RichardErkhov/

Alibaba-NLP_-_gte-Qwen2-7B-instruct-4bits

• Model creator: https://huggingface.co/Alibaba-NLP/
• Original model: https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct/

Original model description:

• 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

• 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

Transformers

Evaluation

MTEB & C-MTEB

GTE Models

Cloud API Services

• 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.

Citation

@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}
}