.482
    • type: ndcg_at_10 value: 54.569
    • type: ndcg_at_100 value: 59.675999999999995
    • type: ndcg_at_1000 value: 60.989000000000004
    • type: ndcg_at_3 value: 48.187000000000005
    • type: ndcg_at_5 value: 51.183
    • type: precision_at_1 value: 41.482
    • type: precision_at_10 value: 10.221
    • type: precision_at_100 value: 1.486
    • type: precision_at_1000 value: 0.17500000000000002
    • type: precision_at_3 value: 23.548
    • type: precision_at_5 value: 16.805
    • type: recall_at_1 value: 33.784
    • type: recall_at_10 value: 69.798
    • type: recall_at_100 value: 90.098
    • type: recall_at_1000 value: 98.176
    • type: recall_at_3 value: 52.127
    • type: recall_at_5 value: 59.861
  • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 metrics:
    • type: map_at_1 value: 28.038999999999998
    • type: map_at_10 value: 41.904
    • type: map_at_100 value: 43.36
    • type: map_at_1000 value: 43.453
    • type: map_at_3 value: 37.785999999999994
    • type: map_at_5 value: 40.105000000000004
    • type: mrr_at_1 value: 35.046
    • type: mrr_at_10 value: 46.926
    • type: mrr_at_100 value: 47.815000000000005
    • type: mrr_at_1000 value: 47.849000000000004
    • type: mrr_at_3 value: 44.273
    • type: mrr_at_5 value: 45.774
    • type: ndcg_at_1 value: 35.046
    • type: ndcg_at_10 value: 48.937000000000005
    • type: ndcg_at_100 value: 54.544000000000004
    • type: ndcg_at_1000 value: 56.069
    • type: ndcg_at_3 value: 42.858000000000004
    • type: ndcg_at_5 value: 45.644
    • type: precision_at_1 value: 35.046
    • type: precision_at_10 value: 9.452
    • type: precision_at_100 value: 1.429
    • type: precision_at_1000 value: 0.173
    • type: precision_at_3 value: 21.346999999999998
    • type: precision_at_5 value: 15.342
    • type: recall_at_1 value: 28.038999999999998
    • type: recall_at_10 value: 64.59700000000001
    • type: recall_at_100 value: 87.735
    • type: recall_at_1000 value: 97.41300000000001
    • type: recall_at_3 value: 47.368
    • type: recall_at_5 value: 54.93900000000001
  • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics:
    • type: map_at_1 value: 28.17291666666667
    • type: map_at_10 value: 40.025749999999995
    • type: map_at_100 value: 41.39208333333333
    • type: map_at_1000 value: 41.499249999999996
    • type: map_at_3 value: 36.347
    • type: map_at_5 value: 38.41391666666667
    • type: mrr_at_1 value: 33.65925
    • type: mrr_at_10 value: 44.085499999999996
    • type: mrr_at_100 value: 44.94116666666667
    • type: mrr_at_1000 value: 44.9855
    • type: mrr_at_3 value: 41.2815
    • type: mrr_at_5 value: 42.91491666666666
    • type: ndcg_at_1 value: 33.65925
    • type: ndcg_at_10 value: 46.430833333333325
    • type: ndcg_at_100 value: 51.761
    • type: ndcg_at_1000 value: 53.50899999999999
    • type: ndcg_at_3 value: 40.45133333333333
    • type: ndcg_at_5 value: 43.31483333333334
    • type: precision_at_1 value: 33.65925
    • type: precision_at_10 value: 8.4995
    • type: precision_at_100 value: 1.3210000000000004
    • type: precision_at_1000 value: 0.16591666666666666
    • type: precision_at_3 value: 19.165083333333335
    • type: precision_at_5 value: 13.81816666666667
    • type: recall_at_1 value: 28.17291666666667
    • type: recall_at_10 value: 61.12624999999999
    • type: recall_at_100 value: 83.97266666666667
    • type: recall_at_1000 value: 95.66550000000001
    • type: recall_at_3 value: 44.661249999999995
    • type: recall_at_5 value: 51.983333333333334
  • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a metrics:
    • type: map_at_1 value: 24.681
    • type: map_at_10 value: 34.892
    • type: map_at_100 value: 35.996
    • type: map_at_1000 value: 36.083
    • type: map_at_3 value: 31.491999999999997
    • type: map_at_5 value: 33.632
    • type: mrr_at_1 value: 28.528
    • type: mrr_at_10 value: 37.694
    • type: mrr_at_100 value: 38.613
    • type: mrr_at_1000 value: 38.668
    • type: mrr_at_3 value: 34.714
    • type: mrr_at_5 value: 36.616
    • type: ndcg_at_1 value: 28.528
    • type: ndcg_at_10 value: 40.703
    • type: ndcg_at_100 value: 45.993
    • type: ndcg_at_1000 value: 47.847
    • type: ndcg_at_3 value: 34.622
    • type: ndcg_at_5 value: 38.035999999999994
    • type: precision_at_1 value: 28.528
    • type: precision_at_10 value: 6.902
    • type: precision_at_100 value: 1.0370000000000001
    • type: precision_at_1000 value: 0.126
    • type: precision_at_3 value: 15.798000000000002
    • type: precision_at_5 value: 11.655999999999999
    • type: recall_at_1 value: 24.681
    • type: recall_at_10 value: 55.81
    • type: recall_at_100 value: 79.785
    • type: recall_at_1000 value: 92.959
    • type: recall_at_3 value: 39.074
    • type: recall_at_5 value: 47.568
  • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: 46989137a86843e03a6195de44b09deda022eec7 metrics:
    • type: map_at_1 value: 18.627
    • type: map_at_10 value: 27.872000000000003
    • type: map_at_100 value: 29.237999999999996
    • type: map_at_1000 value: 29.363
    • type: map_at_3 value: 24.751
    • type: map_at_5 value: 26.521
    • type: mrr_at_1 value: 23.021
    • type: mrr_at_10 value: 31.924000000000003
    • type: mrr_at_100 value: 32.922000000000004
    • type: mrr_at_1000 value: 32.988
    • type: mrr_at_3 value: 29.192
    • type: mrr_at_5 value: 30.798
    • type: ndcg_at_1 value: 23.021
    • type: ndcg_at_10 value: 33.535
    • type: ndcg_at_100 value: 39.732
    • type: ndcg_at_1000 value: 42.201
    • type: ndcg_at_3 value: 28.153
    • type: ndcg_at_5 value: 30.746000000000002
    • type: precision_at_1 value: 23.021
    • type: precision_at_10 value: 6.459
    • type: precision_at_100 value: 1.1320000000000001
    • type: precision_at_1000 value: 0.153
    • type: precision_at_3 value: 13.719000000000001
    • type: precision_at_5 value: 10.193000000000001
    • type: recall_at_1 value: 18.627
    • type: recall_at_10 value: 46.463
    • type: recall_at_100 value: 74.226
    • type: recall_at_1000 value: 91.28500000000001
    • type: recall_at_3 value: 31.357000000000003
    • type: recall_at_5 value: 38.067
  • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 metrics:
    • type: map_at_1 value: 31.457
    • type: map_at_10 value: 42.888
    • type: map_at_100 value: 44.24
    • type: map_at_1000 value: 44.327
    • type: map_at_3 value: 39.588
    • type: map_at_5 value: 41.423
    • type: mrr_at_1 value: 37.126999999999995
    • type: mrr_at_10 value: 47.083000000000006
    • type: mrr_at_100 value: 47.997
    • type: mrr_at_1000 value: 48.044
    • type: mrr_at_3 value: 44.574000000000005
    • type: mrr_at_5 value: 46.202
    • type: ndcg_at_1 value: 37.126999999999995
    • type: ndcg_at_10 value: 48.833
    • type: ndcg_at_100 value: 54.327000000000005
    • type: ndcg_at_1000 value: 56.011
    • type: ndcg_at_3 value: 43.541999999999994
    • type: ndcg_at_5 value: 46.127
    • type: precision_at_1 value: 37.126999999999995
    • type: precision_at_10 value: 8.376999999999999
    • type: precision_at_100 value: 1.2309999999999999
    • type: precision_at_1000 value: 0.146
    • type: precision_at_3 value: 20.211000000000002
    • type: precision_at_5 value: 14.16
    • type: recall_at_1 value: 31.457
    • type: recall_at_10 value: 62.369
    • type: recall_at_100 value: 85.444
    • type: recall_at_1000 value: 96.65599999999999
    • type: recall_at_3 value: 47.961
    • type: recall_at_5 value: 54.676
  • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: 160c094312a0e1facb97e55eeddb698c0abe3571 metrics:
    • type: map_at_1 value: 27.139999999999997
    • type: map_at_10 value: 38.801
    • type: map_at_100 value: 40.549
    • type: map_at_1000 value: 40.802
    • type: map_at_3 value: 35.05
    • type: map_at_5 value: 36.884
    • type: mrr_at_1 value: 33.004
    • type: mrr_at_10 value: 43.864
    • type: mrr_at_100 value: 44.667
    • type: mrr_at_1000 value: 44.717
    • type: mrr_at_3 value: 40.777
    • type: mrr_at_5 value: 42.319
    • type: ndcg_at_1 value: 33.004
    • type: ndcg_at_10 value: 46.022
    • type: ndcg_at_100 value: 51.542
    • type: ndcg_at_1000 value: 53.742000000000004
    • type: ndcg_at_3 value: 39.795
    • type: ndcg_at_5 value: 42.272
    • type: precision_at_1 value: 33.004
    • type: precision_at_10 value: 9.012
    • type: precision_at_100 value: 1.7770000000000001
    • type: precision_at_1000 value: 0.26
    • type: precision_at_3 value: 19.038
    • type: precision_at_5 value: 13.675999999999998
    • type: recall_at_1 value: 27.139999999999997
    • type: recall_at_10 value: 60.961
    • type: recall_at_100 value: 84.451
    • type: recall_at_1000 value: 98.113
    • type: recall_at_3 value: 43.001
    • type: recall_at_5 value: 49.896
  • task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 metrics:
    • type: map_at_1 value: 17.936
    • type: map_at_10 value: 27.399
    • type: map_at_100 value: 28.632
    • type: map_at_1000 value: 28.738000000000003
    • type: map_at_3 value: 24.456
    • type: map_at_5 value: 26.06
    • type: mrr_at_1 value: 19.224
    • type: mrr_at_10 value: 28.998
    • type: mrr_at_100 value: 30.11
    • type: mrr_at_1000 value: 30.177
    • type: mrr_at_3 value: 26.247999999999998
    • type: mrr_at_5 value: 27.708
    • type: ndcg_at_1 value: 19.224
    • type: ndcg_at_10 value: 32.911
    • type: ndcg_at_100 value: 38.873999999999995
    • type: ndcg_at_1000 value: 41.277
    • type: ndcg_at_3 value: 27.142
    • type: ndcg_at_5 value: 29.755
    • type: precision_at_1 value: 19.224
    • type: precision_at_10 value: 5.6930000000000005
    • type: precision_at_100 value: 0.9259999999999999
    • type: precision_at_1000 value: 0.126
    • type: precision_at_3 value: 12.138
    • type: precision_at_5 value: 8.909
    • type: recall_at_1 value: 17.936
    • type: recall_at_10 value: 48.096
    • type: recall_at_100 value: 75.389
    • type: recall_at_1000 value: 92.803
    • type: recall_at_3 value: 32.812999999999995
    • type: recall_at_5 value: 38.851
  • task: type: Retrieval dataset: type: mteb/climate-fever name: MTEB ClimateFEVER config: default split: test revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 metrics:
    • type: map_at_1 value: 22.076999999999998
    • type: map_at_10 value: 35.44
    • type: map_at_100 value: 37.651
    • type: map_at_1000 value: 37.824999999999996
    • type: map_at_3 value: 30.764999999999997
    • type: map_at_5 value: 33.26
    • type: mrr_at_1 value: 50.163000000000004
    • type: mrr_at_10 value: 61.207
    • type: mrr_at_100 value: 61.675000000000004
    • type: mrr_at_1000 value: 61.692
    • type: mrr_at_3 value: 58.60999999999999
    • type: mrr_at_5 value: 60.307
    • type: ndcg_at_1 value: 50.163000000000004
    • type: ndcg_at_10 value: 45.882
    • type: ndcg_at_100 value: 53.239999999999995
    • type: ndcg_at_1000 value: 55.852000000000004
    • type: ndcg_at_3 value: 40.514
    • type: ndcg_at_5 value: 42.038
    • type: precision_at_1 value: 50.163000000000004
    • type: precision_at_10 value: 13.466000000000001
    • type: precision_at_100 value: 2.164
    • type: precision_at_1000 value: 0.266
    • type: precision_at_3 value: 29.707
    • type: precision_at_5 value: 21.694
    • type: recall_at_1 value: 22.076999999999998
    • type: recall_at_10 value: 50.193
    • type: recall_at_100 value: 74.993
    • type: recall_at_1000 value: 89.131
    • type: recall_at_3 value: 35.472
    • type: recall_at_5 value: 41.814
  • task: type: Retrieval dataset: type: mteb/dbpedia name: MTEB DBPedia config: default split: test revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 metrics:
    • type: map_at_1 value: 9.953
    • type: map_at_10 value: 24.515
    • type: map_at_100 value: 36.173
    • type: map_at_1000 value: 38.351
    • type: map_at_3 value: 16.592000000000002
    • type: map_at_5 value: 20.036
    • type: mrr_at_1 value: 74.25
    • type: mrr_at_10 value: 81.813
    • type: mrr_at_100 value: 82.006
    • type: mrr_at_1000 value: 82.011
    • type: mrr_at_3 value: 80.875
    • type: mrr_at_5 value: 81.362
    • type: ndcg_at_1 value: 62.5
    • type: ndcg_at_10 value: 52.42
    • type: ndcg_at_100 value: 56.808
    • type: ndcg_at_1000 value: 63.532999999999994
    • type: ndcg_at_3 value: 56.654
    • type: ndcg_at_5 value: 54.18300000000001
    • type: precision_at_1 value: 74.25
    • type: precision_at_10 value: 42.699999999999996
    • type: precision_at_100 value: 13.675
    • type: precision_at_1000 value: 2.664
    • type: precision_at_3 value: 60.5
    • type: precision_at_5 value: 52.800000000000004
    • type: recall_at_1 value: 9.953
    • type: recall_at_10 value: 30.253999999999998
    • type: recall_at_100 value: 62.516000000000005
    • type: recall_at_1000 value: 84.163
    • type: recall_at_3 value: 18.13
    • type: recall_at_5 value: 22.771
  • task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics:
    • type: accuracy value: 79.455
    • type: f1 value: 74.16798697647569
  • task: type: Retrieval dataset: type: mteb/fever name: MTEB FEVER config: default split: test revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 metrics:
    • type: map_at_1 value: 87.531
    • type: map_at_10 value: 93.16799999999999
    • type: map_at_100 value: 93.341
    • type: map_at_1000 value: 93.349
    • type: map_at_3 value: 92.444
    • type: map_at_5 value: 92.865
    • type: mrr_at_1 value: 94.014
    • type: mrr_at_10 value: 96.761
    • type: mrr_at_100 value: 96.762
    • type: mrr_at_1000 value: 96.762
    • type: mrr_at_3 value: 96.672
    • type: mrr_at_5 value: 96.736
    • type: ndcg_at_1 value: 94.014
    • type: ndcg_at_10 value: 95.112
    • type: ndcg_at_100 value: 95.578
    • type: ndcg_at_1000 value: 95.68900000000001
    • type: ndcg_at_3 value: 94.392
    • type: ndcg_at_5 value: 94.72500000000001
    • type: precision_at_1 value: 94.014
    • type: precision_at_10 value: 11.065
    • type: precision_at_100 value: 1.157
    • type: precision_at_1000 value: 0.11800000000000001
    • type: precision_at_3 value: 35.259
    • type: precision_at_5 value: 21.599
    • type: recall_at_1 value: 87.531
    • type: recall_at_10 value: 97.356
    • type: recall_at_100 value: 98.965
    • type: recall_at_1000 value: 99.607
    • type: recall_at_3 value: 95.312
    • type: recall_at_5 value: 96.295
  • task: type: Retrieval dataset: type: mteb/fiqa name: MTEB FiQA2018 config: default split: test revision: 27a168819829fe9bcd655c2df245fb19452e8e06 metrics:
    • type: map_at_1 value: 32.055
    • type: map_at_10 value: 53.114
    • type: map_at_100 value: 55.235
    • type: map_at_1000 value: 55.345
    • type: map_at_3 value: 45.854
    • type: map_at_5 value: 50.025
    • type: mrr_at_1 value: 60.34
    • type: mrr_at_10 value: 68.804
    • type: mrr_at_100 value: 69.309
    • type: mrr_at_1000 value: 69.32199999999999
    • type: mrr_at_3 value: 66.40899999999999
    • type: mrr_at_5 value: 67.976
    • type: ndcg_at_1 value: 60.34
    • type: ndcg_at_10 value: 62.031000000000006
    • type: ndcg_at_100 value: 68.00500000000001
    • type: ndcg_at_1000 value: 69.286
    • type: ndcg_at_3 value: 56.355999999999995
    • type: ndcg_at_5 value: 58.687
    • type: precision_at_1 value: 60.34
    • type: precision_at_10 value: 17.176
    • type: precision_at_100 value: 2.36
    • type: precision_at_1000 value: 0.259
    • type: precision_at_3 value: 37.14
    • type: precision_at_5 value: 27.809
    • type: recall_at_1 value: 32.055
    • type: recall_at_10 value: 70.91
    • type: recall_at_100 value: 91.83
    • type: recall_at_1000 value: 98.871
    • type: recall_at_3 value: 51.202999999999996
    • type: recall_at_5 value: 60.563
  • task: type: Retrieval dataset: type: mteb/hotpotqa name: MTEB HotpotQA config: default split: test revision: ab518f4d6fcca38d87c25209f94beba119d02014 metrics:
    • type: map_at_1 value: 43.68
    • type: map_at_10 value: 64.389
    • type: map_at_100 value: 65.24
    • type: map_at_1000 value: 65.303
    • type: map_at_3 value: 61.309000000000005
    • type: map_at_5 value: 63.275999999999996
    • type: mrr_at_1 value: 87.36
    • type: mrr_at_10 value: 91.12
    • type: mrr_at_100 value: 91.227
    • type: mrr_at_1000 value: 91.229
    • type: mrr_at_3 value: 90.57600000000001
    • type: mrr_at_5 value: 90.912
    • type: ndcg_at_1 value: 87.36
    • type: ndcg_at_10 value: 73.076
    • type: ndcg_at_100 value: 75.895
    • type: ndcg_at_1000 value: 77.049
    • type: ndcg_at_3 value: 68.929
    • type: ndcg_at_5 value: 71.28
    • type: precision_at_1 value: 87.36
    • type: precision_at_10 value: 14.741000000000001
    • type: precision_at_100 value: 1.694
    • type: precision_at_1000 value: 0.185
    • type: precision_at_3 value: 43.043
    • type: precision_at_5 value: 27.681
    • type: recall_at_1 value: 43.68
    • type: recall_at_10 value: 73.707
    • type: recall_at_100 value: 84.7
    • type: recall_at_1000 value: 92.309
    • type: recall_at_3 value: 64.564
    • type: recall_at_5 value: 69.203
  • task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics:
    • type: accuracy value: 96.75399999999999
    • type: ap value: 95.29389839242187
    • type: f1 value: 96.75348377433475
  • task: type: Retrieval dataset: type: mteb/msmarco name: MTEB MSMARCO config: default split: dev revision: c5a29a104738b98a9e76336939199e264163d4a0 metrics:
    • type: map_at_1 value: 25.176
    • type: map_at_10 value: 38.598
    • type: map_at_100 value: 39.707
    • type: map_at_1000 value: 39.744
    • type: map_at_3 value: 34.566
    • type: map_at_5 value: 36.863
    • type: mrr_at_1 value: 25.874000000000002
    • type: mrr_at_10 value: 39.214
    • type: mrr_at_100 value: 40.251
    • type: mrr_at_1000 value: 40.281
    • type: mrr_at_3 value: 35.291
    • type: mrr_at_5 value: 37.545
    • type: ndcg_at_1 value: 25.874000000000002
    • type: ndcg_at_10 value: 45.98
    • type: ndcg_at_100 value: 51.197
    • type: ndcg_at_1000 value: 52.073
    • type: ndcg_at_3 value: 37.785999999999994
    • type: ndcg_at_5 value: 41.870000000000005
    • type: precision_at_1 value: 25.874000000000002
    • type: precision_at_10 value: 7.181
    • type: precision_at_100 value: 0.979
    • type: precision_at_1000 value: 0.106
    • type: precision_at_3 value: 16.051000000000002
    • type: precision_at_5 value: 11.713
    • type: recall_at_1 value: 25.176
    • type: recall_at_10 value: 68.67699999999999
    • type: recall_at_100 value: 92.55
    • type: recall_at_1000 value: 99.164
    • type: recall_at_3 value: 46.372
    • type: recall_at_5 value: 56.16
  • task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics:
    • type: accuracy value: 99.03784769721841
    • type: f1 value: 98.97791641821495
  • task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics:
    • type: accuracy value: 91.88326493388054
    • type: f1 value: 73.74809928034335
  • task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics:
    • type: accuracy value: 85.41358439811701
    • type: f1 value: 83.503679460639
  • task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics:
    • type: accuracy value: 89.77135171486215
    • type: f1 value: 88.89843747468366
  • task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics:
    • type: v_measure value: 46.22695362087359
  • task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics:
    • type: v_measure value: 44.132372165849425
  • task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics:
    • type: map value: 33.35680810650402
    • type: mrr value: 34.72625715637218
  • task: type: Retrieval dataset: type: mteb/nfcorpus name: MTEB NFCorpus config: default split: test revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 metrics:
    • type: map_at_1 value: 7.165000000000001
    • type: map_at_10 value: 15.424
    • type: map_at_100 value: 20.28
    • type: map_at_1000 value: 22.065
    • type: map_at_3 value: 11.236
    • type: map_at_5 value: 13.025999999999998
    • type: mrr_at_1 value: 51.702999999999996
    • type: mrr_at_10 value: 59.965
    • type: mrr_at_100 value: 60.667
    • type: mrr_at_1000 value: 60.702999999999996
    • type: mrr_at_3 value: 58.772000000000006
    • type: mrr_at_5 value: 59.267
    • type: ndcg_at_1 value: 49.536
    • type: ndcg_at_10 value: 40.6
    • type: ndcg_at_100 value: 37.848
    • type: ndcg_at_1000 value: 46.657
    • type: ndcg_at_3 value: 46.117999999999995
    • type: ndcg_at_5 value: 43.619
    • type: precision_at_1 value: 51.393
    • type: precision_at_10 value: 30.31
    • type: precision_at_100 value: 9.972
    • type: precision_at_1000 value: 2.329
    • type: precision_at_3 value: 43.137
    • type: precision_at_5 value: 37.585
    • type: recall_at_1 value: 7.165000000000001
    • type: recall_at_10 value: 19.689999999999998
    • type: recall_at_100 value: 39.237
    • type: recall_at_1000 value: 71.417
    • type: recall_at_3 value: 12.247
    • type: recall_at_5 value: 14.902999999999999
  • task: type: Retrieval dataset: type: mteb/nq name: MTEB NQ config: default split: test revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 metrics:
    • type: map_at_1 value: 42.653999999999996
    • type: map_at_10 value: 59.611999999999995
    • type: map_at_100 value: 60.32300000000001
    • type: map_at_1000 value: 60.336
    • type: map_at_3 value: 55.584999999999994
    • type: map_at_5 value: 58.19
    • type: mrr_at_1 value: 47.683
    • type: mrr_at_10 value: 62.06700000000001
    • type: mrr_at_100 value: 62.537
    • type: mrr_at_1000 value: 62.544999999999995
    • type: mrr_at_3 value: 59.178
    • type: mrr_at_5 value: 61.034
    • type: ndcg_at_1 value: 47.654
    • type: ndcg_at_10 value: 67.001
    • type: ndcg_at_100 value: 69.73899999999999
    • type: ndcg_at_1000 value: 69.986
    • type: ndcg_at_3 value: 59.95700000000001
    • type: ndcg_at_5 value: 64.025
    • type: precision_at_1 value: 47.654
    • type: precision_at_10 value: 10.367999999999999
    • type: precision_at_100 value: 1.192
    • type: precision_at_1000 value: 0.121
    • type: precision_at_3 value: 26.651000000000003
    • type: precision_at_5 value: 18.459
    • type: recall_at_1 value: 42.653999999999996
    • type: recall_at_10 value: 86.619
    • type: recall_at_100 value: 98.04899999999999
    • type: recall_at_1000 value: 99.812
    • type: recall_at_3 value: 68.987
    • type: recall_at_5 value: 78.158
  • task: type: Retrieval dataset: type: mteb/quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics:
    • type: map_at_1 value: 72.538
    • type: map_at_10 value: 86.702
    • type: map_at_100 value: 87.31
    • type: map_at_1000 value: 87.323
    • type: map_at_3 value: 83.87
    • type: map_at_5 value: 85.682
    • type: mrr_at_1 value: 83.31
    • type: mrr_at_10 value: 89.225
    • type: mrr_at_100 value: 89.30399999999999
    • type: mrr_at_1000 value: 89.30399999999999
    • type: mrr_at_3 value: 88.44300000000001
    • type: mrr_at_5 value: 89.005
    • type: ndcg_at_1 value: 83.32000000000001
    • type: ndcg_at_10 value: 90.095
    • type: ndcg_at_100 value: 91.12
    • type: ndcg_at_1000 value: 91.179
    • type: ndcg_at_3 value: 87.606
    • type: ndcg_at_5 value: 89.031
    • type: precision_at_1 value: 83.32000000000001
    • type: precision_at_10 value: 13.641
    • type: precision_at_100 value: 1.541
    • type: precision_at_1000 value: 0.157
    • type: precision_at_3 value: 38.377
    • type: precision_at_5 value: 25.162000000000003
    • type: recall_at_1 value: 72.538
    • type: recall_at_10 value: 96.47200000000001
    • type: recall_at_100 value: 99.785
    • type: recall_at_1000 value: 99.99900000000001
    • type: recall_at_3 value: 89.278
    • type: recall_at_5 value: 93.367
  • task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics:
    • type: v_measure value: 73.55219145406065
  • task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics:
    • type: v_measure value: 74.13437105242755
  • task: type: Retrieval dataset: type: mteb/scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics:
    • type: map_at_1 value: 6.873
    • type: map_at_10 value: 17.944
    • type: map_at_100 value: 21.171
    • type: map_at_1000 value: 21.528
    • type: map_at_3 value: 12.415
    • type: map_at_5 value: 15.187999999999999
    • type: mrr_at_1 value: 33.800000000000004
    • type: mrr_at_10 value: 46.455
    • type: mrr_at_100 value: 47.378
    • type: mrr_at_1000 value: 47.394999999999996
    • type: mrr_at_3 value: 42.367
    • type: mrr_at_5 value: 44.972
    • type: ndcg_at_1 value: 33.800000000000004
    • type: ndcg_at_10 value: 28.907
    • type: ndcg_at_100 value: 39.695
    • type: ndcg_at_1000 value: 44.582
    • type: ndcg_at_3 value: 26.949
    • type: ndcg_at_5 value: 23.988
    • type: precision_at_1 value: 33.800000000000004
    • type: precision_at_10 value: 15.079999999999998
    • type: precision_at_100 value: 3.056
    • type: precision_at_1000 value: 0.42100000000000004
    • type: precision_at_3 value: 25.167
    • type: precision_at_5 value: 21.26
    • type: recall_at_1 value: 6.873
    • type: recall_at_10 value: 30.568
    • type: recall_at_100 value: 62.062
    • type: recall_at_1000 value: 85.37700000000001
    • type: recall_at_3 value: 15.312999999999999
    • type: recall_at_5 value: 21.575
  • task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics:
    • type: cos_sim_pearson value: 82.37009118256057
    • type: cos_sim_spearman value: 79.27986395671529
    • type: euclidean_pearson value: 79.18037715442115
    • type: euclidean_spearman value: 79.28004791561621
    • type: manhattan_pearson value: 79.34062972800541
    • type: manhattan_spearman value: 79.43106695543402
  • task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics:
    • type: cos_sim_pearson value: 87.48474767383833
    • type: cos_sim_spearman value: 79.54505388752513
    • type: euclidean_pearson value: 83.43282704179565
    • type: euclidean_spearman value: 79.54579919925405
    • type: manhattan_pearson value: 83.77564492427952
    • type: manhattan_spearman value: 79.84558396989286
  • task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics:
    • type: cos_sim_pearson value: 88.803698035802
    • type: cos_sim_spearman value: 88.83451367754881
    • type: euclidean_pearson value: 88.28939285711628
    • type: euclidean_spearman value: 88.83528996073112
    • type: manhattan_pearson value: 88.28017412671795
    • type: manhattan_spearman value: 88.9228828016344
  • task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics:
    • type: cos_sim_pearson value: 85.27469288153428
    • type: cos_sim_spearman value: 83.87477064876288
    • type: euclidean_pearson value: 84.2601737035379
    • type: euclidean_spearman value: 83.87431082479074
    • type: manhattan_pearson value: 84.3621547772745
    • type: manhattan_spearman value: 84.12094375000423
  • task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics:
    • type: cos_sim_pearson value: 88.12749863201587
    • type: cos_sim_spearman value: 88.54287568368565
    • type: euclidean_pearson value: 87.90429700607999
    • type: euclidean_spearman value: 88.5437689576261
    • type: manhattan_pearson value: 88.19276653356833
    • type: manhattan_spearman value: 88.99995393814679
  • task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics:
    • type: cos_sim_pearson value: 85.68398747560902
    • type: cos_sim_spearman value: 86.48815303460574
    • type: euclidean_pearson value: 85.52356631237954
    • type: euclidean_spearman value: 86.486391949551
    • type: manhattan_pearson value: 85.67267981761788
    • type: manhattan_spearman value: 86.7073696332485
  • 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: 88.9057107443124
    • type: cos_sim_spearman value: 88.7312168757697
    • type: euclidean_pearson value: 88.72810439714794
    • type: euclidean_spearman value: 88.71976185854771
    • type: manhattan_pearson value: 88.50433745949111
    • type: manhattan_spearman value: 88.51726175544195
  • 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: 67.59391795109886
    • type: cos_sim_spearman value: 66.87613008631367
    • type: euclidean_pearson value: 69.23198488262217
    • type: euclidean_spearman value: 66.85427723013692
    • type: manhattan_pearson value: 69.50730124841084
    • type: manhattan_spearman value: 67.10404669820792
  • task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics:
    • type: cos_sim_pearson value: 87.0820605344619
    • type: cos_sim_spearman value: 86.8518089863434
    • type: euclidean_pearson value: 86.31087134689284
    • type: euclidean_spearman value: 86.8518520517941
    • type: manhattan_pearson value: 86.47203796160612
    • type: manhattan_spearman value: 87.1080149734421
  • task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics:
    • type: map value: 89.09255369305481
    • type: mrr value: 97.10323445617563
  • task: type: Retrieval dataset: type: mteb/scifact name: MTEB SciFact config: default split: test revision: 0228b52cf27578f30900b9e5271d331663a030d7 metrics:
    • type: map_at_1 value: 61.260999999999996
    • type: map_at_10 value: 74.043
    • type: map_at_100 value: 74.37700000000001
    • type: map_at_1000 value: 74.384
    • type: map_at_3 value: 71.222
    • type: map_at_5 value: 72.875
    • type: mrr_at_1 value: 64.333
    • type: mrr_at_10 value: 74.984
    • type: mrr_at_100 value: 75.247
    • type: mrr_at_1000 value: 75.25500000000001
    • type: mrr_at_3 value: 73.167
    • type: mrr_at_5 value: 74.35000000000001
    • type: ndcg_at_1 value: 64.333
    • type: ndcg_at_10 value: 79.06
    • type: ndcg_at_100 value: 80.416
    • type: ndcg_at_1000 value: 80.55600000000001
    • type: ndcg_at_3 value: 74.753
    • type: ndcg_at_5 value: 76.97500000000001
    • type: precision_at_1 value: 64.333
    • type: precision_at_10 value: 10.567
    • type: precision_at_100 value: 1.1199999999999999
    • type: precision_at_1000 value: 0.11299999999999999
    • type: precision_at_3 value: 29.889
    • type: precision_at_5 value: 19.533
    • type: recall_at_1 value: 61.260999999999996
    • type: recall_at_10 value: 93.167
    • type: recall_at_100 value: 99.0
    • type: recall_at_1000 value: 100.0
    • type: recall_at_3 value: 81.667
    • type: recall_at_5 value: 87.394
  • task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics:
    • type: cos_sim_accuracy value: 99.71980198019801
    • type: cos_sim_ap value: 92.81616007802704
    • type: cos_sim_f1 value: 85.17548454688318
    • type: cos_sim_precision value: 89.43894389438944
    • type: cos_sim_recall value: 81.3
    • type: dot_accuracy value: 99.71980198019801
    • type: dot_ap value: 92.81398760591358
    • type: dot_f1 value: 85.17548454688318
    • type: dot_precision value: 89.43894389438944
    • type: dot_recall value: 81.3
    • type: euclidean_accuracy value: 99.71980198019801
    • type: euclidean_ap value: 92.81560637245072
    • type: euclidean_f1 value: 85.17548454688318
    • type: euclidean_precision value: 89.43894389438944
    • type: euclidean_recall value: 81.3
    • type: manhattan_accuracy value: 99.73069306930694
    • type: manhattan_ap value: 93.14005487480794
    • type: manhattan_f1 value: 85.56263269639068
    • type: manhattan_precision value: 91.17647058823529
    • type: manhattan_recall value: 80.60000000000001
    • type: max_accuracy value: 99.73069306930694
    • type: max_ap value: 93.14005487480794
    • type: max_f1 value: 85.56263269639068
  • task: type: Clustering dataset: type: mteb/stackexchange-clustering name: MTEB StackExchangeClustering config: default split: test revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 metrics:
    • type: v_measure value: 79.86443362395185
  • task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics:
    • type: v_measure value: 49.40897096662564
  • task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics:
    • type: map value: 55.66040806627947
    • type: mrr value: 56.58670475766064
  • task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics:
    • type: cos_sim_pearson value: 31.51015090598575
    • type: cos_sim_spearman value: 31.35016454939226
    • type: dot_pearson value: 31.5150068731
    • type: dot_spearman value: 31.34790869023487
  • task: type: Retrieval dataset: type: mteb/trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics:
    • type: map_at_1 value: 0.254
    • type: map_at_10 value: 2.064
    • type: map_at_100 value: 12.909
    • type: map_at_1000 value: 31.761
    • type: map_at_3 value: 0.738
    • type: map_at_5 value: 1.155
    • type: mrr_at_1 value: 96.0
    • type: mrr_at_10 value: 98.0
    • type: mrr_at_100 value: 98.0
    • type: mrr_at_1000 value: 98.0
    • type: mrr_at_3 value: 98.0
    • type: mrr_at_5 value: 98.0
    • type: ndcg_at_1 value: 93.0
    • type: ndcg_at_10 value: 82.258
    • type: ndcg_at_100 value: 64.34
    • type: ndcg_at_1000 value: 57.912
    • type: ndcg_at_3 value: 90.827
    • type: ndcg_at_5 value: 86.79
    • type: precision_at_1 value: 96.0
    • type: precision_at_10 value: 84.8
    • type: precision_at_100 value: 66.0
    • type: precision_at_1000 value: 25.356
    • type: precision_at_3 value: 94.667
    • type: precision_at_5 value: 90.4
    • type: recall_at_1 value: 0.254
    • type: recall_at_10 value: 2.1950000000000003
    • type: recall_at_100 value: 16.088
    • type: recall_at_1000 value: 54.559000000000005
    • type: recall_at_3 value: 0.75
    • type: recall_at_5 value: 1.191
  • task: type: Retrieval dataset: type: mteb/touche2020 name: MTEB Touche2020 config: default split: test revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f metrics:
    • type: map_at_1 value: 2.976
    • type: map_at_10 value: 11.389000000000001
    • type: map_at_100 value: 18.429000000000002
    • type: map_at_1000 value: 20.113
    • type: map_at_3 value: 6.483
    • type: map_at_5 value: 8.770999999999999
    • type: mrr_at_1 value: 40.816
    • type: mrr_at_10 value: 58.118
    • type: mrr_at_100 value: 58.489999999999995
    • type: mrr_at_1000 value: 58.489999999999995
    • type: mrr_at_3 value: 53.061
    • type: mrr_at_5 value: 57.041
    • type: ndcg_at_1 value: 40.816
    • type: ndcg_at_10 value: 30.567
    • type: ndcg_at_100 value: 42.44
    • type: ndcg_at_1000 value: 53.480000000000004
    • type: ndcg_at_3 value: 36.016
    • type: ndcg_at_5 value: 34.257
    • type: precision_at_1 value: 42.857
    • type: precision_at_10 value: 25.714
    • type: precision_at_100 value: 8.429
    • type: precision_at_1000 value: 1.5939999999999999
    • type: precision_at_3 value: 36.735
    • type: precision_at_5 value: 33.878
    • type: recall_at_1 value: 2.976
    • type: recall_at_10 value: 17.854999999999997
    • type: recall_at_100 value: 51.833
    • type: recall_at_1000 value: 86.223
    • type: recall_at_3 value: 7.887
    • type: recall_at_5 value: 12.026
  • task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics:
    • type: accuracy value: 85.1174
    • type: ap value: 30.169441069345748
    • type: f1 value: 69.79254701873245
  • task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics:
    • type: accuracy value: 72.58347481607245
    • type: f1 value: 72.74877295564937
  • task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics:
    • type: v_measure value: 53.90586138221305
  • task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics:
    • type: cos_sim_accuracy value: 87.35769207844072
    • type: cos_sim_ap value: 77.9645072410354
    • type: cos_sim_f1 value: 71.32352941176471
    • type: cos_sim_precision value: 66.5903890160183
    • type: cos_sim_recall value: 76.78100263852242
    • type: dot_accuracy value: 87.37557370209214
    • type: dot_ap value: 77.96250046429908
    • type: dot_f1 value: 71.28932757557064
    • type: dot_precision value: 66.95249130938586
    • type: dot_recall value: 76.22691292875989
    • type: euclidean_accuracy value: 87.35173153722357
    • type: euclidean_ap value: 77.96520460741593
    • type: euclidean_f1 value: 71.32470733210104
    • type: euclidean_precision value: 66.91329479768785
    • type: euclidean_recall value: 76.35883905013192
    • type: manhattan_accuracy value: 87.25636287774931
    • type: manhattan_ap value: 77.77752485611796
    • type: manhattan_f1 value: 71.18148599269183
    • type: manhattan_precision value: 66.10859728506787
    • type: manhattan_recall value: 77.0976253298153
    • type: max_accuracy value: 87.37557370209214
    • type: max_ap value: 77.96520460741593
    • type: max_f1 value: 71.32470733210104
  • task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics:
    • type: cos_sim_accuracy value: 89.38176737687739
    • type: cos_sim_ap value: 86.58811861657401
    • type: cos_sim_f1 value: 79.09430644097604
    • type: cos_sim_precision value: 75.45085977911366
    • type: cos_sim_recall value: 83.10748383122882
    • type: dot_accuracy value: 89.38370784336554
    • type: dot_ap value: 86.58840606004333
    • type: dot_f1 value: 79.10179860068133
    • type: dot_precision value: 75.44546153308643
    • type: dot_recall value: 83.13058207576223
    • type: euclidean_accuracy value: 89.38564830985369
    • type: euclidean_ap value: 86.58820721061164
    • type: euclidean_f1 value: 79.09070942235888
    • type: euclidean_precision value: 75.38729937194697
    • type: euclidean_recall value: 83.17677856482906
    • type: manhattan_accuracy value: 89.40699344122326
    • type: manhattan_ap value: 86.60631843011362
    • type: manhattan_f1 value: 79.14949970570925
    • type: manhattan_precision value: 75.78191039729502
    • type: manhattan_recall value: 82.83030489682784
    • type: max_accuracy value: 89.40699344122326
    • type: max_ap value: 86.60631843011362
    • type: max_f1 value: 79.14949970570925
  • task: type: STS dataset: type: C-MTEB/AFQMC name: MTEB AFQMC config: default split: validation revision: b44c3b011063adb25877c13823db83bb193913c4 metrics:
    • type: cos_sim_pearson value: 65.58442135663871
    • type: cos_sim_spearman value: 72.2538631361313
    • type: euclidean_pearson value: 70.97255486607429
    • type: euclidean_spearman value: 72.25374250228647
    • type: manhattan_pearson value: 70.83250199989911
    • type: manhattan_spearman value: 72.14819496536272
  • task: type: STS dataset: type: C-MTEB/ATEC name: MTEB ATEC config: default split: test revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865 metrics:
    • type: cos_sim_pearson value: 59.99478404929932
    • type: cos_sim_spearman value: 62.61836216999812
    • type: euclidean_pearson value: 66.86429811933593
    • type: euclidean_spearman value: 62.6183520374191
    • type: manhattan_pearson value: 66.8063778911633
    • type: manhattan_spearman value: 62.569607573241115
  • task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (zh) config: zh split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics:
    • type: accuracy value: 53.98400000000001
    • type: f1 value: 51.21447361350723
  • task: type: STS dataset: type: C-MTEB/BQ name: MTEB BQ config: default split: test revision: e3dda5e115e487b39ec7e618c0c6a29137052a55 metrics:
    • type: cos_sim_pearson value: 79.11941660686553
    • type: cos_sim_spearman value: 81.25029594540435
    • type: euclidean_pearson value: 82.06973504238826
    • type: euclidean_spearman value: 81.2501989488524
    • type: manhattan_pearson value: 82.10094630392753
    • type: manhattan_spearman value: 81.27987244392389
  • task: type: Clustering dataset: type: C-MTEB/CLSClusteringP2P name: MTEB CLSClusteringP2P config: default split: test revision: 4b6227591c6c1a73bc76b1055f3b7f3588e72476 metrics:
    • type: v_measure value: 47.07270168705156
  • task: type: Clustering dataset: type: C-MTEB/CLSClusteringS2S name: MTEB CLSClusteringS2S config: default split: test revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f metrics:
    • type: v_measure value: 45.98511703185043
  • task: type: Reranking dataset: type: C-MTEB/CMedQAv1-reranking name: MTEB CMedQAv1 config: default split: test revision: 8d7f1e942507dac42dc58017c1a001c3717da7df metrics:
    • type: map value: 88.19895157194931
    • type: mrr value: 90.21424603174603
  • task: type: Reranking dataset: type: C-MTEB/CMedQAv2-reranking name: MTEB CMedQAv2 config: default split: test revision: 23d186750531a14a0357ca22cd92d712fd512ea0 metrics:
    • type: map value: 88.03317320980119
    • type: mrr value: 89.9461507936508
  • task: type: Retrieval dataset: type: C-MTEB/CmedqaRetrieval name: MTEB CmedqaRetrieval config: default split: dev revision: cd540c506dae1cf9e9a59c3e06f42030d54e7301 metrics:
    • type: map_at_1 value: 29.037000000000003
    • type: map_at_10 value: 42.001
    • type: map_at_100 value: 43.773
    • type: map_at_1000 value: 43.878
    • type: map_at_3 value: 37.637
    • type: map_at_5 value: 40.034
    • type: mrr_at_1 value: 43.136
    • type: mrr_at_10 value: 51.158
    • type: mrr_at_100 value: 52.083
    • type: mrr_at_1000 value: 52.12
    • type: mrr_at_3 value: 48.733
    • type: mrr_at_5 value: 50.025
    • type: ndcg_at_1 value: 43.136
    • type: ndcg_at_10 value: 48.685
    • type: ndcg_at_100 value: 55.513
    • type: ndcg_at_1000 value: 57.242000000000004
    • type: ndcg_at_3 value: 43.329
    • type: ndcg_at_5 value: 45.438
    • type: precision_at_1 value: 43.136
    • type: precision_at_10 value: 10.56
    • type: precision_at_100 value: 1.6129999999999998
    • type: precision_at_1000 value: 0.184
    • type: precision_at_3 value: 24.064
    • type: precision_at_5 value: 17.269000000000002
    • type: recall_at_1 value: 29.037000000000003
    • type: recall_at_10 value: 59.245000000000005
    • type: recall_at_100 value: 87.355
    • type: recall_at_1000 value: 98.74000000000001
    • type: recall_at_3 value: 42.99
    • type: recall_at_5 value: 49.681999999999995
  • task: type: PairClassification dataset: type: C-MTEB/CMNLI name: MTEB Cmnli config: default split: validation revision: 41bc36f332156f7adc9e38f53777c959b2ae9766 metrics:
    • type: cos_sim_accuracy value: 82.68190018039687
    • type: cos_sim_ap value: 90.18017125327886
    • type: cos_sim_f1 value: 83.64080906868193
    • type: cos_sim_precision value: 79.7076890489303
    • type: cos_sim_recall value: 87.98223053542202
    • type: dot_accuracy value: 82.68190018039687
    • type: dot_ap value: 90.18782350103646
    • type: dot_f1 value: 83.64242087729039
    • type: dot_precision value: 79.65313028764805
    • type: dot_recall value: 88.05237315875614
    • type: euclidean_accuracy value: 82.68190018039687
    • type: euclidean_ap value: 90.1801957900632
    • type: euclidean_f1 value: 83.63636363636364
    • type: euclidean_precision value: 79.52772506852203
    • type: euclidean_recall value: 88.19265840542437
    • type: manhattan_accuracy value: 82.14070956103427
    • type: manhattan_ap value: 89.96178420101427
    • type: manhattan_f1 value: 83.21087838578791
    • type: manhattan_precision value: 78.35605121850475
    • type: manhattan_recall value: 88.70703764320785
    • type: max_accuracy value: 82.68190018039687
    • type: max_ap value: 90.18782350103646
    • type: max_f1 value: 83.64242087729039
  • task: type: Retrieval dataset: type: C-MTEB/CovidRetrieval name: MTEB CovidRetrieval config: default split: dev revision: 1271c7809071a13532e05f25fb53511ffce77117 metrics:
    • type: map_at_1 value: 72.234
    • type: map_at_10 value: 80.10000000000001
    • type: map_at_100 value: 80.36
    • type: map_at_1000 value: 80.363
    • type: map_at_3 value: 78.315
    • type: map_at_5 value: 79.607
    • type: mrr_at_1 value: 72.392
    • type: mrr_at_10 value: 80.117
    • type: mrr_at_100 value: 80.36999999999999
    • type: mrr_at_1000 value: 80.373
    • type: mrr_at_3 value: 78.469
    • type: mrr_at_5 value: 79.633
    • type: ndcg_at_1 value: 72.392
    • type: ndcg_at_10 value: 83.651
    • type: ndcg_at_100 value: 84.749
    • type: ndcg_at_1000 value: 84.83000000000001
    • type: ndcg_at_3 value: 80.253
    • type: ndcg_at_5 value: 82.485
    • type: precision_at_1 value: 72.392
    • type: precision_at_10 value: 9.557
    • type: precision_at_100 value: 1.004
    • type: precision_at_1000 value: 0.101
    • type: precision_at_3 value: 28.732000000000003
    • type: precision_at_5 value: 18.377
    • type: recall_at_1 value: 72.234
    • type: recall_at_10 value: 94.573
    • type: recall_at_100 value: 99.368
    • type: recall_at_1000 value: 100.0
    • type: recall_at_3 value: 85.669
    • type: recall_at_5 value: 91.01700000000001
  • task: type: Retrieval dataset: type: C-MTEB/DuRetrieval name: MTEB DuRetrieval config: default split: dev revision: a1a333e290fe30b10f3f56498e3a0d911a693ced metrics:
    • type: map_at_1 value: 26.173999999999996
    • type: map_at_10 value: 80.04
    • type: map_at_100 value: 82.94500000000001
    • type: map_at_1000 value: 82.98100000000001
    • type: map_at_3 value: 55.562999999999995
    • type: map_at_5 value: 69.89800000000001
    • type: mrr_at_1 value: 89.5
    • type: mrr_at_10 value: 92.996
    • type: mrr_at_100 value: 93.06400000000001
    • type: mrr_at_1000 value: 93.065
    • type: mrr_at_3 value: 92.658
    • type: mrr_at_5 value: 92.84599999999999
    • type: ndcg_at_1 value: 89.5
    • type: ndcg_at_10 value: 87.443
    • type: ndcg_at_100 value: 90.253
    • type: ndcg_at_1000 value: 90.549
    • type: ndcg_at_3 value: 85.874
    • type: ndcg_at_5 value: 84.842
    • type: precision_at_1 value: 89.5
    • type: precision_at_10 value: 41.805
    • type: precision_at_100 value: 4.827
    • type: precision_at_1000 value: 0.49
    • type: precision_at_3 value: 76.85
    • type: precision_at_5 value: 64.8
    • type: recall_at_1 value: 26.173999999999996
    • type: recall_at_10 value: 89.101
    • type: recall_at_100 value: 98.08099999999999
    • type: recall_at_1000 value: 99.529
    • type: recall_at_3 value: 57.902
    • type: recall_at_5 value: 74.602
  • task: type: Retrieval dataset: type: C-MTEB/EcomRetrieval name: MTEB EcomRetrieval config: default split: dev revision: 687de13dc7294d6fd9be10c6945f9e8fec8166b9 metrics:
    • type: map_at_1 value: 56.10000000000001
    • type: map_at_10 value: 66.15299999999999
    • type: map_at_100 value: 66.625
    • type: map_at_1000 value: 66.636
    • type: map_at_3 value: 63.632999999999996
    • type: map_at_5 value: 65.293
    • type: mrr_at_1 value: 56.10000000000001
    • type: mrr_at_10 value: 66.15299999999999
    • type: mrr_at_100 value: 66.625
    • type: mrr_at_1000 value: 66.636
    • type: mrr_at_3 value: 63.632999999999996
    • type: mrr_at_5 value: 65.293
    • type: ndcg_at_1 value: 56.10000000000001
    • type: ndcg_at_10 value: 71.146
    • type: ndcg_at_100 value: 73.27799999999999
    • type: ndcg_at_1000 value: 73.529
    • type: ndcg_at_3 value: 66.09
    • type: ndcg_at_5 value: 69.08999999999999
    • type: precision_at_1 value: 56.10000000000001
    • type: precision_at_10 value: 8.68
    • type: precision_at_100 value: 0.964
    • type: precision_at_1000 value: 0.098
    • type: precision_at_3 value: 24.4
    • type: precision_at_5 value: 16.1
    • type: recall_at_1 value: 56.10000000000001
    • type: recall_at_10 value: 86.8
    • type: recall_at_100 value: 96.39999999999999
    • type: recall_at_1000 value: 98.3
    • type: recall_at_3 value: 73.2
    • type: recall_at_5 value: 80.5
  • task: type: Classification dataset: type: C-MTEB/IFlyTek-classification name: MTEB IFlyTek config: default split: validation revision: 421605374b29664c5fc098418fe20ada9bd55f8a metrics:
    • type: accuracy value: 54.52096960369373
    • type: f1 value: 40.930845295808695
  • task: type: Classification dataset: type: C-MTEB/JDReview-classification name: MTEB JDReview config: default split: test revision: b7c64bd89eb87f8ded463478346f76731f07bf8b metrics:
    • type: accuracy value: 86.51031894934334
    • type: ap value: 55.9516014323483
    • type: f1 value: 81.54813679326381
  • task: type: STS dataset: type: C-MTEB/LCQMC name: MTEB LCQMC config: default split: test revision: 17f9b096f80380fce5ed12a9be8be7784b337daf metrics:
    • type: cos_sim_pearson value: 69.67437838574276
    • type: cos_sim_spearman value: 73.81314174653045
    • type: euclidean_pearson value: 72.63430276680275
    • type: euclidean_spearman value: 73.81358736777001
    • type: manhattan_pearson value: 72.58743833842829
    • type: manhattan_spearman value: 73.7590419009179
  • task: type: Reranking dataset: type: C-MTEB/Mmarco-reranking name: MTEB MMarcoReranking config: default split: dev revision: None metrics:
    • type: map value: 31.648613483640254
    • type: mrr value: 30.37420634920635
  • task: type: Retrieval dataset: type: C-MTEB/MMarcoRetrieval name: MTEB MMarcoRetrieval config: default split: dev revision: 539bbde593d947e2a124ba72651aafc09eb33fc2 metrics:
    • type: map_at_1 value: 73.28099999999999
    • type: map_at_10 value: 81.977
    • type: map_at_100 value: 82.222
    • type: map_at_1000 value: 82.22699999999999
    • type: map_at_3 value: 80.441
    • type: map_at_5 value: 81.46600000000001
    • type: mrr_at_1 value: 75.673
    • type: mrr_at_10 value: 82.41000000000001
    • type: mrr_at_100 value: 82.616
    • type: mrr_at_1000 value: 82.621
    • type: mrr_at_3 value: 81.094
    • type: mrr_at_5 value: 81.962
    • type: ndcg_at_1 value: 75.673
    • type: ndcg_at_10 value: 85.15599999999999
    • type: ndcg_at_100 value: 86.151
    • type: ndcg_at_1000 value: 86.26899999999999
    • type: ndcg_at_3 value: 82.304
    • type: ndcg_at_5 value: 84.009
    • type: precision_at_1 value: 75.673
    • type: precision_at_10 value: 10.042
    • type: precision_at_100 value: 1.052
    • type: precision_at_1000 value: 0.106
    • type: precision_at_3 value: 30.673000000000002
    • type: precision_at_5 value: 19.326999999999998
    • type: recall_at_1 value: 73.28099999999999
    • type: recall_at_10 value: 94.446
    • type: recall_at_100 value: 98.737
    • type: recall_at_1000 value: 99.649
    • type: recall_at_3 value: 86.984
    • type: recall_at_5 value: 91.024
  • task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (zh-CN) config: zh-CN split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics:
    • type: accuracy value: 81.08607935440484
    • type: f1 value: 78.24879986066307
  • task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (zh-CN) config: zh-CN split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics:
    • type: accuracy value: 86.05917955615332
    • type: f1 value: 85.05279279434997
  • task: type: Retrieval dataset: type: C-MTEB/MedicalRetrieval name: MTEB MedicalRetrieval config: default split: dev revision: 2039188fb5800a9803ba5048df7b76e6fb151fc6 metrics:
    • type: map_at_1 value: 56.2
    • type: map_at_10 value: 62.57899999999999
    • type: map_at_100 value: 63.154999999999994
    • type: map_at_1000 value: 63.193
    • type: map_at_3 value: 61.217
    • type: map_at_5 value: 62.012
    • type: mrr_at_1 value: 56.3
    • type: mrr_at_10 value: 62.629000000000005
    • type: mrr_at_100 value: 63.205999999999996
    • type: mrr_at_1000 value: 63.244
    • type: mrr_at_3 value: 61.267
    • type: mrr_at_5 value: 62.062
    • type: ndcg_at_1 value: 56.2
    • type: ndcg_at_10 value: 65.592
    • type: ndcg_at_100 value: 68.657
    • type: ndcg_at_1000 value: 69.671
    • type: ndcg_at_3 value: 62.808
    • type: ndcg_at_5 value: 64.24499999999999
    • type: precision_at_1 value: 56.2
    • type: precision_at_10 value: 7.5
    • type: precision_at_100 value: 0.899
    • type: precision_at_1000 value: 0.098
    • type: precision_at_3 value: 22.467000000000002
    • type: precision_at_5 value: 14.180000000000001
    • type: recall_at_1 value: 56.2
    • type: recall_at_10 value: 75.0
    • type: recall_at_100 value: 89.9
    • type: recall_at_1000 value: 97.89999999999999
    • type: recall_at_3 value: 67.4
    • type: recall_at_5 value: 70.89999999999999
  • task: type: Classification dataset: type: C-MTEB/MultilingualSentiment-classification name: MTEB MultilingualSentiment config: default split: validation revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a metrics:
    • type: accuracy value: 76.87666666666667
    • type: f1 value: 76.7317686219665
  • task: type: PairClassification dataset: type: C-MTEB/OCNLI name: MTEB Ocnli config: default split: validation revision: 66e76a618a34d6d565d5538088562851e6daa7ec metrics:
    • type: cos_sim_accuracy value: 79.64266377910124
    • type: cos_sim_ap value: 84.78274442344829
    • type: cos_sim_f1 value: 81.16947472745292
    • type: cos_sim_precision value: 76.47058823529412
    • type: cos_sim_recall value: 86.48363252375924
    • type: dot_accuracy value: 79.64266377910124
    • type: dot_ap value: 84.7851404063692
    • type: dot_f1 value: 81.16947472745292
    • type: dot_precision value: 76.47058823529412
    • type: dot_recall value: 86.48363252375924
    • type: euclidean_accuracy value: 79.64266377910124
    • type: euclidean_ap value: 84.78068373762378
    • type: euclidean_f1 value: 81.14794656110837
    • type: euclidean_precision value: 76.35009310986965
    • type: euclidean_recall value: 86.58922914466737
    • type: manhattan_accuracy value: 79.48023822414727
    • type: manhattan_ap value: 84.72928897427576
    • type: manhattan_f1 value: 81.32084770823064
    • type: manhattan_precision value: 76.24768946395564
    • type: manhattan_recall value: 87.11721224920802
    • type: max_accuracy value: 79.64266377910124
    • type: max_ap value: 84.7851404063692
    • type: max_f1 value: 81.32084770823064
  • task: type: Classification dataset: type: C-MTEB/OnlineShopping-classification name: MTEB OnlineShopping config: default split: test revision: e610f2ebd179a8fda30ae534c3878750a96db120 metrics:
    • type: accuracy value: 94.3
    • type: ap value: 92.8664032274438
    • type: f1 value: 94.29311102997727
  • task: type: STS dataset: type: C-MTEB/PAWSX name: MTEB PAWSX config: default split: test revision: 9c6a90e430ac22b5779fb019a23e820b11a8b5e1 metrics:
    • type: cos_sim_pearson value: 48.51392279882909
    • type: cos_sim_spearman value: 54.06338895994974
    • type: euclidean_pearson value: 52.58480559573412
    • type: euclidean_spearman value: 54.06417276612201
    • type: manhattan_pearson value: 52.69525121721343
    • type: manhattan_spearman value: 54.048147455389675
  • task: type: STS dataset: type: C-MTEB/QBQTC name: MTEB QBQTC config: default split: test revision: 790b0510dc52b1553e8c49f3d2afb48c0e5c48b7 metrics:
    • type: cos_sim_pearson value: 29.728387290757325
    • type: cos_sim_spearman value: 31.366121633635284
    • type: euclidean_pearson value: 29.14588368552961
    • type: euclidean_spearman value: 31.36764411112844
    • type: manhattan_pearson value: 29.63517350523121
    • type: manhattan_spearman value: 31.94157020583762
  • task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (zh) config: zh split: test revision: eea2b4fe26a775864c896887d910b76a8098ad3f metrics:
    • type: cos_sim_pearson value: 63.64868296271406
    • type: cos_sim_spearman value: 66.12800618164744
    • type: euclidean_pearson value: 63.21405767340238
    • type: euclidean_spearman value: 66.12786567790748
    • type: manhattan_pearson value: 64.04300276525848
    • type: manhattan_spearman value: 66.5066857145652
  • task: type: STS dataset: type: C-MTEB/STSB name: MTEB STSB config: default split: test revision: 0cde68302b3541bb8b3c340dc0644b0b745b3dc0 metrics:
    • type: cos_sim_pearson value: 81.2302623912794
    • type: cos_sim_spearman value: 81.16833673266562
    • type: euclidean_pearson value: 79.47647843876024
    • type: euclidean_spearman value: 81.16944349524972
    • type: manhattan_pearson value: 79.84947238492208
    • type: manhattan_spearman value: 81.64626599410026
  • task: type: Reranking dataset: type: C-MTEB/T2Reranking name: MTEB T2Reranking config: default split: dev revision: 76631901a18387f85eaa53e5450019b87ad58ef9 metrics:
    • type: map value: 67.80129586475687
    • type: mrr value: 77.77402311635554
  • task: type: Retrieval dataset: type: C-MTEB/T2Retrieval name: MTEB T2Retrieval config: default split: dev revision: 8731a845f1bf500a4f111cf1070785c793d10e64 metrics:
    • type: map_at_1 value: 28.666999999999998
    • type: map_at_10 value: 81.063
    • type: map_at_100 value: 84.504
    • type: map_at_1000 value: 84.552
    • type: map_at_3 value: 56.897
    • type: map_at_5 value: 70.073
    • type: mrr_at_1 value: 92.087
    • type: mrr_at_10 value: 94.132
    • type: mrr_at_100 value: 94.19800000000001
    • type: mrr_at_1000 value: 94.19999999999999
    • type: mrr_at_3 value: 93.78999999999999
    • type: mrr_at_5 value: 94.002
    • type: ndcg_at_1 value: 92.087
    • type: ndcg_at_10 value: 87.734
    • type: ndcg_at_100 value: 90.736
    • type: ndcg_at_1000 value: 91.184
    • type: ndcg_at_3 value: 88.78
    • type: ndcg_at_5 value: 87.676
    • type: precision_at_1 value: 92.087
    • type: precision_at_10 value: 43.46
    • type: precision_at_100 value: 5.07
    • type: precision_at_1000 value: 0.518
    • type: precision_at_3 value: 77.49000000000001
    • type: precision_at_5 value: 65.194
    • type: recall_at_1 value: 28.666999999999998
    • type: recall_at_10 value: 86.632
    • type: recall_at_100 value: 96.646
    • type: recall_at_1000 value: 98.917
    • type: recall_at_3 value: 58.333999999999996
    • type: recall_at_5 value: 72.974
  • task: type: Classification dataset: type: C-MTEB/TNews-classification name: MTEB TNews config: default split: validation revision: 317f262bf1e6126357bbe89e875451e4b0938fe4 metrics:
    • type: accuracy value: 52.971999999999994
    • type: f1 value: 50.2898280984929
  • task: type: Clustering dataset: type: C-MTEB/ThuNewsClusteringP2P name: MTEB ThuNewsClusteringP2P config: default split: test revision: 5798586b105c0434e4f0fe5e767abe619442cf93 metrics:
    • type: v_measure value: 86.0797948663824
  • task: type: Clustering dataset: type: C-MTEB/ThuNewsClusteringS2S name: MTEB ThuNewsClusteringS2S config: default split: test revision: 8a8b2caeda43f39e13c4bc5bea0f8a667896e10d metrics:
    • type: v_measure value: 85.10759092255017
  • task: type: Retrieval dataset: type: C-MTEB/VideoRetrieval name: MTEB VideoRetrieval config: default split: dev revision: 58c2597a5943a2ba48f4668c3b90d796283c5639 metrics:
    • type: map_at_1 value: 65.60000000000001
    • type: map_at_10 value: 74.773
    • type: map_at_100 value: 75.128
    • type: map_at_1000 value: 75.136
    • type: map_at_3 value: 73.05
    • type: map_at_5 value: 74.13499999999999
    • type: mrr_at_1 value: 65.60000000000001
    • type: mrr_at_10 value: 74.773
    • type: mrr_at_100 value: 75.128
    • type: mrr_at_1000 value: 75.136
    • type: mrr_at_3 value: 73.05
    • type: mrr_at_5 value: 74.13499999999999
    • type: ndcg_at_1 value: 65.60000000000001
    • type: ndcg_at_10 value: 78.84299999999999
    • type: ndcg_at_100 value: 80.40899999999999
    • type: ndcg_at_1000 value: 80.57
    • type: ndcg_at_3 value: 75.40599999999999
    • type: ndcg_at_5 value: 77.351
    • type: precision_at_1 value: 65.60000000000001
    • type: precision_at_10 value: 9.139999999999999
    • type: precision_at_100 value: 0.984
    • type: precision_at_1000 value: 0.1
    • type: precision_at_3 value: 27.400000000000002
    • type: precision_at_5 value: 17.380000000000003
    • type: recall_at_1 value: 65.60000000000001
    • type: recall_at_10 value: 91.4
    • type: recall_at_100 value: 98.4
    • type: recall_at_1000 value: 99.6
    • type: recall_at_3 value: 82.19999999999999
    • type: recall_at_5 value: 86.9
  • task: type: Classification dataset: type: C-MTEB/waimai-classification name: MTEB Waimai config: default split: test revision: 339287def212450dcaa9df8c22bf93e9980c7023 metrics:
    • type: accuracy value: 89.47
    • type: ap value: 75.59561751845389
    • type: f1 value: 87.95207751382563
  • dataset: config: default name: MTEB AlloProfClusteringP2P revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b split: test type: lyon-nlp/alloprof metrics:
    • type: v_measure value: 76.05592323841036 task: type: Clustering
  • dataset: config: default name: MTEB AlloProfClusteringS2S revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b split: test type: lyon-nlp/alloprof metrics:
    • type: v_measure value: 64.51718058866508 task: type: Clustering
  • dataset: config: default name: MTEB AlloprofReranking revision: 666fdacebe0291776e86f29345663dfaf80a0db9 split: test type: lyon-nlp/mteb-fr-reranking-alloprof-s2p metrics:
    • type: map value: 73.08278490943373
    • type: mrr value: 74.66561454570449 task: type: Reranking
  • dataset: config: default name: MTEB AlloprofRetrieval revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b split: test type: lyon-nlp/alloprof metrics:
    • type: map_at_1 value: 38.912
    • type: map_at_10 value: 52.437999999999995
    • type: map_at_100 value: 53.38
    • type: map_at_1000 value: 53.427
    • type: map_at_3 value: 48.879
    • type: map_at_5 value: 50.934000000000005
    • type: mrr_at_1 value: 44.085
    • type: mrr_at_10 value: 55.337
    • type: mrr_at_100 value: 56.016999999999996
    • type: mrr_at_1000 value: 56.043
    • type: mrr_at_3 value: 52.55499999999999
    • type: mrr_at_5 value: 54.20399999999999
    • type: ndcg_at_1 value: 44.085
    • type: ndcg_at_10 value: 58.876
    • type: ndcg_at_100 value: 62.714000000000006
    • type: ndcg_at_1000 value: 63.721000000000004
    • type: ndcg_at_3 value: 52.444
    • type: ndcg_at_5 value: 55.692
    • type: precision_at_1 value: 44.085
    • type: precision_at_10 value: 9.21
    • type: precision_at_100 value: 1.164
    • type: precision_at_1000 value: 0.128
    • type: precision_at_3 value: 23.043
    • type: precision_at_5 value: 15.898000000000001
    • type: recall_at_1 value: 38.912
    • type: recall_at_10 value: 75.577
    • type: recall_at_100 value: 92.038
    • type: recall_at_1000 value: 99.325
    • type: recall_at_3 value: 58.592
    • type: recall_at_5 value: 66.235 task: type: Retrieval
  • dataset: config: fr name: MTEB AmazonReviewsClassification (fr) revision: 1399c76144fd37290681b995c656ef9b2e06e26d split: test type: mteb/amazon_reviews_multi metrics:
    • type: accuracy value: 55.532000000000004
    • type: f1 value: 52.5783943471605 task: type: Classification
  • dataset: config: default name: MTEB BSARDRetrieval revision: 5effa1b9b5fa3b0f9e12523e6e43e5f86a6e6d59 split: test type: maastrichtlawtech/bsard metrics:
    • type: map_at_1 value: 8.108
    • type: map_at_10 value: 14.710999999999999
    • type: map_at_100 value: 15.891
    • type: map_at_1000 value: 15.983
    • type: map_at_3 value: 12.237
    • type: map_at_5 value: 13.679
    • type: mrr_at_1 value: 8.108
    • type: mrr_at_10 value: 14.710999999999999
    • type: mrr_at_100 value: 15.891
    • type: mrr_at_1000 value: 15.983
    • type: mrr_at_3 value: 12.237
    • type: mrr_at_5 value: 13.679
    • type: ndcg_at_1 value: 8.108
    • type: ndcg_at_10 value: 18.796
    • type: ndcg_at_100 value: 25.098
    • type: ndcg_at_1000 value: 27.951999999999998
    • type: ndcg_at_3 value: 13.712
    • type: ndcg_at_5 value: 16.309
    • type: precision_at_1 value: 8.108
    • type: precision_at_10 value: 3.198
    • type: precision_at_100 value: 0.626
    • type: precision_at_1000 value: 0.086
    • type: precision_at_3 value: 6.006
    • type: precision_at_5 value: 4.865
    • type: recall_at_1 value: 8.108
    • type: recall_at_10 value: 31.982
    • type: recall_at_100 value: 62.613
    • type: recall_at_1000 value: 86.036
    • type: recall_at_3 value: 18.018
    • type: recall_at_5 value: 24.324 task: type: Retrieval
  • dataset: config: default name: MTEB HALClusteringS2S revision: e06ebbbb123f8144bef1a5d18796f3dec9ae2915 split: test type: lyon-nlp/clustering-hal-s2s metrics:
    • type: v_measure value: 30.833269778867116 task: type: Clustering
  • dataset: config: default name: MTEB MLSUMClusteringP2P revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7 split: test type: mlsum metrics:
    • type: v_measure value: 50.0281928004713 task: type: Clustering
  • dataset: config: default name: MTEB MLSUMClusteringS2S revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7 split: test type: mlsum metrics:
    • type: v_measure value: 43.699961510636534 task: type: Clustering
  • dataset: config: fr name: MTEB MTOPDomainClassification (fr) revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf split: test type: mteb/mtop_domain metrics:
    • type: accuracy value: 96.68963357344191
    • type: f1 value: 96.45175170820961 task: type: Classification
  • dataset: config: fr name: MTEB MTOPIntentClassification (fr) revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba split: test type: mteb/mtop_intent metrics:
    • type: accuracy value: 87.46946445349202
    • type: f1 value: 65.79860440988624 task: type: Classification
  • dataset: config: fra name: MTEB MasakhaNEWSClassification (fra) revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60 split: test type: masakhane/masakhanews metrics:
    • type: accuracy value: 82.60663507109005
    • type: f1 value: 77.20462646604777 task: type: Classification
  • dataset: config: fra name: MTEB MasakhaNEWSClusteringP2P (fra) revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60 split: test type: masakhane/masakhanews metrics:
    • type: v_measure value: 60.19311264967803 task: type: Clustering
  • dataset: config: fra name: MTEB MasakhaNEWSClusteringS2S (fra) revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60 split: test type: masakhane/masakhanews metrics:
    • type: v_measure value: 63.6235764409785 task: type: Clustering
  • dataset: config: fr name: MTEB MassiveIntentClassification (fr) revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 split: test type: mteb/amazon_massive_intent metrics:
    • type: accuracy value: 81.65097511768661
    • type: f1 value: 78.77796091490924 task: type: Classification
  • dataset: config: fr name: MTEB MassiveScenarioClassification (fr) revision: 7d571f92784cd94a019292a1f45445077d0ef634 split: test type: mteb/amazon_massive_scenario metrics:
    • type: accuracy value: 86.64425016812373
    • type: f1 value: 85.4912728670017 task: type: Classification
  • dataset: config: fr name: MTEB MintakaRetrieval (fr) revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e split: test type: jinaai/mintakaqa metrics:
    • type: map_at_1 value: 35.913000000000004
    • type: map_at_10 value: 48.147
    • type: map_at_100 value: 48.91
    • type: map_at_1000 value: 48.949
    • type: map_at_3 value: 45.269999999999996
    • type: map_at_5 value: 47.115
    • type: mrr_at_1 value: 35.913000000000004
    • type: mrr_at_10 value: 48.147
    • type: mrr_at_100 value: 48.91
    • type: mrr_at_1000 value: 48.949
    • type: mrr_at_3 value: 45.269999999999996
    • type: mrr_at_5 value: 47.115
    • type: ndcg_at_1 value: 35.913000000000004
    • type: ndcg_at_10 value: 54.03
    • type: ndcg_at_100 value: 57.839
    • type: ndcg_at_1000 value: 58.925000000000004
    • type: ndcg_at_3 value: 48.217999999999996
    • type: ndcg_at_5 value: 51.56699999999999
    • type: precision_at_1 value: 35.913000000000004
    • type: precision_at_10 value: 7.244000000000001
    • type: precision_at_100 value: 0.9039999999999999
    • type: precision_at_1000 value: 0.099
    • type: precision_at_3 value: 18.905
    • type: precision_at_5 value: 12.981000000000002
    • type: recall_at_1 value: 35.913000000000004
    • type: recall_at_10 value: 72.441
    • type: recall_at_100 value: 90.41799999999999
    • type: recall_at_1000 value: 99.099
    • type: recall_at_3 value: 56.716
    • type: recall_at_5 value: 64.90599999999999 task: type: Retrieval
  • dataset: config: fr name: MTEB OpusparcusPC (fr) revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a split: test type: GEM/opusparcus metrics:
    • type: cos_sim_accuracy value: 99.90069513406156
    • type: cos_sim_ap value: 100.0
    • type: cos_sim_f1 value: 99.95032290114257
    • type: cos_sim_precision value: 100.0
    • type: cos_sim_recall value: 99.90069513406156
    • type: dot_accuracy value: 99.90069513406156
    • type: dot_ap value: 100.0
    • type: dot_f1 value: 99.95032290114257
    • type: dot_precision value: 100.0
    • type: dot_recall value: 99.90069513406156
    • type: euclidean_accuracy value: 99.90069513406156
    • type: euclidean_ap value: 100.0
    • type: euclidean_f1 value: 99.95032290114257
    • type: euclidean_precision value: 100.0
    • type: euclidean_recall value: 99.90069513406156
    • type: manhattan_accuracy value: 99.90069513406156
    • type: manhattan_ap value: 100.0
    • type: manhattan_f1 value: 99.95032290114257
    • type: manhattan_precision value: 100.0
    • type: manhattan_recall value: 99.90069513406156
    • type: max_accuracy value: 99.90069513406156
    • type: max_ap value: 100.0
    • type: max_f1 value: 99.95032290114257 task: type: PairClassification
  • dataset: config: fr name: MTEB PawsX (fr) revision: 8a04d940a42cd40658986fdd8e3da561533a3646 split: test type: paws-x metrics:
    • type: cos_sim_accuracy value: 75.25
    • type: cos_sim_ap value: 80.86376001270014
    • type: cos_sim_f1 value: 73.65945437441204
    • type: cos_sim_precision value: 64.02289452166802
    • type: cos_sim_recall value: 86.71096345514951
    • type: dot_accuracy value: 75.25
    • type: dot_ap value: 80.93686107633002
    • type: dot_f1 value: 73.65945437441204
    • type: dot_precision value: 64.02289452166802
    • type: dot_recall value: 86.71096345514951
    • type: euclidean_accuracy value: 75.25
    • type: euclidean_ap value: 80.86379136218862
    • type: euclidean_f1 value: 73.65945437441204
    • type: euclidean_precision value: 64.02289452166802
    • type: euclidean_recall value: 86.71096345514951
    • type: manhattan_accuracy value: 75.3
    • type: manhattan_ap value: 80.87826606097734
    • type: manhattan_f1 value: 73.68421052631581
    • type: manhattan_precision value: 64.0
    • type: manhattan_recall value: 86.82170542635659
    • type: max_accuracy value: 75.3
    • type: max_ap value: 80.93686107633002
    • type: max_f1 value: 73.68421052631581 task: type: PairClassification
  • dataset: config: default name: MTEB SICKFr revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a split: test type: Lajavaness/SICK-fr metrics:
    • type: cos_sim_pearson value: 81.42349425981143
    • type: cos_sim_spearman value: 78.90454327031226
    • type: euclidean_pearson value: 78.39086497435166
    • type: euclidean_spearman value: 78.9046133980509
    • type: manhattan_pearson value: 78.63743094286502
    • type: manhattan_spearman value: 79.12136348449269 task: type: STS
  • dataset: config: fr name: MTEB STS22 (fr) revision: eea2b4fe26a775864c896887d910b76a8098ad3f split: test type: mteb/sts22-crosslingual-sts metrics:
    • type: cos_sim_pearson value: 81.452697919749
    • type: cos_sim_spearman value: 82.58116836039301
    • type: euclidean_pearson value: 81.04038478932786
    • type: euclidean_spearman value: 82.58116836039301
    • type: manhattan_pearson value: 81.37075396187771
    • type: manhattan_spearman value: 82.73678231355368 task: type: STS
  • dataset: config: fr name: MTEB STSBenchmarkMultilingualSTS (fr) revision: 93d57ef91790589e3ce9c365164337a8a78b7632 split: test type: stsb_multi_mt metrics:
    • type: cos_sim_pearson value: 85.7419764013806
    • type: cos_sim_spearman value: 85.46085808849622
    • type: euclidean_pearson value: 83.70449639870063
    • type: euclidean_spearman value: 85.46159013076233
    • type: manhattan_pearson value: 83.95259510313929
    • type: manhattan_spearman value: 85.8029724659458 task: type: STS
  • dataset: config: default name: MTEB SummEvalFr revision: b385812de6a9577b6f4d0f88c6a6e35395a94054 split: test type: lyon-nlp/summarization-summeval-fr-p2p metrics:
    • type: cos_sim_pearson value: 32.61063271753325
    • type: cos_sim_spearman value: 31.454589417353603
    • type: dot_pearson value: 32.6106288643431
    • type: dot_spearman value: 31.454589417353603 task: type: Summarization
  • dataset: config: default name: MTEB SyntecReranking revision: b205c5084a0934ce8af14338bf03feb19499c84d split: test type: lyon-nlp/mteb-fr-reranking-syntec-s2p metrics:
    • type: map value: 84.31666666666666
    • type: mrr value: 84.31666666666666 task: type: Reranking
  • dataset: config: default name: MTEB SyntecRetrieval revision: 77f7e271bf4a92b24fce5119f3486b583ca016ff split: test type: lyon-nlp/mteb-fr-retrieval-syntec-s2p metrics:
    • type: map_at_1 value: 63.0
    • type: map_at_10 value: 73.471
    • type: map_at_100 value: 73.87
    • type: map_at_1000 value: 73.87
    • type: map_at_3 value: 70.5
    • type: map_at_5 value: 73.05
    • type: mrr_at_1 value: 63.0
    • type: mrr_at_10 value: 73.471
    • type: mrr_at_100 value: 73.87
    • type: mrr_at_1000 value: 73.87
    • type: mrr_at_3 value: 70.5
    • type: mrr_at_5 value: 73.05
    • type: ndcg_at_1 value: 63.0
    • type: ndcg_at_10 value: 78.255
    • type: ndcg_at_100 value: 79.88
    • type: ndcg_at_1000 value: 79.88
    • type: ndcg_at_3 value: 72.702
    • type: ndcg_at_5 value: 77.264
    • type: precision_at_1 value: 63.0
    • type: precision_at_10 value: 9.3
    • type: precision_at_100 value: 1.0
    • type: precision_at_1000 value: 0.1
    • type: precision_at_3 value: 26.333000000000002
    • type: precision_at_5 value: 18.0
    • type: recall_at_1 value: 63.0
    • type: recall_at_10 value: 93.0
    • type: recall_at_100 value: 100.0
    • type: recall_at_1000 value: 100.0
    • type: recall_at_3 value: 79.0
    • type: recall_at_5 value: 90.0 task: type: Retrieval
  • dataset: config: fr name: MTEB XPQARetrieval (fr) revision: c99d599f0a6ab9b85b065da6f9d94f9cf731679f split: test type: jinaai/xpqa metrics:
    • type: map_at_1 value: 40.338
    • type: map_at_10 value: 61.927
    • type: map_at_100 value: 63.361999999999995
    • type: map_at_1000 value: 63.405
    • type: map_at_3 value: 55.479
    • type: map_at_5 value: 59.732
    • type: mrr_at_1 value: 63.551
    • type: mrr_at_10 value: 71.006
    • type: mrr_at_100 value: 71.501
    • type: mrr_at_1000 value: 71.509
    • type: mrr_at_3 value: 69.07
    • type: mrr_at_5 value: 70.165
    • type: ndcg_at_1 value: 63.551
    • type: ndcg_at_10 value: 68.297
    • type: ndcg_at_100 value: 73.13199999999999
    • type: ndcg_at_1000 value: 73.751
    • type: ndcg_at_3 value: 62.999
    • type: ndcg_at_5 value: 64.89
    • type: precision_at_1 value: 63.551
    • type: precision_at_10 value: 15.661
    • type: precision_at_100 value: 1.9789999999999999
    • type: precision_at_1000 value: 0.207
    • type: precision_at_3 value: 38.273
    • type: precision_at_5 value: 27.61
    • type: recall_at_1 value: 40.338
    • type: recall_at_10 value: 77.267
    • type: recall_at_100 value: 95.892
    • type: recall_at_1000 value: 99.75500000000001
    • type: recall_at_3 value: 60.36
    • type: recall_at_5 value: 68.825 task: type: Retrieval
  • dataset: config: default name: MTEB 8TagsClustering revision: None split: test type: PL-MTEB/8tags-clustering metrics:
    • type: v_measure value: 51.36126303874126 task: type: Clustering
  • dataset: config: default name: MTEB AllegroReviews revision: None split: test type: PL-MTEB/allegro-reviews metrics:
    • type: accuracy value: 67.13717693836979
    • type: f1 value: 57.27609848003782 task: type: Classification
  • dataset: config: default name: MTEB ArguAna-PL revision: 63fc86750af76253e8c760fc9e534bbf24d260a2 split: test type: clarin-knext/arguana-pl metrics:
    • type: map_at_1 value: 35.276999999999994
    • type: map_at_10 value: 51.086
    • type: map_at_100 value: 51.788000000000004
    • type: map_at_1000 value: 51.791
    • type: map_at_3 value: 46.147
    • type: map_at_5 value: 49.078
    • type: mrr_at_1 value: 35.917
    • type: mrr_at_10 value: 51.315999999999995
    • type: mrr_at_100 value: 52.018
    • type: mrr_at_1000 value: 52.022
    • type: mrr_at_3 value: 46.349000000000004
    • type: mrr_at_5 value: 49.297000000000004
    • type: ndcg_at_1 value: 35.276999999999994
    • type: ndcg_at_10 value: 59.870999999999995
    • type: ndcg_at_100 value: 62.590999999999994
    • type: ndcg_at_1000 value: 62.661
    • type: ndcg_at_3 value: 49.745
    • type: ndcg_at_5 value: 55.067
    • type: precision_at_1 value: 35.276999999999994
    • type: precision_at_10 value: 8.791
    • type: precision_at_100 value: 0.991
    • type: precision_at_1000 value: 0.1
    • type: precision_at_3 value: 20.057
    • type: precision_at_5 value: 14.637
    • type: recall_at_1 value: 35.276999999999994
    • type: recall_at_10 value: 87.909
    • type: recall_at_100 value: 99.14699999999999
    • type: recall_at_1000 value: 99.644
    • type: recall_at_3 value: 60.171
    • type: recall_at_5 value: 73.18599999999999 task: type: Retrieval
  • dataset: config: default name: MTEB CBD revision: None split: test type: PL-MTEB/cbd metrics:
    • type: accuracy value: 78.03000000000002
    • type: ap value: 29.12548553897622
    • type: f1 value: 66.54857118886073 task: type: Classification
  • dataset: config: default name: MTEB CDSC-E revision: None split: test type: PL-MTEB/cdsce-pairclassification metrics:
    • type: cos_sim_accuracy value: 89.0
    • type: cos_sim_ap value: 76.75437826834582
    • type: cos_sim_f1 value: 66.4850136239782
    • type: cos_sim_precision value: 68.92655367231639
    • type: cos_sim_recall value: 64.21052631578948
    • type: dot_accuracy value: 89.0
    • type: dot_ap value: 76.75437826834582
    • type: dot_f1 value: 66.4850136239782
    • type: dot_precision value: 68.92655367231639
    • type: dot_recall value: 64.21052631578948
    • type: euclidean_accuracy value: 89.0
    • type: euclidean_ap value: 76.75437826834582
    • type: euclidean_f1 value: 66.4850136239782
    • type: euclidean_precision value: 68.92655367231639
    • type: euclidean_recall value: 64.21052631578948
    • type: manhattan_accuracy value: 89.0
    • type: manhattan_ap value: 76.66074220647083
    • type: manhattan_f1 value: 66.47058823529412
    • type: manhattan_precision value: 75.33333333333333
    • type: manhattan_recall value: 59.473684210526315
    • type: max_accuracy value: 89.0
    • type: max_ap value: 76.75437826834582
    • type: max_f1 value: 66.4850136239782 task: type: PairClassification
  • dataset: config: default name: MTEB CDSC-R revision: None split: test type: PL-MTEB/cdscr-sts metrics:
    • type: cos_sim_pearson value: 93.12903172428328
    • type: cos_sim_spearman value: 92.66381487060741
    • type: euclidean_pearson value: 90.37278396708922
    • type: euclidean_spearman value: 92.66381487060741
    • type: manhattan_pearson value: 90.32503296540962
    • type: manhattan_spearman value: 92.6902938354313 task: type: STS
  • dataset: config: default name: MTEB DBPedia-PL revision: 76afe41d9af165cc40999fcaa92312b8b012064a split: test type: clarin-knext/dbpedia-pl metrics:
    • type: map_at_1 value: 8.83
    • type: map_at_10 value: 18.326
    • type: map_at_100 value: 26.496
    • type: map_at_1000 value: 28.455000000000002
    • type: map_at_3 value: 12.933
    • type: map_at_5 value: 15.168000000000001
    • type: mrr_at_1 value: 66.0
    • type: mrr_at_10 value: 72.76700000000001
    • type: mrr_at_100 value: 73.203
    • type: mrr_at_1000 value: 73.219
    • type: mrr_at_3 value: 71.458
    • type: mrr_at_5 value: 72.246
    • type: ndcg_at_1 value: 55.375
    • type: ndcg_at_10 value: 41.3
    • type: ndcg_at_100 value: 45.891
    • type: ndcg_at_1000 value: 52.905
    • type: ndcg_at_3 value: 46.472
    • type: ndcg_at_5 value: 43.734
    • type: precision_at_1 value: 66.0
    • type: precision_at_10 value: 33.074999999999996
    • type: precision_at_100 value: 11.094999999999999
    • type: precision_at_1000 value: 2.374
    • type: precision_at_3 value: 48.583
    • type: precision_at_5 value: 42.0
    • type: recall_at_1 value: 8.83
    • type: recall_at_10 value: 22.587
    • type: recall_at_100 value: 50.61600000000001
    • type: recall_at_1000 value: 73.559
    • type: recall_at_3 value: 13.688
    • type: recall_at_5 value: 16.855 task: type: Retrieval
  • dataset: config: default name: MTEB FiQA-PL revision: 2e535829717f8bf9dc829b7f911cc5bbd4e6608e split: test type: clarin-knext/fiqa-pl metrics:
    • type: map_at_1 value: 20.587
    • type: map_at_10 value: 33.095
    • type: map_at_100 value: 35.24
    • type: map_at_1000 value: 35.429
    • type: map_at_3 value: 28.626
    • type: map_at_5 value: 31.136999999999997
    • type: mrr_at_1 value: 40.586
    • type: mrr_at_10 value: 49.033
    • type: mrr_at_100 value: 49.952999999999996
    • type: mrr_at_1000 value: 49.992
    • type: mrr_at_3 value: 46.553
    • type: mrr_at_5 value: 48.035
    • type: ndcg_at_1 value: 40.586
    • type: ndcg_at_10 value: 41.046
    • type: ndcg_at_100 value: 48.586
    • type: ndcg_at_1000 value: 51.634
    • type: ndcg_at_3 value: 36.773
    • type: ndcg_at_5 value: 38.389
    • type: precision_at_1 value: 40.586
    • type: precision_at_10 value: 11.466
    • type: precision_at_100 value: 1.909
    • type: precision_at_1000 value: 0.245
    • type: precision_at_3 value: 24.434
    • type: precision_at_5 value: 18.426000000000002
    • type: recall_at_1 value: 20.587
    • type: recall_at_10 value: 47.986000000000004
    • type: recall_at_100 value: 75.761
    • type: recall_at_1000 value: 94.065
    • type: recall_at_3 value: 33.339
    • type: recall_at_5 value: 39.765 task: type: Retrieval
  • dataset: config: default name: MTEB HotpotQA-PL revision: a0bd479ac97b4ccb5bd6ce320c415d0bb4beb907 split: test type: clarin-knext/hotpotqa-pl metrics:
    • type: map_at_1 value: 40.878
    • type: map_at_10 value: 58.775999999999996
    • type: map_at_100 value: 59.632
    • type: map_at_1000 value: 59.707
    • type: map_at_3 value: 56.074
    • type: map_at_5 value: 57.629
    • type: mrr_at_1 value: 81.756
    • type: mrr_at_10 value: 86.117
    • type: mrr_at_100 value: 86.299
    • type: mrr_at_1000 value: 86.30600000000001
    • type: mrr_at_3 value: 85.345
    • type: mrr_at_5 value: 85.832
    • type: ndcg_at_1 value: 81.756
    • type: ndcg_at_10 value: 67.608
    • type: ndcg_at_100 value: 70.575
    • type: ndcg_at_1000 value: 71.99600000000001
    • type: ndcg_at_3 value: 63.723
    • type: ndcg_at_5 value: 65.70700000000001
    • type: precision_at_1 value: 81.756
    • type: precision_at_10 value: 13.619
    • type: precision_at_100 value: 1.5939999999999999
    • type: precision_at_1000 value: 0.178
    • type: precision_at_3 value: 39.604
    • type: precision_at_5 value: 25.332
    • type: recall_at_1 value: 40.878
    • type: recall_at_10 value: 68.096
    • type: recall_at_100 value: 79.696
    • type: recall_at_1000 value: 89.082
    • type: recall_at_3 value: 59.406000000000006
    • type: recall_at_5 value: 63.329 task: type: Retrieval
  • dataset: config: default name: MTEB MSMARCO-PL revision: 8634c07806d5cce3a6138e260e59b81760a0a640 split: test type: clarin-knext/msmarco-pl metrics:
    • type: map_at_1 value: 2.1839999999999997
    • type: map_at_10 value: 11.346
    • type: map_at_100 value: 30.325000000000003
    • type: map_at_1000 value: 37.806
    • type: map_at_3 value: 4.842
    • type: map_at_5 value: 6.891
    • type: mrr_at_1 value: 86.047
    • type: mrr_at_10 value: 89.14699999999999
    • type: mrr_at_100 value: 89.46600000000001
    • type: mrr_at_1000 value: 89.46600000000001
    • type: mrr_at_3 value: 89.14699999999999
    • type: mrr_at_5 value: 89.14699999999999
    • type: ndcg_at_1 value: 67.829
    • type: ndcg_at_10 value: 62.222
    • type: ndcg_at_100 value: 55.337
    • type: ndcg_at_1000 value: 64.076
    • type: ndcg_at_3 value: 68.12700000000001
    • type: ndcg_at_5 value: 64.987
    • type: precision_at_1 value: 86.047
    • type: precision_at_10 value: 69.535
    • type: precision_at_100 value: 32.93
    • type: precision_at_1000 value: 6.6049999999999995
    • type: precision_at_3 value: 79.845
    • type: precision_at_5 value: 75.349
    • type: recall_at_1 value: 2.1839999999999997
    • type: recall_at_10 value: 12.866
    • type: recall_at_100 value: 43.505
    • type: recall_at_1000 value: 72.366
    • type: recall_at_3 value: 4.947
    • type: recall_at_5 value: 7.192 task: type: Retrieval
  • dataset: config: pl name: MTEB MassiveIntentClassification (pl) revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 split: test type: mteb/amazon_massive_intent metrics:
    • type: accuracy value: 80.75319435104238
    • type: f1 value: 77.58961444860606 task: type: Classification
  • dataset: config: pl name: MTEB MassiveScenarioClassification (pl) revision: 7d571f92784cd94a019292a1f45445077d0ef634 split: test type: mteb/amazon_massive_scenario metrics:
    • type: accuracy value: 85.54472091459313
    • type: f1 value: 84.29498563572106 task: type: Classification
  • dataset: config: default name: MTEB NFCorpus-PL revision: 9a6f9567fda928260afed2de480d79c98bf0bec0 split: test type: clarin-knext/nfcorpus-pl metrics:
    • type: map_at_1 value: 4.367
    • type: map_at_10 value: 10.38
    • type: map_at_100 value: 13.516
    • type: map_at_1000 value: 14.982000000000001
    • type: map_at_3 value: 7.367
    • type: map_at_5 value: 8.59
    • type: mrr_at_1 value: 41.486000000000004
    • type: mrr_at_10 value: 48.886
    • type: mrr_at_100 value: 49.657000000000004
    • type: mrr_at_1000 value: 49.713
    • type: mrr_at_3 value: 46.904
    • type: mrr_at_5 value: 48.065000000000005
    • type: ndcg_at_1 value: 40.402
    • type: ndcg_at_10 value: 30.885
    • type: ndcg_at_100 value: 28.393
    • type: ndcg_at_1000 value: 37.428
    • type: ndcg_at_3 value: 35.394999999999996
    • type: ndcg_at_5 value: 33.391999999999996
    • type: precision_at_1 value: 41.486000000000004
    • type: precision_at_10 value: 23.437
    • type: precision_at_100 value: 7.638
    • type: precision_at_1000 value: 2.0389999999999997
    • type: precision_at_3 value: 32.817
    • type: precision_at_5 value: 28.915999999999997
    • type: recall_at_1 value: 4.367
    • type: recall_at_10 value: 14.655000000000001
    • type: recall_at_100 value: 29.665999999999997
    • type: recall_at_1000 value: 62.073
    • type: recall_at_3 value: 8.51
    • type: recall_at_5 value: 10.689 task: type: Retrieval
  • dataset: config: default name: MTEB NQ-PL revision: f171245712cf85dd4700b06bef18001578d0ca8d split: test type: clarin-knext/nq-pl metrics:
    • type: map_at_1 value: 28.616000000000003
    • type: map_at_10 value: 41.626000000000005
    • type: map_at_100 value: 42.689
    • type: map_at_1000 value: 42.733
    • type: map_at_3 value: 37.729
    • type: map_at_5 value: 39.879999999999995
    • type: mrr_at_1 value: 32.068000000000005
    • type: mrr_at_10 value: 44.029
    • type: mrr_at_100 value: 44.87
    • type: mrr_at_1000 value: 44.901
    • type: mrr_at_3 value: 40.687
    • type: mrr_at_5 value: 42.625
    • type: ndcg_at_1 value: 32.068000000000005
    • type: ndcg_at_10 value: 48.449999999999996
    • type: ndcg_at_100 value: 53.13
    • type: ndcg_at_1000 value: 54.186
    • type: ndcg_at_3 value: 40.983999999999995
    • type: ndcg_at_5 value: 44.628
    • type: precision_at_1 value: 32.068000000000005
    • type: precision_at_10 value: 7.9750000000000005
    • type: precision_at_100 value: 1.061
    • type: precision_at_1000 value: 0.116
    • type: precision_at_3 value: 18.404999999999998
    • type: precision_at_5 value: 13.111
    • type: recall_at_1 value: 28.616000000000003
    • type: recall_at_10 value: 66.956
    • type: recall_at_100 value: 87.657
    • type: recall_at_1000 value: 95.548
    • type: recall_at_3 value: 47.453
    • type: recall_at_5 value: 55.87800000000001 task: type: Retrieval
  • dataset: config: default name: MTEB PAC revision: None split: test type: laugustyniak/abusive-clauses-pl metrics:
    • type: accuracy value: 69.04141326382856
    • type: ap value: 77.47589122111044
    • type: f1 value: 66.6332277374775 task: type: Classification
  • dataset: config: default name: MTEB PPC revision: None split: test type: PL-MTEB/ppc-pairclassification metrics:
    • type: cos_sim_accuracy value: 86.4
    • type: cos_sim_ap value: 94.1044939667201
    • type: cos_sim_f1 value: 88.78048780487805
    • type: cos_sim_precision value: 87.22044728434504
    • type: cos_sim_recall value: 90.39735099337747
    • type: dot_accuracy value: 86.4
    • type: dot_ap value: 94.1044939667201
    • type: dot_f1 value: 88.78048780487805
    • type: dot_precision value: 87.22044728434504
    • type: dot_recall value: 90.39735099337747
    • type: euclidean_accuracy value: 86.4
    • type: euclidean_ap value: 94.1044939667201
    • type: euclidean_f1 value: 88.78048780487805
    • type: euclidean_precision value: 87.22044728434504
    • type: euclidean_recall value: 90.39735099337747
    • type: manhattan_accuracy value: 86.4
    • type: manhattan_ap value: 94.11438365697387
    • type: manhattan_f1 value: 88.77968877968877
    • type: manhattan_precision value: 87.84440842787681
    • type: manhattan_recall value: 89.73509933774835
    • type: max_accuracy value: 86.4
    • type: max_ap value: 94.11438365697387
    • type: max_f1 value: 88.78048780487805 task: type: PairClassification
  • dataset: config: default name: MTEB PSC revision: None split: test type: PL-MTEB/psc-pairclassification metrics:
    • type: cos_sim_accuracy value: 97.86641929499072
    • type: cos_sim_ap value: 99.36904211868182
    • type: cos_sim_f1 value: 96.56203288490283
    • type: cos_sim_precision value: 94.72140762463343
    • type: cos_sim_recall value: 98.47560975609755
    • type: dot_accuracy value: 97.86641929499072
    • type: dot_ap value: 99.36904211868183
    • type: dot_f1 value: 96.56203288490283
    • type: dot_precision value: 94.72140762463343
    • type: dot_recall value: 98.47560975609755
    • type: euclidean_accuracy value: 97.86641929499072
    • type: euclidean_ap value: 99.36904211868183
    • type: euclidean_f1 value: 96.56203288490283
    • type: euclidean_precision value: 94.72140762463343
    • type: euclidean_recall value: 98.47560975609755
    • type: manhattan_accuracy value: 98.14471243042672
    • type: manhattan_ap value: 99.43359540492416
    • type: manhattan_f1 value: 96.98795180722892
    • type: manhattan_precision value: 95.83333333333334
    • type: manhattan_recall value: 98.17073170731707
    • type: max_accuracy value: 98.14471243042672
    • type: max_ap value: 99.43359540492416
    • type: max_f1 value: 96.98795180722892 task: type: PairClassification
  • dataset: config: default name: MTEB PolEmo2.0-IN revision: None split: test type: PL-MTEB/polemo2_in metrics:
    • type: accuracy value: 89.39058171745152
    • type: f1 value: 86.8552093529568 task: type: Classification
  • dataset: config: default name: MTEB PolEmo2.0-OUT revision: None split: test type: PL-MTEB/polemo2_out metrics:
    • type: accuracy value: 74.97975708502024
    • type: f1 value: 58.73081628832407 task: type: Classification
  • dataset: config: default name: MTEB Quora-PL revision: 0be27e93455051e531182b85e85e425aba12e9d4 split: test type: clarin-knext/quora-pl metrics:
    • type: map_at_1 value: 64.917
    • type: map_at_10 value: 78.74600000000001
    • type: map_at_100 value: 79.501
    • type: map_at_1000 value: 79.524
    • type: map_at_3 value: 75.549
    • type: map_at_5 value: 77.495
    • type: mrr_at_1 value: 74.9
    • type: mrr_at_10 value: 82.112
    • type: mrr_at_100 value: 82.314
    • type: mrr_at_1000 value: 82.317
    • type: mrr_at_3 value: 80.745
    • type: mrr_at_5 value: 81.607
    • type: ndcg_at_1 value: 74.83999999999999
    • type: ndcg_at_10 value: 83.214
    • type: ndcg_at_100 value: 84.997
    • type: ndcg_at_1000 value: 85.207
    • type: ndcg_at_3 value: 79.547
    • type: ndcg_at_5 value: 81.46600000000001
    • type: precision_at_1 value: 74.83999999999999
    • type: precision_at_10 value: 12.822
    • type: precision_at_100 value: 1.506
    • type: precision_at_1000 value: 0.156
    • type: precision_at_3 value: 34.903
    • type: precision_at_5 value: 23.16
    • type: recall_at_1 value: 64.917
    • type: recall_at_10 value: 92.27199999999999
    • type: recall_at_100 value: 98.715
    • type: recall_at_1000 value: 99.854
    • type: recall_at_3 value: 82.04599999999999
    • type: recall_at_5 value: 87.2 task: type: Retrieval
  • dataset: config: default name: MTEB SCIDOCS-PL revision: 45452b03f05560207ef19149545f168e596c9337 split: test type: clarin-knext/scidocs-pl metrics:
    • type: map_at_1 value: 3.51
    • type: map_at_10 value: 9.046999999999999
    • type: map_at_100 value: 10.823
    • type: map_at_1000 value: 11.144
    • type: map_at_3 value: 6.257
    • type: map_at_5 value: 7.648000000000001
    • type: mrr_at_1 value: 17.299999999999997
    • type: mrr_at_10 value: 27.419
    • type: mrr_at_100 value: 28.618
    • type: mrr_at_1000 value: 28.685
    • type: mrr_at_3 value: 23.817
    • type: mrr_at_5 value: 25.927
    • type: ndcg_at_1 value: 17.299999999999997
    • type: ndcg_at_10 value: 16.084
    • type: ndcg_at_100 value: 23.729
    • type: ndcg_at_1000 value: 29.476999999999997
    • type: ndcg_at_3 value: 14.327000000000002
    • type: ndcg_at_5 value: 13.017999999999999
    • type: precision_at_1 value: 17.299999999999997
    • type: precision_at_10 value: 8.63
    • type: precision_at_100 value: 1.981
    • type: precision_at_1000 value: 0.336
    • type: precision_at_3 value: 13.4
    • type: precision_at_5 value: 11.700000000000001
    • type: recall_at_1 value: 3.51
    • type: recall_at_10 value: 17.518
    • type: recall_at_100 value: 40.275
    • type: recall_at_1000 value: 68.203
    • type: recall_at_3 value: 8.155
    • type: recall_at_5 value: 11.875 task: type: Retrieval
  • dataset: config: default name: MTEB SICK-E-PL revision: None split: test type: PL-MTEB/sicke-pl-pairclassification metrics:
    • type: cos_sim_accuracy value: 86.30248675091724
    • type: cos_sim_ap value: 83.6756734006714
    • type: cos_sim_f1 value: 74.97367497367497
    • type: cos_sim_precision value: 73.91003460207612
    • type: cos_sim_recall value: 76.06837606837607
    • type: dot_accuracy value: 86.30248675091724
    • type: dot_ap value: 83.6756734006714
    • type: dot_f1 value: 74.97367497367497
    • type: dot_precision value: 73.91003460207612
    • type: dot_recall value: 76.06837606837607
    • type: euclidean_accuracy value: 86.30248675091724
    • type: euclidean_ap value: 83.67566984333091
    • type: euclidean_f1 value: 74.97367497367497
    • type: euclidean_precision value: 73.91003460207612
    • type: euclidean_recall value: 76.06837606837607
    • type: manhattan_accuracy value: 86.28210354667753
    • type: manhattan_ap value: 83.64216119130171
    • type: manhattan_f1 value: 74.92152075340078
    • type: manhattan_precision value: 73.4107997265892
    • type: manhattan_recall value: 76.49572649572649
    • type: max_accuracy value: 86.30248675091724
    • type: max_ap value: 83.6756734006714
    • type: max_f1 value: 74.97367497367497 task: type: PairClassification
  • dataset: config: default name: MTEB SICK-R-PL revision: None split: test type: PL-MTEB/sickr-pl-sts metrics:
    • type: cos_sim_pearson value: 82.23295940859121
    • type: cos_sim_spearman value: 78.89329160768719
    • type: euclidean_pearson value: 79.56019107076818
    • type: euclidean_spearman value: 78.89330209904084
    • type: manhattan_pearson value: 79.76098513973719
    • type: manhattan_spearman value: 79.05490162570123 task: type: STS
  • dataset: config: pl name: MTEB STS22 (pl) revision: eea2b4fe26a775864c896887d910b76a8098ad3f split: test type: mteb/sts22-crosslingual-sts metrics:
    • type: cos_sim_pearson value: 37.732606308062486
    • type: cos_sim_spearman value: 41.01645667030284
    • type: euclidean_pearson value: 26.61722556367085
    • type: euclidean_spearman value: 41.01645667030284
    • type: manhattan_pearson value: 26.60917378970807
    • type: manhattan_spearman value: 41.51335727617614 task: type: STS
  • dataset: config: default name: MTEB SciFact-PL revision: 47932a35f045ef8ed01ba82bf9ff67f6e109207e split: test type: clarin-knext/scifact-pl metrics:
    • type: map_at_1 value: 54.31700000000001
    • type: map_at_10 value: 65.564
    • type: map_at_100 value: 66.062
    • type: map_at_1000 value: 66.08699999999999
    • type: map_at_3 value: 62.592999999999996
    • type: map_at_5 value: 63.888
    • type: mrr_at_1 value: 56.99999999999999
    • type: mrr_at_10 value: 66.412
    • type: mrr_at_100 value: 66.85900000000001
    • type: mrr_at_1000 value: 66.88
    • type: mrr_at_3 value: 64.22200000000001
    • type: mrr_at_5 value: 65.206
    • type: ndcg_at_1 value: 56.99999999999999
    • type: ndcg_at_10 value: 70.577
    • type: ndcg_at_100 value: 72.879
    • type: ndcg_at_1000 value: 73.45
    • type: ndcg_at_3 value: 65.5
    • type: ndcg_at_5 value: 67.278
    • type: precision_at_1 value: 56.99999999999999
    • type: precision_at_10 value: 9.667
    • type: precision_at_100 value: 1.083
    • type: precision_at_1000 value: 0.11299999999999999
    • type: precision_at_3 value: 26.0
    • type: precision_at_5 value: 16.933
    • type: recall_at_1 value: 54.31700000000001
    • type: recall_at_10 value: 85.056
    • type: recall_at_100 value: 95.667
    • type: recall_at_1000 value: 100.0
    • type: recall_at_3 value: 71.0
    • type: recall_at_5 value: 75.672 task: type: Retrieval
  • dataset: config: default name: MTEB TRECCOVID-PL revision: 81bcb408f33366c2a20ac54adafad1ae7e877fdd split: test type: clarin-knext/trec-covid-pl metrics:
    • type: map_at_1 value: 0.245
    • type: map_at_10 value: 2.051
    • type: map_at_100 value: 12.009
    • type: map_at_1000 value: 27.448
    • type: map_at_3 value: 0.721
    • type: map_at_5 value: 1.13
    • type: mrr_at_1 value: 88.0
    • type: mrr_at_10 value: 93.0
    • type: mrr_at_100 value: 93.0
    • type: mrr_at_1000 value: 93.0
    • type: mrr_at_3 value: 93.0
    • type: mrr_at_5 value: 93.0
    • type: ndcg_at_1 value: 85.0
    • type: ndcg_at_10 value: 80.303
    • type: ndcg_at_100 value: 61.23499999999999
    • type: ndcg_at_1000 value: 52.978
    • type: ndcg_at_3 value: 84.419
    • type: ndcg_at_5 value: 82.976
    • type: precision_at_1 value: 88.0
    • type: precision_at_10 value: 83.39999999999999
    • type: precision_at_100 value: 61.96
    • type: precision_at_1000 value: 22.648
    • type: precision_at_3 value: 89.333
    • type: precision_at_5 value: 87.2
    • type: recall_at_1 value: 0.245
    • type: recall_at_10 value: 2.193
    • type: recall_at_100 value: 14.938
    • type: recall_at_1000 value: 48.563
    • type: recall_at_3 value: 0.738
    • type: recall_at_5 value: 1.173 task: type: Retrieval

gte-Qwen2-7B-instruct

gte-Qwen2-7B-instruct is the latest model in the gte (General Text Embedding) model family that ranks No.1 in both English and Chinese evaluations on the Massive Text Embedding Benchmark MTEB benchmark (as of June 16, 2024).

Recently, the Qwen team released the Qwen2 series models, and we have trained the gte-Qwen2-7B-instruct model based on the Qwen2-7B LLM model. Compared to the gte-Qwen1.5-7B-instruct model, the gte-Qwen2-7B-instruct model uses the same training data and training strategies during the finetuning stage, with the only difference being the upgraded base model to Qwen2-7B. Considering the improvements in the Qwen2 series models compared to the Qwen1.5 series, we can also expect consistent performance enhancements in the embedding models.

The model incorporates several key advancements:

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

Model Information

• Model Size: 7B
• Embedding Dimension: 3584
• Max Input Tokens: 32k

Requirements

transformers>=4.39.2
flash_attn>=2.5.6

Usage

Sentence Transformers

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("Alibaba-NLP/gte-Qwen2-7B-instruct", trust_remote_code=True)
# In case you want to reduce the maximum length:
model.max_seq_length = 8192

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

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

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

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

Transformers

import torch
import torch.nn.functional as F

from torch import Tensor
from transformers import AutoTokenizer, AutoModel


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


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


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

tokenizer = AutoTokenizer.from_pretrained('Alibaba-NLP/gte-Qwen2-7B-instruct', trust_remote_code=True)
model = AutoModel.from_pretrained('Alibaba-NLP/gte-Qwen2-7B-instruct', trust_remote_code=True)

max_length = 8192

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

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

Evaluation

MTEB & C-MTEB

You can use the scripts/eval_mteb.py to reproduce the following result of gte-Qwen2-7B-instruct on MTEB(English)/C-MTEB(Chinese):

GTE Models

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

Cloud API Services

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

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

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

Citation

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

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