sentence-transformers gguf mteb transformers Qwen2 sentence-similarity llama-cpp gguf-my-repo base_model:Alibaba-NLP/gte-Qwen2-1.5B-instruct base_model:quantized:Alibaba-NLP/gte-Qwen2-1.5B-instruct license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us conversational
---
base_model: Alibaba-NLP/gte-Qwen2-1.5B-instruct
license: apache-2.0
tags:
- mteb
- sentence-transformers
- transformers
- Qwen2
- sentence-similarity
- llama-cpp
- gguf-my-repo
model-index:
- name: gte-qwen2-7B-instruct
  results:
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonCounterfactualClassification (en)
      type: mteb/amazon_counterfactual
      config: en
      split: test
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
    metrics:
    - type: accuracy
      value: 83.98507462686567
    - type: ap
      value: 50.93015252587014
    - type: f1
      value: 78.50416599051215
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonPolarityClassification
      type: mteb/amazon_polarity
      config: default
      split: test
      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
    metrics:
    - type: accuracy
      value: 96.61065
    - type: ap
      value: 94.89174052954196
    - type: f1
      value: 96.60942596940565
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonReviewsClassification (en)
      type: mteb/amazon_reviews_multi
      config: en
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 55.614000000000004
    - type: f1
      value: 54.90553480294904
  - task:
      type: Retrieval
    dataset:
      name: MTEB ArguAna
      type: mteb/arguana
      config: default
      split: test
      revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
    metrics:
    - type: map_at_1
      value: 45.164
    - type: map_at_10
      value: 61.519
    - type: map_at_100
      value: 61.769
    - type: map_at_1000
      value: 61.769
    - type: map_at_3
      value: 57.443999999999996
    - type: map_at_5
      value: 60.058
    - type: mrr_at_1
      value: 46.088
    - type: mrr_at_10
      value: 61.861
    - type: mrr_at_100
      value: 62.117999999999995
    - type: mrr_at_1000
      value: 62.117999999999995
    - type: mrr_at_3
      value: 57.729
    - type: mrr_at_5
      value: 60.392
    - type: ndcg_at_1
      value: 45.164
    - type: ndcg_at_10
      value: 69.72
    - type: ndcg_at_100
      value: 70.719
    - type: ndcg_at_1000
      value: 70.719
    - type: ndcg_at_3
      value: 61.517999999999994
    - type: ndcg_at_5
      value: 66.247
    - type: precision_at_1
      value: 45.164
    - type: precision_at_10
      value: 9.545
    - type: precision_at_100
      value: 0.996
    - type: precision_at_1000
      value: 0.1
    - type: precision_at_3
      value: 24.443
    - type: precision_at_5
      value: 16.97
    - type: recall_at_1
      value: 45.164
    - type: recall_at_10
      value: 95.448
    - type: recall_at_100
      value: 99.644
    - type: recall_at_1000
      value: 99.644
    - type: recall_at_3
      value: 73.329
    - type: recall_at_5
      value: 84.851
  - task:
      type: Clustering
    dataset:
      name: MTEB ArxivClusteringP2P
      type: mteb/arxiv-clustering-p2p
      config: default
      split: test
      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
    metrics:
    - type: v_measure
      value: 50.511868162026175
  - task:
      type: Clustering
    dataset:
      name: MTEB ArxivClusteringS2S
      type: mteb/arxiv-clustering-s2s
      config: default
      split: test
      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
    metrics:
    - type: v_measure
      value: 45.007803189284004
  - task:
      type: Reranking
    dataset:
      name: MTEB AskUbuntuDupQuestions
      type: mteb/askubuntudupquestions-reranking
      config: default
      split: test
      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
    metrics:
    - type: map
      value: 64.55292107723382
    - type: mrr
      value: 77.66158818097877
  - task:
      type: STS
    dataset:
      name: MTEB BIOSSES
      type: mteb/biosses-sts
      config: default
      split: test
      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
    metrics:
    - type: cos_sim_pearson
      value: 85.65459047085452
    - type: cos_sim_spearman
      value: 82.10729255710761
    - type: euclidean_pearson
      value: 82.78079159312476
    - type: euclidean_spearman
      value: 80.50002701880933
    - type: manhattan_pearson
      value: 82.41372641383016
    - type: manhattan_spearman
      value: 80.57412509272639
  - task:
      type: Classification
    dataset:
      name: MTEB Banking77Classification
      type: mteb/banking77
      config: default
      split: test
      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
    metrics:
    - type: accuracy
      value: 87.30844155844156
    - type: f1
      value: 87.25307322443255
  - task:
      type: Clustering
    dataset:
      name: MTEB BiorxivClusteringP2P
      type: mteb/biorxiv-clustering-p2p
      config: default
      split: test
      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
    metrics:
    - type: v_measure
      value: 43.20754608934859
  - task:
      type: Clustering
    dataset:
      name: MTEB BiorxivClusteringS2S
      type: mteb/biorxiv-clustering-s2s
      config: default
      split: test
      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
    metrics:
    - type: v_measure
      value: 38.818037697335505
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackAndroidRetrieval
      type: BeIR/cqadupstack
      config: default
      split: test
      revision: f46a197baaae43b4f621051089b82a364682dfeb
    metrics:
    - type: map_at_1
      value: 35.423
    - type: map_at_10
      value: 47.198
    - type: map_at_100
      value: 48.899
    - type: map_at_1000
      value: 49.004
    - type: map_at_3
      value: 43.114999999999995
    - type: map_at_5
      value: 45.491
    - type: mrr_at_1
      value: 42.918
    - type: mrr_at_10
      value: 53.299
    - type: mrr_at_100
      value: 54.032000000000004
    - type: mrr_at_1000
      value: 54.055
    - type: mrr_at_3
      value: 50.453
    - type: mrr_at_5
      value: 52.205999999999996
    - type: ndcg_at_1
      value: 42.918
    - type: ndcg_at_10
      value: 53.98
    - type: ndcg_at_100
      value: 59.57
    - type: ndcg_at_1000
      value: 60.879000000000005
    - type: ndcg_at_3
      value: 48.224000000000004
    - type: ndcg_at_5
      value: 50.998
    - type: precision_at_1
      value: 42.918
    - type: precision_at_10
      value: 10.299999999999999
    - type: precision_at_100
      value: 1.687
    - type: precision_at_1000
      value: 0.211
    - type: precision_at_3
      value: 22.842000000000002
    - type: precision_at_5
      value: 16.681
    - type: recall_at_1
      value: 35.423
    - type: recall_at_10
      value: 66.824
    - type: recall_at_100
      value: 89.564
    - type: recall_at_1000
      value: 97.501
    - type: recall_at_3
      value: 50.365
    - type: recall_at_5
      value: 57.921
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackEnglishRetrieval
      type: BeIR/cqadupstack
      config: default
      split: test
      revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
    metrics:
    - type: map_at_1
      value: 33.205
    - type: map_at_10
      value: 44.859
    - type: map_at_100
      value: 46.135
    - type: map_at_1000
      value: 46.259
    - type: map_at_3
      value: 41.839
    - type: map_at_5
      value: 43.662
    - type: mrr_at_1
      value: 41.146
    - type: mrr_at_10
      value: 50.621
    - type: mrr_at_100
      value: 51.207
    - type: mrr_at_1000
      value: 51.246
    - type: mrr_at_3
      value: 48.535000000000004
    - type: mrr_at_5
      value: 49.818
    - type: ndcg_at_1
      value: 41.146
    - type: ndcg_at_10
      value: 50.683
    - type: ndcg_at_100
      value: 54.82
    - type: ndcg_at_1000
      value: 56.69
    - type: ndcg_at_3
      value: 46.611000000000004
    - type: ndcg_at_5
      value: 48.66
    - type: precision_at_1
      value: 41.146
    - type: precision_at_10
      value: 9.439
    - type: precision_at_100
      value: 1.465
    - type: precision_at_1000
      value: 0.194
    - type: precision_at_3
      value: 22.59
    - type: precision_at_5
      value: 15.86
    - type: recall_at_1
      value: 33.205
    - type: recall_at_10
      value: 61.028999999999996
    - type: recall_at_100
      value: 78.152
    - type: recall_at_1000
      value: 89.59700000000001
    - type: recall_at_3
      value: 49.05
    - type: recall_at_5
      value: 54.836
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackGamingRetrieval
      type: BeIR/cqadupstack
      config: default
      split: test
      revision: 4885aa143210c98657558c04aaf3dc47cfb54340
    metrics:
    - type: map_at_1
      value: 41.637
    - type: map_at_10
      value: 55.162
    - type: map_at_100
      value: 56.142
    - type: map_at_1000
      value: 56.188
    - type: map_at_3
      value: 51.564
    - type: map_at_5
      value: 53.696
    - type: mrr_at_1
      value: 47.524
    - type: mrr_at_10
      value: 58.243
    - type: mrr_at_100
      value: 58.879999999999995
    - type: mrr_at_1000
      value: 58.9
    - type: mrr_at_3
      value: 55.69499999999999
    - type: mrr_at_5
      value: 57.284
    - type: ndcg_at_1
      value: 47.524
    - type: ndcg_at_10
      value: 61.305
    - type: ndcg_at_100
      value: 65.077
    - type: ndcg_at_1000
      value: 65.941
    - type: ndcg_at_3
      value: 55.422000000000004
    - type: ndcg_at_5
      value: 58.516
    - type: precision_at_1
      value: 47.524
    - type: precision_at_10
      value: 9.918000000000001
    - type: precision_at_100
      value: 1.276
    - type: precision_at_1000
      value: 0.13899999999999998
    - type: precision_at_3
      value: 24.765
    - type: precision_at_5
      value: 17.204
    - type: recall_at_1
      value: 41.637
    - type: recall_at_10
      value: 76.185
    - type: recall_at_100
      value: 92.149
    - type: recall_at_1000
      value: 98.199
    - type: recall_at_3
      value: 60.856
    - type: recall_at_5
      value: 68.25099999999999
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackGisRetrieval
      type: BeIR/cqadupstack
      config: default
      split: test
      revision: 5003b3064772da1887988e05400cf3806fe491f2
    metrics:
    - type: map_at_1
      value: 26.27
    - type: map_at_10
      value: 37.463
    - type: map_at_100
      value: 38.434000000000005
    - type: map_at_1000
      value: 38.509
    - type: map_at_3
      value: 34.226
    - type: map_at_5
      value: 36.161
    - type: mrr_at_1
      value: 28.588
    - type: mrr_at_10
      value: 39.383
    - type: mrr_at_100
      value: 40.23
    - type: mrr_at_1000
      value: 40.281
    - type: mrr_at_3
      value: 36.422
    - type: mrr_at_5
      value: 38.252
    - type: ndcg_at_1
      value: 28.588
    - type: ndcg_at_10
      value: 43.511
    - type: ndcg_at_100
      value: 48.274
    - type: ndcg_at_1000
      value: 49.975
    - type: ndcg_at_3
      value: 37.319
    - type: ndcg_at_5
      value: 40.568
    - type: precision_at_1
      value: 28.588
    - type: precision_at_10
      value: 6.893000000000001
    - type: precision_at_100
      value: 0.9900000000000001
    - type: precision_at_1000
      value: 0.117
    - type: precision_at_3
      value: 16.347
    - type: precision_at_5
      value: 11.661000000000001
    - type: recall_at_1
      value: 26.27
    - type: recall_at_10
      value: 60.284000000000006
    - type: recall_at_100
      value: 81.902
    - type: recall_at_1000
      value: 94.43
    - type: recall_at_3
      value: 43.537
    - type: recall_at_5
      value: 51.475
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackMathematicaRetrieval
      type: BeIR/cqadupstack
      config: default
      split: test
      revision: 90fceea13679c63fe563ded68f3b6f06e50061de
    metrics:
    - type: map_at_1
      value: 18.168
    - type: map_at_10
      value: 28.410000000000004
    - type: map_at_100
      value: 29.78
    - type: map_at_1000
      value: 29.892999999999997
    - type: map_at_3
      value: 25.238
    - type: map_at_5
      value: 26.96
    - type: mrr_at_1
      value: 23.507
    - type: mrr_at_10
      value: 33.382
    - type: mrr_at_100
      value: 34.404
    - type: mrr_at_1000
      value: 34.467999999999996
    - type: mrr_at_3
      value: 30.637999999999998
    - type: mrr_at_5
      value: 32.199
    - type: ndcg_at_1
      value: 23.507
    - type: ndcg_at_10
      value: 34.571000000000005
    - type: ndcg_at_100
      value: 40.663
    - type: ndcg_at_1000
      value: 43.236000000000004
    - type: ndcg_at_3
      value: 29.053
    - type: ndcg_at_5
      value: 31.563999999999997
    - type: precision_at_1
      value: 23.507
    - type: precision_at_10
      value: 6.654
    - type: precision_at_100
      value: 1.113
    - type: precision_at_1000
      value: 0.146
    - type: precision_at_3
      value: 14.427999999999999
    - type: precision_at_5
      value: 10.498000000000001
    - type: recall_at_1
      value: 18.168
    - type: recall_at_10
      value: 48.443000000000005
    - type: recall_at_100
      value: 74.47
    - type: recall_at_1000
      value: 92.494
    - type: recall_at_3
      value: 33.379999999999995
    - type: recall_at_5
      value: 39.76
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackPhysicsRetrieval
      type: BeIR/cqadupstack
      config: default
      split: test
      revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
    metrics:
    - type: map_at_1
      value: 32.39
    - type: map_at_10
      value: 44.479
    - type: map_at_100
      value: 45.977000000000004
    - type: map_at_1000
      value: 46.087
    - type: map_at_3
      value: 40.976
    - type: map_at_5
      value: 43.038
    - type: mrr_at_1
      value: 40.135
    - type: mrr_at_10
      value: 50.160000000000004
    - type: mrr_at_100
      value: 51.052
    - type: mrr_at_1000
      value: 51.087
    - type: mrr_at_3
      value: 47.818
    - type: mrr_at_5
      value: 49.171
    - type: ndcg_at_1
      value: 40.135
    - type: ndcg_at_10
      value: 50.731
    - type: ndcg_at_100
      value: 56.452000000000005
    - type: ndcg_at_1000
      value: 58.123000000000005
    - type: ndcg_at_3
      value: 45.507
    - type: ndcg_at_5
      value: 48.11
    - type: precision_at_1
      value: 40.135
    - type: precision_at_10
      value: 9.192
    - type: precision_at_100
      value: 1.397
    - type: precision_at_1000
      value: 0.169
    - type: precision_at_3
      value: 21.816
    - type: precision_at_5
      value: 15.476
    - type: recall_at_1
      value: 32.39
    - type: recall_at_10
      value: 63.597
    - type: recall_at_100
      value: 86.737
    - type: recall_at_1000
      value: 97.039
    - type: recall_at_3
      value: 48.906
    - type: recall_at_5
      value: 55.659000000000006
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackProgrammersRetrieval
      type: BeIR/cqadupstack
      config: default
      split: test
      revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
    metrics:
    - type: map_at_1
      value: 28.397
    - type: map_at_10
      value: 39.871
    - type: map_at_100
      value: 41.309000000000005
    - type: map_at_1000
      value: 41.409
    - type: map_at_3
      value: 36.047000000000004
    - type: map_at_5
      value: 38.104
    - type: mrr_at_1
      value: 34.703
    - type: mrr_at_10
      value: 44.773
    - type: mrr_at_100
      value: 45.64
    - type: mrr_at_1000
      value: 45.678999999999995
    - type: mrr_at_3
      value: 41.705
    - type: mrr_at_5
      value: 43.406
    - type: ndcg_at_1
      value: 34.703
    - type: ndcg_at_10
      value: 46.271
    - type: ndcg_at_100
      value: 52.037
    - type: ndcg_at_1000
      value: 53.81700000000001
    - type: ndcg_at_3
      value: 39.966
    - type: ndcg_at_5
      value: 42.801
    - type: precision_at_1
      value: 34.703
    - type: precision_at_10
      value: 8.744
    - type: precision_at_100
      value: 1.348
    - type: precision_at_1000
      value: 0.167
    - type: precision_at_3
      value: 19.102
    - type: precision_at_5
      value: 13.836
    - type: recall_at_1
      value: 28.397
    - type: recall_at_10
      value: 60.299
    - type: recall_at_100
      value: 84.595
    - type: recall_at_1000
      value: 96.155
    - type: recall_at_3
      value: 43.065
    - type: recall_at_5
      value: 50.371
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackRetrieval
      type: BeIR/cqadupstack
      config: default
      split: test
      revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
    metrics:
    - type: map_at_1
      value: 28.044333333333338
    - type: map_at_10
      value: 38.78691666666666
    - type: map_at_100
      value: 40.113
    - type: map_at_1000
      value: 40.22125
    - type: map_at_3
      value: 35.52966666666667
    - type: map_at_5
      value: 37.372749999999996
    - type: mrr_at_1
      value: 33.159083333333335
    - type: mrr_at_10
      value: 42.913583333333335
    - type: mrr_at_100
      value: 43.7845
    - type: mrr_at_1000
      value: 43.830333333333336
    - type: mrr_at_3
      value: 40.29816666666667
    - type: mrr_at_5
      value: 41.81366666666667
    - type: ndcg_at_1
      value: 33.159083333333335
    - type: ndcg_at_10
      value: 44.75750000000001
    - type: ndcg_at_100
      value: 50.13658333333334
    - type: ndcg_at_1000
      value: 52.037
    - type: ndcg_at_3
      value: 39.34258333333334
    - type: ndcg_at_5
      value: 41.93708333333333
    - type: precision_at_1
      value: 33.159083333333335
    - type: precision_at_10
      value: 7.952416666666667
    - type: precision_at_100
      value: 1.2571666666666668
    - type: precision_at_1000
      value: 0.16099999999999998
    - type: precision_at_3
      value: 18.303833333333337
    - type: precision_at_5
      value: 13.057083333333333
    - type: recall_at_1
      value: 28.044333333333338
    - type: recall_at_10
      value: 58.237249999999996
    - type: recall_at_100
      value: 81.35391666666666
    - type: recall_at_1000
      value: 94.21283333333334
    - type: recall_at_3
      value: 43.32341666666667
    - type: recall_at_5
      value: 49.94908333333333
    - type: map_at_1
      value: 18.398
    - type: map_at_10
      value: 27.929
    - type: map_at_100
      value: 29.032999999999998
    - type: map_at_1000
      value: 29.126
    - type: map_at_3
      value: 25.070999999999998
    - type: map_at_5
      value: 26.583000000000002
    - type: mrr_at_1
      value: 19.963
    - type: mrr_at_10
      value: 29.997
    - type: mrr_at_100
      value: 30.9
    - type: mrr_at_1000
      value: 30.972
    - type: mrr_at_3
      value: 27.264
    - type: mrr_at_5
      value: 28.826
    - type: ndcg_at_1
      value: 19.963
    - type: ndcg_at_10
      value: 33.678999999999995
    - type: ndcg_at_100
      value: 38.931
    - type: ndcg_at_1000
      value: 41.379
    - type: ndcg_at_3
      value: 28.000000000000004
    - type: ndcg_at_5
      value: 30.637999999999998
    - type: precision_at_1
      value: 19.963
    - type: precision_at_10
      value: 5.7299999999999995
    - type: precision_at_100
      value: 0.902
    - type: precision_at_1000
      value: 0.122
    - type: precision_at_3
      value: 12.631
    - type: precision_at_5
      value: 9.057
    - type: recall_at_1
      value: 18.398
    - type: recall_at_10
      value: 49.254
    - type: recall_at_100
      value: 73.182
    - type: recall_at_1000
      value: 91.637
    - type: recall_at_3
      value: 34.06
    - type: recall_at_5
      value: 40.416000000000004
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackStatsRetrieval
      type: BeIR/cqadupstack
      config: default
      split: test
      revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
    metrics:
    - type: map_at_1
      value: 27.838
    - type: map_at_10
      value: 36.04
    - type: map_at_100
      value: 37.113
    - type: map_at_1000
      value: 37.204
    - type: map_at_3
      value: 33.585
    - type: map_at_5
      value: 34.845
    - type: mrr_at_1
      value: 30.982
    - type: mrr_at_10
      value: 39.105000000000004
    - type: mrr_at_100
      value: 39.98
    - type: mrr_at_1000
      value: 40.042
    - type: mrr_at_3
      value: 36.912
    - type: mrr_at_5
      value: 38.062000000000005
    - type: ndcg_at_1
      value: 30.982
    - type: ndcg_at_10
      value: 40.982
    - type: ndcg_at_100
      value: 46.092
    - type: ndcg_at_1000
      value: 48.25
    - type: ndcg_at_3
      value: 36.41
    - type: ndcg_at_5
      value: 38.379999999999995
    - type: precision_at_1
      value: 30.982
    - type: precision_at_10
      value: 6.534
    - type: precision_at_100
      value: 0.9820000000000001
    - type: precision_at_1000
      value: 0.124
    - type: precision_at_3
      value: 15.745999999999999
    - type: precision_at_5
      value: 10.828
    - type: recall_at_1
      value: 27.838
    - type: recall_at_10
      value: 52.971000000000004
    - type: recall_at_100
      value: 76.357
    - type: recall_at_1000
      value: 91.973
    - type: recall_at_3
      value: 40.157
    - type: recall_at_5
      value: 45.147999999999996
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackTexRetrieval
      type: BeIR/cqadupstack
      config: default
      split: test
      revision: 46989137a86843e03a6195de44b09deda022eec7
    metrics:
    - type: map_at_1
      value: 19.059
    - type: map_at_10
      value: 27.454
    - type: map_at_100
      value: 28.736
    - type: map_at_1000
      value: 28.865000000000002
    - type: map_at_3
      value: 24.773999999999997
    - type: map_at_5
      value: 26.266000000000002
    - type: mrr_at_1
      value: 23.125
    - type: mrr_at_10
      value: 31.267
    - type: mrr_at_100
      value: 32.32
    - type: mrr_at_1000
      value: 32.394
    - type: mrr_at_3
      value: 28.894
    - type: mrr_at_5
      value: 30.281000000000002
    - type: ndcg_at_1
      value: 23.125
    - type: ndcg_at_10
      value: 32.588
    - type: ndcg_at_100
      value: 38.432
    - type: ndcg_at_1000
      value: 41.214
    - type: ndcg_at_3
      value: 27.938000000000002
    - type: ndcg_at_5
      value: 30.127
    - type: precision_at_1
      value: 23.125
    - type: precision_at_10
      value: 5.9639999999999995
    - type: precision_at_100
      value: 1.047
    - type: precision_at_1000
      value: 0.148
    - type: precision_at_3
      value: 13.294
    - type: precision_at_5
      value: 9.628
    - type: recall_at_1
      value: 19.059
    - type: recall_at_10
      value: 44.25
    - type: recall_at_100
      value: 69.948
    - type: recall_at_1000
      value: 89.35300000000001
    - type: recall_at_3
      value: 31.114000000000004
    - type: recall_at_5
      value: 36.846000000000004
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackUnixRetrieval
      type: BeIR/cqadupstack
      config: default
      split: test
      revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
    metrics:
    - type: map_at_1
      value: 28.355999999999998
    - type: map_at_10
      value: 39.055
    - type: map_at_100
      value: 40.486
    - type: map_at_1000
      value: 40.571
    - type: map_at_3
      value: 35.69
    - type: map_at_5
      value: 37.605
    - type: mrr_at_1
      value: 33.302
    - type: mrr_at_10
      value: 42.986000000000004
    - type: mrr_at_100
      value: 43.957
    - type: mrr_at_1000
      value: 43.996
    - type: mrr_at_3
      value: 40.111999999999995
    - type: mrr_at_5
      value: 41.735
    - type: ndcg_at_1
      value: 33.302
    - type: ndcg_at_10
      value: 44.962999999999994
    - type: ndcg_at_100
      value: 50.917
    - type: ndcg_at_1000
      value: 52.622
    - type: ndcg_at_3
      value: 39.182
    - type: ndcg_at_5
      value: 41.939
    - type: precision_at_1
      value: 33.302
    - type: precision_at_10
      value: 7.779999999999999
    - type: precision_at_100
      value: 1.203
    - type: precision_at_1000
      value: 0.145
    - type: precision_at_3
      value: 18.035
    - type: precision_at_5
      value: 12.873000000000001
    - type: recall_at_1
      value: 28.355999999999998
    - type: recall_at_10
      value: 58.782000000000004
    - type: recall_at_100
      value: 84.02199999999999
    - type: recall_at_1000
      value: 95.511
    - type: recall_at_3
      value: 43.126999999999995
    - type: recall_at_5
      value: 50.14999999999999
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackWebmastersRetrieval
      type: BeIR/cqadupstack
      config: default
      split: test
      revision: 160c094312a0e1facb97e55eeddb698c0abe3571
    metrics:
    - type: map_at_1
      value: 27.391
    - type: map_at_10
      value: 37.523
    - type: map_at_100
      value: 39.312000000000005
    - type: map_at_1000
      value: 39.54
    - type: map_at_3
      value: 34.231
    - type: map_at_5
      value: 36.062
    - type: mrr_at_1
      value: 32.016
    - type: mrr_at_10
      value: 41.747
    - type: mrr_at_100
      value: 42.812
    - type: mrr_at_1000
      value: 42.844
    - type: mrr_at_3
      value: 39.129999999999995
    - type: mrr_at_5
      value: 40.524
    - type: ndcg_at_1
      value: 32.016
    - type: ndcg_at_10
      value: 43.826
    - type: ndcg_at_100
      value: 50.373999999999995
    - type: ndcg_at_1000
      value: 52.318
    - type: ndcg_at_3
      value: 38.479
    - type: ndcg_at_5
      value: 40.944
    - type: precision_at_1
      value: 32.016
    - type: precision_at_10
      value: 8.280999999999999
    - type: precision_at_100
      value: 1.6760000000000002
    - type: precision_at_1000
      value: 0.25
    - type: precision_at_3
      value: 18.05
    - type: precision_at_5
      value: 13.083
    - type: recall_at_1
      value: 27.391
    - type: recall_at_10
      value: 56.928999999999995
    - type: recall_at_100
      value: 85.169
    - type: recall_at_1000
      value: 96.665
    - type: recall_at_3
      value: 42.264
    - type: recall_at_5
      value: 48.556
  - task:
      type: Retrieval
    dataset:
      name: MTEB ClimateFEVER
      type: mteb/climate-fever
      config: default
      split: test
      revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
    metrics:
    - type: map_at_1
      value: 19.681
    - type: map_at_10
      value: 32.741
    - type: map_at_100
      value: 34.811
    - type: map_at_1000
      value: 35.003
    - type: map_at_3
      value: 27.697
    - type: map_at_5
      value: 30.372
    - type: mrr_at_1
      value: 44.951
    - type: mrr_at_10
      value: 56.34400000000001
    - type: mrr_at_100
      value: 56.961
    - type: mrr_at_1000
      value: 56.987
    - type: mrr_at_3
      value: 53.681
    - type: mrr_at_5
      value: 55.407
    - type: ndcg_at_1
      value: 44.951
    - type: ndcg_at_10
      value: 42.905
    - type: ndcg_at_100
      value: 49.95
    - type: ndcg_at_1000
      value: 52.917
    - type: ndcg_at_3
      value: 36.815
    - type: ndcg_at_5
      value: 38.817
    - type: precision_at_1
      value: 44.951
    - type: precision_at_10
      value: 12.989999999999998
    - type: precision_at_100
      value: 2.068
    - type: precision_at_1000
      value: 0.263
    - type: precision_at_3
      value: 27.275
    - type: precision_at_5
      value: 20.365
    - type: recall_at_1
      value: 19.681
    - type: recall_at_10
      value: 48.272999999999996
    - type: recall_at_100
      value: 71.87400000000001
    - type: recall_at_1000
      value: 87.929
    - type: recall_at_3
      value: 32.653999999999996
    - type: recall_at_5
      value: 39.364
  - task:
      type: Retrieval
    dataset:
      name: MTEB DBPedia
      type: mteb/dbpedia
      config: default
      split: test
      revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
    metrics:
    - type: map_at_1
      value: 10.231
    - type: map_at_10
      value: 22.338
    - type: map_at_100
      value: 31.927
    - type: map_at_1000
      value: 33.87
    - type: map_at_3
      value: 15.559999999999999
    - type: map_at_5
      value: 18.239
    - type: mrr_at_1
      value: 75.0
    - type: mrr_at_10
      value: 81.303
    - type: mrr_at_100
      value: 81.523
    - type: mrr_at_1000
      value: 81.53
    - type: mrr_at_3
      value: 80.083
    - type: mrr_at_5
      value: 80.758
    - type: ndcg_at_1
      value: 64.625
    - type: ndcg_at_10
      value: 48.687000000000005
    - type: ndcg_at_100
      value: 52.791
    - type: ndcg_at_1000
      value: 60.041999999999994
    - type: ndcg_at_3
      value: 53.757999999999996
    - type: ndcg_at_5
      value: 50.76500000000001
    - type: precision_at_1
      value: 75.0
    - type: precision_at_10
      value: 38.3
    - type: precision_at_100
      value: 12.025
    - type: precision_at_1000
      value: 2.3970000000000002
    - type: precision_at_3
      value: 55.417
    - type: precision_at_5
      value: 47.5
    - type: recall_at_1
      value: 10.231
    - type: recall_at_10
      value: 27.697
    - type: recall_at_100
      value: 57.409
    - type: recall_at_1000
      value: 80.547
    - type: recall_at_3
      value: 16.668
    - type: recall_at_5
      value: 20.552
  - task:
      type: Classification
    dataset:
      name: MTEB EmotionClassification
      type: mteb/emotion
      config: default
      split: test
      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
    metrics:
    - type: accuracy
      value: 61.365
    - type: f1
      value: 56.7540827912991
  - task:
      type: Retrieval
    dataset:
      name: MTEB FEVER
      type: mteb/fever
      config: default
      split: test
      revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
    metrics:
    - type: map_at_1
      value: 83.479
    - type: map_at_10
      value: 88.898
    - type: map_at_100
      value: 89.11
    - type: map_at_1000
      value: 89.12400000000001
    - type: map_at_3
      value: 88.103
    - type: map_at_5
      value: 88.629
    - type: mrr_at_1
      value: 89.934
    - type: mrr_at_10
      value: 93.91000000000001
    - type: mrr_at_100
      value: 93.937
    - type: mrr_at_1000
      value: 93.938
    - type: mrr_at_3
      value: 93.62700000000001
    - type: mrr_at_5
      value: 93.84599999999999
    - type: ndcg_at_1
      value: 89.934
    - type: ndcg_at_10
      value: 91.574
    - type: ndcg_at_100
      value: 92.238
    - type: ndcg_at_1000
      value: 92.45
    - type: ndcg_at_3
      value: 90.586
    - type: ndcg_at_5
      value: 91.16300000000001
    - type: precision_at_1
      value: 89.934
    - type: precision_at_10
      value: 10.555
    - type: precision_at_100
      value: 1.1159999999999999
    - type: precision_at_1000
      value: 0.11499999999999999
    - type: precision_at_3
      value: 33.588
    - type: precision_at_5
      value: 20.642
    - type: recall_at_1
      value: 83.479
    - type: recall_at_10
      value: 94.971
    - type: recall_at_100
      value: 97.397
    - type: recall_at_1000
      value: 98.666
    - type: recall_at_3
      value: 92.24799999999999
    - type: recall_at_5
      value: 93.797
  - task:
      type: Retrieval
    dataset:
      name: MTEB FiQA2018
      type: mteb/fiqa
      config: default
      split: test
      revision: 27a168819829fe9bcd655c2df245fb19452e8e06
    metrics:
    - type: map_at_1
      value: 27.16
    - type: map_at_10
      value: 45.593
    - type: map_at_100
      value: 47.762
    - type: map_at_1000
      value: 47.899
    - type: map_at_3
      value: 39.237
    - type: map_at_5
      value: 42.970000000000006
    - type: mrr_at_1
      value: 52.623
    - type: mrr_at_10
      value: 62.637
    - type: mrr_at_100
      value: 63.169
    - type: mrr_at_1000
      value: 63.185
    - type: mrr_at_3
      value: 59.928000000000004
    - type: mrr_at_5
      value: 61.702999999999996
    - type: ndcg_at_1
      value: 52.623
    - type: ndcg_at_10
      value: 54.701
    - type: ndcg_at_100
      value: 61.263
    - type: ndcg_at_1000
      value: 63.134
    - type: ndcg_at_3
      value: 49.265
    - type: ndcg_at_5
      value: 51.665000000000006
    - type: precision_at_1
      value: 52.623
    - type: precision_at_10
      value: 15.185
    - type: precision_at_100
      value: 2.202
    - type: precision_at_1000
      value: 0.254
    - type: precision_at_3
      value: 32.767
    - type: precision_at_5
      value: 24.722
    - type: recall_at_1
      value: 27.16
    - type: recall_at_10
      value: 63.309000000000005
    - type: recall_at_100
      value: 86.722
    - type: recall_at_1000
      value: 97.505
    - type: recall_at_3
      value: 45.045
    - type: recall_at_5
      value: 54.02400000000001
  - task:
      type: Retrieval
    dataset:
      name: MTEB HotpotQA
      type: mteb/hotpotqa
      config: default
      split: test
      revision: ab518f4d6fcca38d87c25209f94beba119d02014
    metrics:
    - type: map_at_1
      value: 42.573
    - type: map_at_10
      value: 59.373
    - type: map_at_100
      value: 60.292
    - type: map_at_1000
      value: 60.358999999999995
    - type: map_at_3
      value: 56.159000000000006
    - type: map_at_5
      value: 58.123999999999995
    - type: mrr_at_1
      value: 85.14500000000001
    - type: mrr_at_10
      value: 89.25999999999999
    - type: mrr_at_100
      value: 89.373
    - type: mrr_at_1000
      value: 89.377
    - type: mrr_at_3
      value: 88.618
    - type: mrr_at_5
      value: 89.036
    - type: ndcg_at_1
      value: 85.14500000000001
    - type: ndcg_at_10
      value: 68.95
    - type: ndcg_at_100
      value: 71.95
    - type: ndcg_at_1000
      value: 73.232
    - type: ndcg_at_3
      value: 64.546
    - type: ndcg_at_5
      value: 66.945
    - type: precision_at_1
      value: 85.14500000000001
    - type: precision_at_10
      value: 13.865
    - type: precision_at_100
      value: 1.619
    - type: precision_at_1000
      value: 0.179
    - type: precision_at_3
      value: 39.703
    - type: precision_at_5
      value: 25.718000000000004
    - type: recall_at_1
      value: 42.573
    - type: recall_at_10
      value: 69.325
    - type: recall_at_100
      value: 80.932
    - type: recall_at_1000
      value: 89.446
    - type: recall_at_3
      value: 59.553999999999995
    - type: recall_at_5
      value: 64.294
  - task:
      type: Classification
    dataset:
      name: MTEB ImdbClassification
      type: mteb/imdb
      config: default
      split: test
      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
    metrics:
    - type: accuracy
      value: 95.8336
    - type: ap
      value: 93.78862962194073
    - type: f1
      value: 95.83192650728371
  - task:
      type: Retrieval
    dataset:
      name: MTEB MSMARCO
      type: mteb/msmarco
      config: default
      split: dev
      revision: c5a29a104738b98a9e76336939199e264163d4a0
    metrics:
    - type: map_at_1
      value: 23.075000000000003
    - type: map_at_10
      value: 36.102000000000004
    - type: map_at_100
      value: 37.257
    - type: map_at_1000
      value: 37.3
    - type: map_at_3
      value: 32.144
    - type: map_at_5
      value: 34.359
    - type: mrr_at_1
      value: 23.711
    - type: mrr_at_10
      value: 36.671
    - type: mrr_at_100
      value: 37.763999999999996
    - type: mrr_at_1000
      value: 37.801
    - type: mrr_at_3
      value: 32.775
    - type: mrr_at_5
      value: 34.977000000000004
    - type: ndcg_at_1
      value: 23.711
    - type: ndcg_at_10
      value: 43.361
    - type: ndcg_at_100
      value: 48.839
    - type: ndcg_at_1000
      value: 49.88
    - type: ndcg_at_3
      value: 35.269
    - type: ndcg_at_5
      value: 39.224
    - type: precision_at_1
      value: 23.711
    - type: precision_at_10
      value: 6.866999999999999
    - type: precision_at_100
      value: 0.96
    - type: precision_at_1000
      value: 0.105
    - type: precision_at_3
      value: 15.096000000000002
    - type: precision_at_5
      value: 11.083
    - type: recall_at_1
      value: 23.075000000000003
    - type: recall_at_10
      value: 65.756
    - type: recall_at_100
      value: 90.88199999999999
    - type: recall_at_1000
      value: 98.739
    - type: recall_at_3
      value: 43.691
    - type: recall_at_5
      value: 53.15800000000001
  - task:
      type: Classification
    dataset:
      name: MTEB MTOPDomainClassification (en)
      type: mteb/mtop_domain
      config: en
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
    - type: accuracy
      value: 97.69493844049248
    - type: f1
      value: 97.55048089616261
  - task:
      type: Classification
    dataset:
      name: MTEB MTOPIntentClassification (en)
      type: mteb/mtop_intent
      config: en
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 88.75968992248062
    - type: f1
      value: 72.26321223399123
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (en)
      type: mteb/amazon_massive_intent
      config: en
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 82.40080699394754
    - type: f1
      value: 79.62590029057968
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveScenarioClassification (en)
      type: mteb/amazon_massive_scenario
      config: en
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 84.49562878278414
    - type: f1
      value: 84.0040193313333
  - task:
      type: Clustering
    dataset:
      name: MTEB MedrxivClusteringP2P
      type: mteb/medrxiv-clustering-p2p
      config: default
      split: test
      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
    metrics:
    - type: v_measure
      value: 39.386760057101945
  - task:
      type: Clustering
    dataset:
      name: MTEB MedrxivClusteringS2S
      type: mteb/medrxiv-clustering-s2s
      config: default
      split: test
      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
    metrics:
    - type: v_measure
      value: 37.89687154075537
  - task:
      type: Reranking
    dataset:
      name: MTEB MindSmallReranking
      type: mteb/mind_small
      config: default
      split: test
      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
    metrics:
    - type: map
      value: 33.94151656057482
    - type: mrr
      value: 35.32684700746953
  - task:
      type: Retrieval
    dataset:
      name: MTEB NFCorpus
      type: mteb/nfcorpus
      config: default
      split: test
      revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
    metrics:
    - type: map_at_1
      value: 6.239999999999999
    - type: map_at_10
      value: 14.862
    - type: map_at_100
      value: 18.955
    - type: map_at_1000
      value: 20.694000000000003
    - type: map_at_3
      value: 10.683
    - type: map_at_5
      value: 12.674
    - type: mrr_at_1
      value: 50.15500000000001
    - type: mrr_at_10
      value: 59.697
    - type: mrr_at_100
      value: 60.095
    - type: mrr_at_1000
      value: 60.129999999999995
    - type: mrr_at_3
      value: 58.35900000000001
    - type: mrr_at_5
      value: 58.839
    - type: ndcg_at_1
      value: 48.452
    - type: ndcg_at_10
      value: 39.341
    - type: ndcg_at_100
      value: 35.866
    - type: ndcg_at_1000
      value: 45.111000000000004
    - type: ndcg_at_3
      value: 44.527
    - type: ndcg_at_5
      value: 42.946
    - type: precision_at_1
      value: 50.15500000000001
    - type: precision_at_10
      value: 29.536
    - type: precision_at_100
      value: 9.142
    - type: precision_at_1000
      value: 2.2849999999999997
    - type: precision_at_3
      value: 41.899
    - type: precision_at_5
      value: 37.647000000000006
    - type: recall_at_1
      value: 6.239999999999999
    - type: recall_at_10
      value: 19.278000000000002
    - type: recall_at_100
      value: 36.074
    - type: recall_at_1000
      value: 70.017
    - type: recall_at_3
      value: 12.066
    - type: recall_at_5
      value: 15.254000000000001
  - task:
      type: Retrieval
    dataset:
      name: MTEB NQ
      type: mteb/nq
      config: default
      split: test
      revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
    metrics:
    - type: map_at_1
      value: 39.75
    - type: map_at_10
      value: 56.443
    - type: map_at_100
      value: 57.233999999999995
    - type: map_at_1000
      value: 57.249
    - type: map_at_3
      value: 52.032999999999994
    - type: map_at_5
      value: 54.937999999999995
    - type: mrr_at_1
      value: 44.728
    - type: mrr_at_10
      value: 58.939
    - type: mrr_at_100
      value: 59.489000000000004
    - type: mrr_at_1000
      value: 59.499
    - type: mrr_at_3
      value: 55.711999999999996
    - type: mrr_at_5
      value: 57.89
    - type: ndcg_at_1
      value: 44.728
    - type: ndcg_at_10
      value: 63.998999999999995
    - type: ndcg_at_100
      value: 67.077
    - type: ndcg_at_1000
      value: 67.40899999999999
    - type: ndcg_at_3
      value: 56.266000000000005
    - type: ndcg_at_5
      value: 60.88
    - type: precision_at_1
      value: 44.728
    - type: precision_at_10
      value: 10.09
    - type: precision_at_100
      value: 1.1809999999999998
    - type: precision_at_1000
      value: 0.121
    - type: precision_at_3
      value: 25.145
    - type: precision_at_5
      value: 17.822
    - type: recall_at_1
      value: 39.75
    - type: recall_at_10
      value: 84.234
    - type: recall_at_100
      value: 97.055
    - type: recall_at_1000
      value: 99.517
    - type: recall_at_3
      value: 64.851
    - type: recall_at_5
      value: 75.343
  - task:
      type: Retrieval
    dataset:
      name: MTEB QuoraRetrieval
      type: mteb/quora
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 72.085
    - type: map_at_10
      value: 86.107
    - type: map_at_100
      value: 86.727
    - type: map_at_1000
      value: 86.74
    - type: map_at_3
      value: 83.21
    - type: map_at_5
      value: 85.06
    - type: mrr_at_1
      value: 82.94
    - type: mrr_at_10
      value: 88.845
    - type: mrr_at_100
      value: 88.926
    - type: mrr_at_1000
      value: 88.927
    - type: mrr_at_3
      value: 87.993
    - type: mrr_at_5
      value: 88.62299999999999
    - type: ndcg_at_1
      value: 82.97
    - type: ndcg_at_10
      value: 89.645
    - type: ndcg_at_100
      value: 90.717
    - type: ndcg_at_1000
      value: 90.78
    - type: ndcg_at_3
      value: 86.99900000000001
    - type: ndcg_at_5
      value: 88.52600000000001
    - type: precision_at_1
      value: 82.97
    - type: precision_at_10
      value: 13.569
    - type: precision_at_100
      value: 1.539
    - type: precision_at_1000
      value: 0.157
    - type: precision_at_3
      value: 38.043
    - type: precision_at_5
      value: 24.992
    - type: recall_at_1
      value: 72.085
    - type: recall_at_10
      value: 96.262
    - type: recall_at_100
      value: 99.77000000000001
    - type: recall_at_1000
      value: 99.997
    - type: recall_at_3
      value: 88.652
    - type: recall_at_5
      value: 93.01899999999999
  - task:
      type: Clustering
    dataset:
      name: MTEB RedditClustering
      type: mteb/reddit-clustering
      config: default
      split: test
      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
    metrics:
    - type: v_measure
      value: 55.82153952668092
  - task:
      type: Clustering
    dataset:
      name: MTEB RedditClusteringP2P
      type: mteb/reddit-clustering-p2p
      config: default
      split: test
      revision: 282350215ef01743dc01b456c7f5241fa8937f16
    metrics:
    - type: v_measure
      value: 62.094465801879295
  - task:
      type: Retrieval
    dataset:
      name: MTEB SCIDOCS
      type: mteb/scidocs
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 5.688
    - type: map_at_10
      value: 15.201999999999998
    - type: map_at_100
      value: 18.096
    - type: map_at_1000
      value: 18.481
    - type: map_at_3
      value: 10.734
    - type: map_at_5
      value: 12.94
    - type: mrr_at_1
      value: 28.000000000000004
    - type: mrr_at_10
      value: 41.101
    - type: mrr_at_100
      value: 42.202
    - type: mrr_at_1000
      value: 42.228
    - type: mrr_at_3
      value: 37.683
    - type: mrr_at_5
      value: 39.708
    - type: ndcg_at_1
      value: 28.000000000000004
    - type: ndcg_at_10
      value: 24.976000000000003
    - type: ndcg_at_100
      value: 35.129
    - type: ndcg_at_1000
      value: 40.77
    - type: ndcg_at_3
      value: 23.787
    - type: ndcg_at_5
      value: 20.816000000000003
    - type: precision_at_1
      value: 28.000000000000004
    - type: precision_at_10
      value: 13.04
    - type: precision_at_100
      value: 2.761
    - type: precision_at_1000
      value: 0.41000000000000003
    - type: precision_at_3
      value: 22.6
    - type: precision_at_5
      value: 18.52
    - type: recall_at_1
      value: 5.688
    - type: recall_at_10
      value: 26.43
    - type: recall_at_100
      value: 56.02
    - type: recall_at_1000
      value: 83.21
    - type: recall_at_3
      value: 13.752
    - type: recall_at_5
      value: 18.777
  - task:
      type: STS
    dataset:
      name: MTEB SICK-R
      type: mteb/sickr-sts
      config: default
      split: test
      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
    metrics:
    - type: cos_sim_pearson
      value: 85.15084859283178
    - type: cos_sim_spearman
      value: 80.49030614009419
    - type: euclidean_pearson
      value: 81.84574978672468
    - type: euclidean_spearman
      value: 79.89787150656818
    - type: manhattan_pearson
      value: 81.63076538567131
    - type: manhattan_spearman
      value: 79.69867352121841
  - task:
      type: STS
    dataset:
      name: MTEB STS12
      type: mteb/sts12-sts
      config: default
      split: test
      revision: a0d554a64d88156834ff5ae9920b964011b16384
    metrics:
    - type: cos_sim_pearson
      value: 84.64097921490992
    - type: cos_sim_spearman
      value: 77.25370084896514
    - type: euclidean_pearson
      value: 82.71210826468788
    - type: euclidean_spearman
      value: 78.50445584994826
    - type: manhattan_pearson
      value: 82.92580164330298
    - type: manhattan_spearman
      value: 78.69686891301019
  - task:
      type: STS
    dataset:
      name: MTEB STS13
      type: mteb/sts13-sts
      config: default
      split: test
      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
    metrics:
    - type: cos_sim_pearson
      value: 87.24596417308994
    - type: cos_sim_spearman
      value: 87.79454220555091
    - type: euclidean_pearson
      value: 87.40242561671164
    - type: euclidean_spearman
      value: 88.25955597373556
    - type: manhattan_pearson
      value: 87.25160240485849
    - type: manhattan_spearman
      value: 88.155794979818
  - task:
      type: STS
    dataset:
      name: MTEB STS14
      type: mteb/sts14-sts
      config: default
      split: test
      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
    metrics:
    - type: cos_sim_pearson
      value: 84.44914233422564
    - type: cos_sim_spearman
      value: 82.91015471820322
    - type: euclidean_pearson
      value: 84.7206656630327
    - type: euclidean_spearman
      value: 83.86408872059216
    - type: manhattan_pearson
      value: 84.72816725158454
    - type: manhattan_spearman
      value: 84.01603388572788
  - task:
      type: STS
    dataset:
      name: MTEB STS15
      type: mteb/sts15-sts
      config: default
      split: test
      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
    metrics:
    - type: cos_sim_pearson
      value: 87.6168026237477
    - type: cos_sim_spearman
      value: 88.45414278092397
    - type: euclidean_pearson
      value: 88.57023240882022
    - type: euclidean_spearman
      value: 89.04102190922094
    - type: manhattan_pearson
      value: 88.66695535796354
    - type: manhattan_spearman
      value: 89.19898476680969
  - task:
      type: STS
    dataset:
      name: MTEB STS16
      type: mteb/sts16-sts
      config: default
      split: test
      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
    metrics:
    - type: cos_sim_pearson
      value: 84.27925826089424
    - type: cos_sim_spearman
      value: 85.45291099550461
    - type: euclidean_pearson
      value: 83.63853036580834
    - type: euclidean_spearman
      value: 84.33468035821484
    - type: manhattan_pearson
      value: 83.72778773251596
    - type: manhattan_spearman
      value: 84.51583132445376
  - task:
      type: STS
    dataset:
      name: MTEB STS17 (en-en)
      type: mteb/sts17-crosslingual-sts
      config: en-en
      split: test
      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
    metrics:
    - type: cos_sim_pearson
      value: 89.67375185692552
    - type: cos_sim_spearman
      value: 90.32542469203855
    - type: euclidean_pearson
      value: 89.63513717951847
    - type: euclidean_spearman
      value: 89.87760271003745
    - type: manhattan_pearson
      value: 89.28381452982924
    - type: manhattan_spearman
      value: 89.53568197785721
  - task:
      type: STS
    dataset:
      name: MTEB STS22 (en)
      type: mteb/sts22-crosslingual-sts
      config: en
      split: test
      revision: eea2b4fe26a775864c896887d910b76a8098ad3f
    metrics:
    - type: cos_sim_pearson
      value: 66.24644693819846
    - type: cos_sim_spearman
      value: 66.09889420525377
    - type: euclidean_pearson
      value: 63.72551583520747
    - type: euclidean_spearman
      value: 63.01385470780679
    - type: manhattan_pearson
      value: 64.09258157214097
    - type: manhattan_spearman
      value: 63.080517752822594
  - task:
      type: STS
    dataset:
      name: MTEB STSBenchmark
      type: mteb/stsbenchmark-sts
      config: default
      split: test
      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
    metrics:
    - type: cos_sim_pearson
      value: 86.27321463839989
    - type: cos_sim_spearman
      value: 86.37572865993327
    - type: euclidean_pearson
      value: 86.36268020198149
    - type: euclidean_spearman
      value: 86.31089339478922
    - type: manhattan_pearson
      value: 86.4260445761947
    - type: manhattan_spearman
      value: 86.45885895320457
  - task:
      type: Reranking
    dataset:
      name: MTEB SciDocsRR
      type: mteb/scidocs-reranking
      config: default
      split: test
      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
    metrics:
    - type: map
      value: 86.52456702387798
    - type: mrr
      value: 96.34556529164372
  - task:
      type: Retrieval
    dataset:
      name: MTEB SciFact
      type: mteb/scifact
      config: default
      split: test
      revision: 0228b52cf27578f30900b9e5271d331663a030d7
    metrics:
    - type: map_at_1
      value: 61.99400000000001
    - type: map_at_10
      value: 73.38799999999999
    - type: map_at_100
      value: 73.747
    - type: map_at_1000
      value: 73.75
    - type: map_at_3
      value: 70.04599999999999
    - type: map_at_5
      value: 72.095
    - type: mrr_at_1
      value: 65.0
    - type: mrr_at_10
      value: 74.42800000000001
    - type: mrr_at_100
      value: 74.722
    - type: mrr_at_1000
      value: 74.725
    - type: mrr_at_3
      value: 72.056
    - type: mrr_at_5
      value: 73.60600000000001
    - type: ndcg_at_1
      value: 65.0
    - type: ndcg_at_10
      value: 78.435
    - type: ndcg_at_100
      value: 79.922
    - type: ndcg_at_1000
      value: 80.00500000000001
    - type: ndcg_at_3
      value: 73.05199999999999
    - type: ndcg_at_5
      value: 75.98
    - type: precision_at_1
      value: 65.0
    - type: precision_at_10
      value: 10.5
    - type: precision_at_100
      value: 1.123
    - type: precision_at_1000
      value: 0.11299999999999999
    - type: precision_at_3
      value: 28.555999999999997
    - type: precision_at_5
      value: 19.0
    - type: recall_at_1
      value: 61.99400000000001
    - type: recall_at_10
      value: 92.72200000000001
    - type: recall_at_100
      value: 99.333
    - type: recall_at_1000
      value: 100.0
    - type: recall_at_3
      value: 78.739
    - type: recall_at_5
      value: 85.828
  - task:
      type: PairClassification
    dataset:
      name: MTEB SprintDuplicateQuestions
      type: mteb/sprintduplicatequestions-pairclassification
      config: default
      split: test
      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
    metrics:
    - type: cos_sim_accuracy
      value: 99.79009900990098
    - type: cos_sim_ap
      value: 95.3203137438653
    - type: cos_sim_f1
      value: 89.12386706948641
    - type: cos_sim_precision
      value: 89.75659229208925
    - type: cos_sim_recall
      value: 88.5
    - type: dot_accuracy
      value: 99.67821782178218
    - type: dot_ap
      value: 89.94069840000675
    - type: dot_f1
      value: 83.45902463549521
    - type: dot_precision
      value: 83.9231547017189
    - type: dot_recall
      value: 83.0
    - type: euclidean_accuracy
      value: 99.78613861386138
    - type: euclidean_ap
      value: 95.10648259135526
    - type: euclidean_f1
      value: 88.77338877338877
    - type: euclidean_precision
      value: 92.42424242424242
    - type: euclidean_recall
      value: 85.39999999999999
    - type: manhattan_accuracy
      value: 99.7950495049505
    - type: manhattan_ap
      value: 95.29987661320946
    - type: manhattan_f1
      value: 89.21313183949972
    - type: manhattan_precision
      value: 93.14472252448314
    - type: manhattan_recall
      value: 85.6
    - type: max_accuracy
      value: 99.7950495049505
    - type: max_ap
      value: 95.3203137438653
    - type: max_f1
      value: 89.21313183949972
  - task:
      type: Clustering
    dataset:
      name: MTEB StackExchangeClustering
      type: mteb/stackexchange-clustering
      config: default
      split: test
      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
    metrics:
    - type: v_measure
      value: 67.65446577183913
  - task:
      type: Clustering
    dataset:
      name: MTEB StackExchangeClusteringP2P
      type: mteb/stackexchange-clustering-p2p
      config: default
      split: test
      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
    metrics:
    - type: v_measure
      value: 46.30749237193961
  - task:
      type: Reranking
    dataset:
      name: MTEB StackOverflowDupQuestions
      type: mteb/stackoverflowdupquestions-reranking
      config: default
      split: test
      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
    metrics:
    - type: map
      value: 54.91481849959949
    - type: mrr
      value: 55.853506175197346
  - task:
      type: Summarization
    dataset:
      name: MTEB SummEval
      type: mteb/summeval
      config: default
      split: test
      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
    metrics:
    - type: cos_sim_pearson
      value: 30.08196549170419
    - type: cos_sim_spearman
      value: 31.16661390597077
    - type: dot_pearson
      value: 29.892258410943466
    - type: dot_spearman
      value: 30.51328811965085
  - task:
      type: Retrieval
    dataset:
      name: MTEB TRECCOVID
      type: mteb/trec-covid
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 0.23900000000000002
    - type: map_at_10
      value: 2.173
    - type: map_at_100
      value: 14.24
    - type: map_at_1000
      value: 35.309000000000005
    - type: map_at_3
      value: 0.7100000000000001
    - type: map_at_5
      value: 1.163
    - type: mrr_at_1
      value: 92.0
    - type: mrr_at_10
      value: 96.0
    - type: mrr_at_100
      value: 96.0
    - type: mrr_at_1000
      value: 96.0
    - type: mrr_at_3
      value: 96.0
    - type: mrr_at_5
      value: 96.0
    - type: ndcg_at_1
      value: 90.0
    - type: ndcg_at_10
      value: 85.382
    - type: ndcg_at_100
      value: 68.03
    - type: ndcg_at_1000
      value: 61.021
    - type: ndcg_at_3
      value: 89.765
    - type: ndcg_at_5
      value: 88.444
    - type: precision_at_1
      value: 92.0
    - type: precision_at_10
      value: 88.0
    - type: precision_at_100
      value: 70.02000000000001
    - type: precision_at_1000
      value: 26.984
    - type: precision_at_3
      value: 94.0
    - type: precision_at_5
      value: 92.80000000000001
    - type: recall_at_1
      value: 0.23900000000000002
    - type: recall_at_10
      value: 2.313
    - type: recall_at_100
      value: 17.049
    - type: recall_at_1000
      value: 57.489999999999995
    - type: recall_at_3
      value: 0.737
    - type: recall_at_5
      value: 1.221
  - task:
      type: Retrieval
    dataset:
      name: MTEB Touche2020
      type: mteb/touche2020
      config: default
      split: test
      revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
    metrics:
    - type: map_at_1
      value: 2.75
    - type: map_at_10
      value: 11.29
    - type: map_at_100
      value: 18.032999999999998
    - type: map_at_1000
      value: 19.746
    - type: map_at_3
      value: 6.555
    - type: map_at_5
      value: 8.706999999999999
    - type: mrr_at_1
      value: 34.694
    - type: mrr_at_10
      value: 50.55
    - type: mrr_at_100
      value: 51.659
    - type: mrr_at_1000
      value: 51.659
    - type: mrr_at_3
      value: 47.278999999999996
    - type: mrr_at_5
      value: 49.728
    - type: ndcg_at_1
      value: 32.653
    - type: ndcg_at_10
      value: 27.894000000000002
    - type: ndcg_at_100
      value: 39.769
    - type: ndcg_at_1000
      value: 51.495999999999995
    - type: ndcg_at_3
      value: 32.954
    - type: ndcg_at_5
      value: 31.502999999999997
    - type: precision_at_1
      value: 34.694
    - type: precision_at_10
      value: 23.265
    - type: precision_at_100
      value: 7.898
    - type: precision_at_1000
      value: 1.58
    - type: precision_at_3
      value: 34.694
    - type: precision_at_5
      value: 31.429000000000002
    - type: recall_at_1
      value: 2.75
    - type: recall_at_10
      value: 16.953
    - type: recall_at_100
      value: 48.68
    - type: recall_at_1000
      value: 85.18599999999999
    - type: recall_at_3
      value: 7.710999999999999
    - type: recall_at_5
      value: 11.484
  - task:
      type: Classification
    dataset:
      name: MTEB ToxicConversationsClassification
      type: mteb/toxic_conversations_50k
      config: default
      split: test
      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
    metrics:
    - type: accuracy
      value: 82.66099999999999
    - type: ap
      value: 25.555698090238337
    - type: f1
      value: 66.48402012461622
  - task:
      type: Classification
    dataset:
      name: MTEB TweetSentimentExtractionClassification
      type: mteb/tweet_sentiment_extraction
      config: default
      split: test
      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
    metrics:
    - type: accuracy
      value: 72.94567062818335
    - type: f1
      value: 73.28139189595674
  - task:
      type: Clustering
    dataset:
      name: MTEB TwentyNewsgroupsClustering
      type: mteb/twentynewsgroups-clustering
      config: default
      split: test
      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
    metrics:
    - type: v_measure
      value: 49.581627240203474
  - task:
      type: PairClassification
    dataset:
      name: MTEB TwitterSemEval2015
      type: mteb/twittersemeval2015-pairclassification
      config: default
      split: test
      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
    metrics:
    - type: cos_sim_accuracy
      value: 87.78089050485785
    - type: cos_sim_ap
      value: 79.64487116574168
    - type: cos_sim_f1
      value: 72.46563021970964
    - type: cos_sim_precision
      value: 70.62359128474831
    - type: cos_sim_recall
      value: 74.40633245382587
    - type: dot_accuracy
      value: 86.2609524944865
    - type: dot_ap
      value: 75.513046857613
    - type: dot_f1
      value: 68.58213616489695
    - type: dot_precision
      value: 65.12455516014235
    - type: dot_recall
      value: 72.42744063324538
    - type: euclidean_accuracy
      value: 87.6080348095607
    - type: euclidean_ap
      value: 79.00204933649795
    - type: euclidean_f1
      value: 72.14495342605589
    - type: euclidean_precision
      value: 69.85421299728193
    - type: euclidean_recall
      value: 74.5910290237467
    - type: manhattan_accuracy
      value: 87.59611372712642
    - type: manhattan_ap
      value: 78.78523756706264
    - type: manhattan_f1
      value: 71.86499137718648
    - type: manhattan_precision
      value: 67.39833641404806
    - type: manhattan_recall
      value: 76.96569920844327
    - type: max_accuracy
      value: 87.78089050485785
    - type: max_ap
      value: 79.64487116574168
    - type: max_f1
      value: 72.46563021970964
  - task:
      type: PairClassification
    dataset:
      name: MTEB TwitterURLCorpus
      type: mteb/twitterurlcorpus-pairclassification
      config: default
      split: test
      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
    metrics:
    - type: cos_sim_accuracy
      value: 89.98719292117825
    - type: cos_sim_ap
      value: 87.58146137353202
    - type: cos_sim_f1
      value: 80.28543232369239
    - type: cos_sim_precision
      value: 79.1735289714029
    - type: cos_sim_recall
      value: 81.42901139513397
    - type: dot_accuracy
      value: 88.9199363526992
    - type: dot_ap
      value: 84.98499998630417
    - type: dot_f1
      value: 78.21951400757969
    - type: dot_precision
      value: 75.58523624874336
    - type: dot_recall
      value: 81.04404065291038
    - type: euclidean_accuracy
      value: 89.77374160748244
    - type: euclidean_ap
      value: 87.35151562835209
    - type: euclidean_f1
      value: 79.92160922940393
    - type: euclidean_precision
      value: 76.88531587933979
    - type: euclidean_recall
      value: 83.20757622420696
    - type: manhattan_accuracy
      value: 89.72717041176699
    - type: manhattan_ap
      value: 87.34065592142515
    - type: manhattan_f1
      value: 79.85603419187943
    - type: manhattan_precision
      value: 77.82243332115455
    - type: manhattan_recall
      value: 81.99876809362489
    - type: max_accuracy
      value: 89.98719292117825
    - type: max_ap
      value: 87.58146137353202
    - type: max_f1
      value: 80.28543232369239
  - task:
      type: STS
    dataset:
      name: MTEB AFQMC
      type: C-MTEB/AFQMC
      config: default
      split: validation
      revision: b44c3b011063adb25877c13823db83bb193913c4
    metrics:
    - type: cos_sim_pearson
      value: 53.45954203592337
    - type: cos_sim_spearman
      value: 58.42154680418638
    - type: euclidean_pearson
      value: 56.41543791722753
    - type: euclidean_spearman
      value: 58.39328016640146
    - type: manhattan_pearson
      value: 56.318510356833876
    - type: manhattan_spearman
      value: 58.28423447818184
  - task:
      type: STS
    dataset:
      name: MTEB ATEC
      type: C-MTEB/ATEC
      config: default
      split: test
      revision: 0f319b1142f28d00e055a6770f3f726ae9b7d865
    metrics:
    - type: cos_sim_pearson
      value: 50.78356460675945
    - type: cos_sim_spearman
      value: 55.6530411663269
    - type: euclidean_pearson
      value: 56.50763660417816
    - type: euclidean_spearman
      value: 55.733823335669065
    - type: manhattan_pearson
      value: 56.45323093512866
    - type: manhattan_spearman
      value: 55.63248619032702
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonReviewsClassification (zh)
      type: mteb/amazon_reviews_multi
      config: zh
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 47.209999999999994
    - type: f1
      value: 46.08892432018655
  - task:
      type: STS
    dataset:
      name: MTEB BQ
      type: C-MTEB/BQ
      config: default
      split: test
      revision: e3dda5e115e487b39ec7e618c0c6a29137052a55
    metrics:
    - type: cos_sim_pearson
      value: 70.25573992001478
    - type: cos_sim_spearman
      value: 73.85247134951433
    - type: euclidean_pearson
      value: 72.60033082168442
    - type: euclidean_spearman
      value: 73.72445893756499
    - type: manhattan_pearson
      value: 72.59932284620231
    - type: manhattan_spearman
      value: 73.68002490614583
  - task:
      type: Clustering
    dataset:
      name: MTEB CLSClusteringP2P
      type: C-MTEB/CLSClusteringP2P
      config: default
      split: test
      revision: 4b6227591c6c1a73bc76b1055f3b7f3588e72476
    metrics:
    - type: v_measure
      value: 45.21317724305628
  - task:
      type: Clustering
    dataset:
      name: MTEB CLSClusteringS2S
      type: C-MTEB/CLSClusteringS2S
      config: default
      split: test
      revision: e458b3f5414b62b7f9f83499ac1f5497ae2e869f
    metrics:
    - type: v_measure
      value: 42.49825170976724
  - task:
      type: Reranking
    dataset:
      name: MTEB CMedQAv1
      type: C-MTEB/CMedQAv1-reranking
      config: default
      split: test
      revision: 8d7f1e942507dac42dc58017c1a001c3717da7df
    metrics:
    - type: map
      value: 88.15661686810597
    - type: mrr
      value: 90.11222222222223
  - task:
      type: Reranking
    dataset:
      name: MTEB CMedQAv2
      type: C-MTEB/CMedQAv2-reranking
      config: default
      split: test
      revision: 23d186750531a14a0357ca22cd92d712fd512ea0
    metrics:
    - type: map
      value: 88.1204726064383
    - type: mrr
      value: 90.20142857142858
  - task:
      type: Retrieval
    dataset:
      name: MTEB CmedqaRetrieval
      type: C-MTEB/CmedqaRetrieval
      config: default
      split: dev
      revision: cd540c506dae1cf9e9a59c3e06f42030d54e7301
    metrics:
    - type: map_at_1
      value: 27.224999999999998
    - type: map_at_10
      value: 40.169
    - type: map_at_100
      value: 42.0
    - type: map_at_1000
      value: 42.109
    - type: map_at_3
      value: 35.76
    - type: map_at_5
      value: 38.221
    - type: mrr_at_1
      value: 40.56
    - type: mrr_at_10
      value: 49.118
    - type: mrr_at_100
      value: 50.092999999999996
    - type: mrr_at_1000
      value: 50.133
    - type: mrr_at_3
      value: 46.507
    - type: mrr_at_5
      value: 47.973
    - type: ndcg_at_1
      value: 40.56
    - type: ndcg_at_10
      value: 46.972
    - type: ndcg_at_100
      value: 54.04
    - type: ndcg_at_1000
      value: 55.862
    - type: ndcg_at_3
      value: 41.36
    - type: ndcg_at_5
      value: 43.704
    - type: precision_at_1
      value: 40.56
    - type: precision_at_10
      value: 10.302999999999999
    - type: precision_at_100
      value: 1.606
    - type: precision_at_1000
      value: 0.184
    - type: precision_at_3
      value: 23.064
    - type: precision_at_5
      value: 16.764000000000003
    - type: recall_at_1
      value: 27.224999999999998
    - type: recall_at_10
      value: 58.05200000000001
    - type: recall_at_100
      value: 87.092
    - type: recall_at_1000
      value: 99.099
    - type: recall_at_3
      value: 41.373
    - type: recall_at_5
      value: 48.453
  - task:
      type: PairClassification
    dataset:
      name: MTEB Cmnli
      type: C-MTEB/CMNLI
      config: default
      split: validation
      revision: 41bc36f332156f7adc9e38f53777c959b2ae9766
    metrics:
    - type: cos_sim_accuracy
      value: 77.40228502705953
    - type: cos_sim_ap
      value: 86.22359172956327
    - type: cos_sim_f1
      value: 78.96328293736501
    - type: cos_sim_precision
      value: 73.36945615091311
    - type: cos_sim_recall
      value: 85.48047696983868
    - type: dot_accuracy
      value: 75.53818400481059
    - type: dot_ap
      value: 83.70164011305312
    - type: dot_f1
      value: 77.67298719348754
    - type: dot_precision
      value: 67.49482401656314
    - type: dot_recall
      value: 91.46598082768296
    - type: euclidean_accuracy
      value: 77.94347564642213
    - type: euclidean_ap
      value: 86.4652108728609
    - type: euclidean_f1
      value: 79.15555555555555
    - type: euclidean_precision
      value: 75.41816641964853
    - type: euclidean_recall
      value: 83.28267477203647
    - type: manhattan_accuracy
      value: 77.45039085989175
    - type: manhattan_ap
      value: 86.09986583900665
    - type: manhattan_f1
      value: 78.93669264438988
    - type: manhattan_precision
      value: 72.63261296660117
    - type: manhattan_recall
      value: 86.43909282207154
    - type: max_accuracy
      value: 77.94347564642213
    - type: max_ap
      value: 86.4652108728609
    - type: max_f1
      value: 79.15555555555555
  - task:
      type: Retrieval
    dataset:
      name: MTEB CovidRetrieval
      type: C-MTEB/CovidRetrieval
      config: default
      split: dev
      revision: 1271c7809071a13532e05f25fb53511ffce77117
    metrics:
    - type: map_at_1
      value: 69.336
    - type: map_at_10
      value: 77.16
    - type: map_at_100
      value: 77.47500000000001
    - type: map_at_1000
      value: 77.482
    - type: map_at_3
      value: 75.42999999999999
    - type: map_at_5
      value: 76.468
    - type: mrr_at_1
      value: 69.44200000000001
    - type: mrr_at_10
      value: 77.132
    - type: mrr_at_100
      value: 77.43299999999999
    - type: mrr_at_1000
      value: 77.44
    - type: mrr_at_3
      value: 75.395
    - type: mrr_at_5
      value: 76.459
    - type: ndcg_at_1
      value: 69.547
    - type: ndcg_at_10
      value: 80.794
    - type: ndcg_at_100
      value: 82.245
    - type: ndcg_at_1000
      value: 82.40899999999999
    - type: ndcg_at_3
      value: 77.303
    - type: ndcg_at_5
      value: 79.168
    - type: precision_at_1
      value: 69.547
    - type: precision_at_10
      value: 9.305
    - type: precision_at_100
      value: 0.9979999999999999
    - type: precision_at_1000
      value: 0.101
    - type: precision_at_3
      value: 27.749000000000002
    - type: precision_at_5
      value: 17.576
    - type: recall_at_1
      value: 69.336
    - type: recall_at_10
      value: 92.097
    - type: recall_at_100
      value: 98.736
    - type: recall_at_1000
      value: 100.0
    - type: recall_at_3
      value: 82.64
    - type: recall_at_5
      value: 87.144
  - task:
      type: Retrieval
    dataset:
      name: MTEB DuRetrieval
      type: C-MTEB/DuRetrieval
      config: default
      split: dev
      revision: a1a333e290fe30b10f3f56498e3a0d911a693ced
    metrics:
    - type: map_at_1
      value: 26.817999999999998
    - type: map_at_10
      value: 82.67
    - type: map_at_100
      value: 85.304
    - type: map_at_1000
      value: 85.334
    - type: map_at_3
      value: 57.336
    - type: map_at_5
      value: 72.474
    - type: mrr_at_1
      value: 91.45
    - type: mrr_at_10
      value: 94.272
    - type: mrr_at_100
      value: 94.318
    - type: mrr_at_1000
      value: 94.32000000000001
    - type: mrr_at_3
      value: 94.0
    - type: mrr_at_5
      value: 94.17699999999999
    - type: ndcg_at_1
      value: 91.45
    - type: ndcg_at_10
      value: 89.404
    - type: ndcg_at_100
      value: 91.724
    - type: ndcg_at_1000
      value: 91.973
    - type: ndcg_at_3
      value: 88.104
    - type: ndcg_at_5
      value: 87.25699999999999
    - type: precision_at_1
      value: 91.45
    - type: precision_at_10
      value: 42.585
    - type: precision_at_100
      value: 4.838
    - type: precision_at_1000
      value: 0.49
    - type: precision_at_3
      value: 78.8
    - type: precision_at_5
      value: 66.66
    - type: recall_at_1
      value: 26.817999999999998
    - type: recall_at_10
      value: 90.67
    - type: recall_at_100
      value: 98.36200000000001
    - type: recall_at_1000
      value: 99.583
    - type: recall_at_3
      value: 59.614999999999995
    - type: recall_at_5
      value: 77.05199999999999
  - task:
      type: Retrieval
    dataset:
      name: MTEB EcomRetrieval
      type: C-MTEB/EcomRetrieval
      config: default
      split: dev
      revision: 687de13dc7294d6fd9be10c6945f9e8fec8166b9
    metrics:
    - type: map_at_1
      value: 47.699999999999996
    - type: map_at_10
      value: 57.589999999999996
    - type: map_at_100
      value: 58.226
    - type: map_at_1000
      value: 58.251
    - type: map_at_3
      value: 55.233
    - type: map_at_5
      value: 56.633
    - type: mrr_at_1
      value: 47.699999999999996
    - type: mrr_at_10
      value: 57.589999999999996
    - type: mrr_at_100
      value: 58.226
    - type: mrr_at_1000
      value: 58.251
    - type: mrr_at_3
      value: 55.233
    - type: mrr_at_5
      value: 56.633
    - type: ndcg_at_1
      value: 47.699999999999996
    - type: ndcg_at_10
      value: 62.505
    - type: ndcg_at_100
      value: 65.517
    - type: ndcg_at_1000
      value: 66.19800000000001
    - type: ndcg_at_3
      value: 57.643
    - type: ndcg_at_5
      value: 60.181
    - type: precision_at_1
      value: 47.699999999999996
    - type: precision_at_10
      value: 7.8
    - type: precision_at_100
      value: 0.919
    - type: precision_at_1000
      value: 0.097
    - type: precision_at_3
      value: 21.532999999999998
    - type: precision_at_5
      value: 14.16
    - type: recall_at_1
      value: 47.699999999999996
    - type: recall_at_10
      value: 78.0
    - type: recall_at_100
      value: 91.9
    - type: recall_at_1000
      value: 97.3
    - type: recall_at_3
      value: 64.60000000000001
    - type: recall_at_5
      value: 70.8
  - task:
      type: Classification
    dataset:
      name: MTEB IFlyTek
      type: C-MTEB/IFlyTek-classification
      config: default
      split: validation
      revision: 421605374b29664c5fc098418fe20ada9bd55f8a
    metrics:
    - type: accuracy
      value: 44.84801846864178
    - type: f1
      value: 37.47347897956339
  - task:
      type: Classification
    dataset:
      name: MTEB JDReview
      type: C-MTEB/JDReview-classification
      config: default
      split: test
      revision: b7c64bd89eb87f8ded463478346f76731f07bf8b
    metrics:
    - type: accuracy
      value: 85.81613508442777
    - type: ap
      value: 52.68244615477374
    - type: f1
      value: 80.0445640948843
  - task:
      type: STS
    dataset:
      name: MTEB LCQMC
      type: C-MTEB/LCQMC
      config: default
      split: test
      revision: 17f9b096f80380fce5ed12a9be8be7784b337daf
    metrics:
    - type: cos_sim_pearson
      value: 69.57786502217138
    - type: cos_sim_spearman
      value: 75.39106054489906
    - type: euclidean_pearson
      value: 73.72082954602402
    - type: euclidean_spearman
      value: 75.14421475913619
    - type: manhattan_pearson
      value: 73.62463076633642
    - type: manhattan_spearman
      value: 75.01301565104112
  - task:
      type: Reranking
    dataset:
      name: MTEB MMarcoReranking
      type: C-MTEB/Mmarco-reranking
      config: default
      split: dev
      revision: None
    metrics:
    - type: map
      value: 29.143797057999134
    - type: mrr
      value: 28.08174603174603
  - task:
      type: Retrieval
    dataset:
      name: MTEB MMarcoRetrieval
      type: C-MTEB/MMarcoRetrieval
      config: default
      split: dev
      revision: 539bbde593d947e2a124ba72651aafc09eb33fc2
    metrics:
    - type: map_at_1
      value: 70.492
    - type: map_at_10
      value: 79.501
    - type: map_at_100
      value: 79.728
    - type: map_at_1000
      value: 79.735
    - type: map_at_3
      value: 77.77
    - type: map_at_5
      value: 78.851
    - type: mrr_at_1
      value: 72.822
    - type: mrr_at_10
      value: 80.001
    - type: mrr_at_100
      value: 80.19
    - type: mrr_at_1000
      value: 80.197
    - type: mrr_at_3
      value: 78.484
    - type: mrr_at_5
      value: 79.42099999999999
    - type: ndcg_at_1
      value: 72.822
    - type: ndcg_at_10
      value: 83.013
    - type: ndcg_at_100
      value: 84.013
    - type: ndcg_at_1000
      value: 84.20400000000001
    - type: ndcg_at_3
      value: 79.728
    - type: ndcg_at_5
      value: 81.542
    - type: precision_at_1
      value: 72.822
    - type: precision_at_10
      value: 9.917
    - type: precision_at_100
      value: 1.042
    - type: precision_at_1000
      value: 0.106
    - type: precision_at_3
      value: 29.847
    - type: precision_at_5
      value: 18.871
    - type: recall_at_1
      value: 70.492
    - type: recall_at_10
      value: 93.325
    - type: recall_at_100
      value: 97.822
    - type: recall_at_1000
      value: 99.319
    - type: recall_at_3
      value: 84.636
    - type: recall_at_5
      value: 88.93100000000001
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (zh-CN)
      type: mteb/amazon_massive_intent
      config: zh-CN
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 76.88298587760592
    - type: f1
      value: 73.89001762017176
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveScenarioClassification (zh-CN)
      type: mteb/amazon_massive_scenario
      config: zh-CN
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 80.76328177538669
    - type: f1
      value: 80.24718532423358
  - task:
      type: Retrieval
    dataset:
      name: MTEB MedicalRetrieval
      type: C-MTEB/MedicalRetrieval
      config: default
      split: dev
      revision: 2039188fb5800a9803ba5048df7b76e6fb151fc6
    metrics:
    - type: map_at_1
      value: 49.6
    - type: map_at_10
      value: 55.620999999999995
    - type: map_at_100
      value: 56.204
    - type: map_at_1000
      value: 56.251
    - type: map_at_3
      value: 54.132999999999996
    - type: map_at_5
      value: 54.933
    - type: mrr_at_1
      value: 49.7
    - type: mrr_at_10
      value: 55.67100000000001
    - type: mrr_at_100
      value: 56.254000000000005
    - type: mrr_at_1000
      value: 56.301
    - type: mrr_at_3
      value: 54.18300000000001
    - type: mrr_at_5
      value: 54.983000000000004
    - type: ndcg_at_1
      value: 49.6
    - type: ndcg_at_10
      value: 58.645
    - type: ndcg_at_100
      value: 61.789
    - type: ndcg_at_1000
      value: 63.219
    - type: ndcg_at_3
      value: 55.567
    - type: ndcg_at_5
      value: 57.008
    - type: precision_at_1
      value: 49.6
    - type: precision_at_10
      value: 6.819999999999999
    - type: precision_at_100
      value: 0.836
    - type: precision_at_1000
      value: 0.095
    - type: precision_at_3
      value: 19.900000000000002
    - type: precision_at_5
      value: 12.64
    - type: recall_at_1
      value: 49.6
    - type: recall_at_10
      value: 68.2
    - type: recall_at_100
      value: 83.6
    - type: recall_at_1000
      value: 95.3
    - type: recall_at_3
      value: 59.699999999999996
    - type: recall_at_5
      value: 63.2
  - task:
      type: Classification
    dataset:
      name: MTEB MultilingualSentiment
      type: C-MTEB/MultilingualSentiment-classification
      config: default
      split: validation
      revision: 46958b007a63fdbf239b7672c25d0bea67b5ea1a
    metrics:
    - type: accuracy
      value: 74.45666666666666
    - type: f1
      value: 74.32582402190089
  - task:
      type: PairClassification
    dataset:
      name: MTEB Ocnli
      type: C-MTEB/OCNLI
      config: default
      split: validation
      revision: 66e76a618a34d6d565d5538088562851e6daa7ec
    metrics:
    - type: cos_sim_accuracy
      value: 80.67135896047645
    - type: cos_sim_ap
      value: 87.60421240712051
    - type: cos_sim_f1
      value: 82.1304131408661
    - type: cos_sim_precision
      value: 77.68361581920904
    - type: cos_sim_recall
      value: 87.11721224920802
    - type: dot_accuracy
      value: 79.04710341093666
    - type: dot_ap
      value: 85.6370059719336
    - type: dot_f1
      value: 80.763723150358
    - type: dot_precision
      value: 73.69337979094077
    - type: dot_recall
      value: 89.33474128827878
    - type: euclidean_accuracy
      value: 81.05035192203573
    - type: euclidean_ap
      value: 87.7880240053663
    - type: euclidean_f1
      value: 82.50244379276637
    - type: euclidean_precision
      value: 76.7970882620564
    - type: euclidean_recall
      value: 89.1235480464625
    - type: manhattan_accuracy
      value: 80.61721710882512
    - type: manhattan_ap
      value: 87.43568120591175
    - type: manhattan_f1
      value: 81.89526184538653
    - type: manhattan_precision
      value: 77.5992438563327
    - type: manhattan_recall
      value: 86.6948257655755
    - type: max_accuracy
      value: 81.05035192203573
    - type: max_ap
      value: 87.7880240053663
    - type: max_f1
      value: 82.50244379276637
  - task:
      type: Classification
    dataset:
      name: MTEB OnlineShopping
      type: C-MTEB/OnlineShopping-classification
      config: default
      split: test
      revision: e610f2ebd179a8fda30ae534c3878750a96db120
    metrics:
    - type: accuracy
      value: 93.5
    - type: ap
      value: 91.31357903446782
    - type: f1
      value: 93.48088994006616
  - task:
      type: STS
    dataset:
      name: MTEB PAWSX
      type: C-MTEB/PAWSX
      config: default
      split: test
      revision: 9c6a90e430ac22b5779fb019a23e820b11a8b5e1
    metrics:
    - type: cos_sim_pearson
      value: 36.93293453538077
    - type: cos_sim_spearman
      value: 42.45972506308574
    - type: euclidean_pearson
      value: 42.34945133152159
    - type: euclidean_spearman
      value: 42.331610303674644
    - type: manhattan_pearson
      value: 42.31455070249498
    - type: manhattan_spearman
      value: 42.19887982891834
  - task:
      type: STS
    dataset:
      name: MTEB QBQTC
      type: C-MTEB/QBQTC
      config: default
      split: test
      revision: 790b0510dc52b1553e8c49f3d2afb48c0e5c48b7
    metrics:
    - type: cos_sim_pearson
      value: 33.683290790043785
    - type: cos_sim_spearman
      value: 35.149171171202994
    - type: euclidean_pearson
      value: 32.33806561267862
    - type: euclidean_spearman
      value: 34.483576387347966
    - type: manhattan_pearson
      value: 32.47629754599608
    - type: manhattan_spearman
      value: 34.66434471867615
  - task:
      type: STS
    dataset:
      name: MTEB STS22 (zh)
      type: mteb/sts22-crosslingual-sts
      config: zh
      split: test
      revision: eea2b4fe26a775864c896887d910b76a8098ad3f
    metrics:
    - type: cos_sim_pearson
      value: 66.46322760516104
    - type: cos_sim_spearman
      value: 67.398478319726
    - type: euclidean_pearson
      value: 64.7223480293625
    - type: euclidean_spearman
      value: 66.83118568812951
    - type: manhattan_pearson
      value: 64.88440039828305
    - type: manhattan_spearman
      value: 66.80429458952257
  - task:
      type: STS
    dataset:
      name: MTEB STSB
      type: C-MTEB/STSB
      config: default
      split: test
      revision: 0cde68302b3541bb8b3c340dc0644b0b745b3dc0
    metrics:
    - type: cos_sim_pearson
      value: 79.08991383232105
    - type: cos_sim_spearman
      value: 79.39715677296854
    - type: euclidean_pearson
      value: 78.63201279320496
    - type: euclidean_spearman
      value: 79.40262660785731
    - type: manhattan_pearson
      value: 78.98138363146906
    - type: manhattan_spearman
      value: 79.79968413014194
  - task:
      type: Reranking
    dataset:
      name: MTEB T2Reranking
      type: C-MTEB/T2Reranking
      config: default
      split: dev
      revision: 76631901a18387f85eaa53e5450019b87ad58ef9
    metrics:
    - type: map
      value: 67.43289278789972
    - type: mrr
      value: 77.53012460908535
  - task:
      type: Retrieval
    dataset:
      name: MTEB T2Retrieval
      type: C-MTEB/T2Retrieval
      config: default
      split: dev
      revision: 8731a845f1bf500a4f111cf1070785c793d10e64
    metrics:
    - type: map_at_1
      value: 27.733999999999998
    - type: map_at_10
      value: 78.24799999999999
    - type: map_at_100
      value: 81.765
    - type: map_at_1000
      value: 81.824
    - type: map_at_3
      value: 54.92
    - type: map_at_5
      value: 67.61399999999999
    - type: mrr_at_1
      value: 90.527
    - type: mrr_at_10
      value: 92.843
    - type: mrr_at_100
      value: 92.927
    - type: mrr_at_1000
      value: 92.93
    - type: mrr_at_3
      value: 92.45100000000001
    - type: mrr_at_5
      value: 92.693
    - type: ndcg_at_1
      value: 90.527
    - type: ndcg_at_10
      value: 85.466
    - type: ndcg_at_100
      value: 88.846
    - type: ndcg_at_1000
      value: 89.415
    - type: ndcg_at_3
      value: 86.768
    - type: ndcg_at_5
      value: 85.46000000000001
    - type: precision_at_1
      value: 90.527
    - type: precision_at_10
      value: 42.488
    - type: precision_at_100
      value: 5.024
    - type: precision_at_1000
      value: 0.516
    - type: precision_at_3
      value: 75.907
    - type: precision_at_5
      value: 63.727000000000004
    - type: recall_at_1
      value: 27.733999999999998
    - type: recall_at_10
      value: 84.346
    - type: recall_at_100
      value: 95.536
    - type: recall_at_1000
      value: 98.42999999999999
    - type: recall_at_3
      value: 56.455
    - type: recall_at_5
      value: 70.755
  - task:
      type: Classification
    dataset:
      name: MTEB TNews
      type: C-MTEB/TNews-classification
      config: default
      split: validation
      revision: 317f262bf1e6126357bbe89e875451e4b0938fe4
    metrics:
    - type: accuracy
      value: 49.952000000000005
    - type: f1
      value: 48.264617195258054
  - task:
      type: Clustering
    dataset:
      name: MTEB ThuNewsClusteringP2P
      type: C-MTEB/ThuNewsClusteringP2P
      config: default
      split: test
      revision: 5798586b105c0434e4f0fe5e767abe619442cf93
    metrics:
    - type: v_measure
      value: 68.23769904483508
  - task:
      type: Clustering
    dataset:
      name: MTEB ThuNewsClusteringS2S
      type: C-MTEB/ThuNewsClusteringS2S
      config: default
      split: test
      revision: 8a8b2caeda43f39e13c4bc5bea0f8a667896e10d
    metrics:
    - type: v_measure
      value: 62.50294403136556
  - task:
      type: Retrieval
    dataset:
      name: MTEB VideoRetrieval
      type: C-MTEB/VideoRetrieval
      config: default
      split: dev
      revision: 58c2597a5943a2ba48f4668c3b90d796283c5639
    metrics:
    - type: map_at_1
      value: 54.0
    - type: map_at_10
      value: 63.668
    - type: map_at_100
      value: 64.217
    - type: map_at_1000
      value: 64.23100000000001
    - type: map_at_3
      value: 61.7
    - type: map_at_5
      value: 62.870000000000005
    - type: mrr_at_1
      value: 54.0
    - type: mrr_at_10
      value: 63.668
    - type: mrr_at_100
      value: 64.217
    - type: mrr_at_1000
      value: 64.23100000000001
    - type: mrr_at_3
      value: 61.7
    - type: mrr_at_5
      value: 62.870000000000005
    - type: ndcg_at_1
      value: 54.0
    - type: ndcg_at_10
      value: 68.11399999999999
    - type: ndcg_at_100
      value: 70.723
    - type: ndcg_at_1000
      value: 71.123
    - type: ndcg_at_3
      value: 64.074
    - type: ndcg_at_5
      value: 66.178
    - type: precision_at_1
      value: 54.0
    - type: precision_at_10
      value: 8.200000000000001
    - type: precision_at_100
      value: 0.941
    - type: precision_at_1000
      value: 0.097
    - type: precision_at_3
      value: 23.633000000000003
    - type: precision_at_5
      value: 15.2
    - type: recall_at_1
      value: 54.0
    - type: recall_at_10
      value: 82.0
    - type: recall_at_100
      value: 94.1
    - type: recall_at_1000
      value: 97.3
    - type: recall_at_3
      value: 70.89999999999999
    - type: recall_at_5
      value: 76.0
  - task:
      type: Classification
    dataset:
      name: MTEB Waimai
      type: C-MTEB/waimai-classification
      config: default
      split: test
      revision: 339287def212450dcaa9df8c22bf93e9980c7023
    metrics:
    - type: accuracy
      value: 86.63000000000001
    - type: ap
      value: 69.99457882599567
    - type: f1
      value: 85.07735617998541
  - task:
      type: Clustering
    dataset:
      name: MTEB 8TagsClustering
      type: PL-MTEB/8tags-clustering
      config: default
      split: test
      revision: None
    metrics:
    - type: v_measure
      value: 44.594104491193555
  - task:
      type: Classification
    dataset:
      name: MTEB AllegroReviews
      type: PL-MTEB/allegro-reviews
      config: default
      split: test
      revision: None
    metrics:
    - type: accuracy
      value: 63.97614314115309
    - type: f1
      value: 52.15634261679283
  - task:
      type: Retrieval
    dataset:
      name: MTEB ArguAna-PL
      type: clarin-knext/arguana-pl
      config: default
      split: test
      revision: 63fc86750af76253e8c760fc9e534bbf24d260a2
    metrics:
    - type: map_at_1
      value: 32.646
    - type: map_at_10
      value: 47.963
    - type: map_at_100
      value: 48.789
    - type: map_at_1000
      value: 48.797000000000004
    - type: map_at_3
      value: 43.196
    - type: map_at_5
      value: 46.016
    - type: mrr_at_1
      value: 33.073
    - type: mrr_at_10
      value: 48.126000000000005
    - type: mrr_at_100
      value: 48.946
    - type: mrr_at_1000
      value: 48.953
    - type: mrr_at_3
      value: 43.374
    - type: mrr_at_5
      value: 46.147
    - type: ndcg_at_1
      value: 32.646
    - type: ndcg_at_10
      value: 56.481
    - type: ndcg_at_100
      value: 59.922
    - type: ndcg_at_1000
      value: 60.07
    - type: ndcg_at_3
      value: 46.675
    - type: ndcg_at_5
      value: 51.76500000000001
    - type: precision_at_1
      value: 32.646
    - type: precision_at_10
      value: 8.371
    - type: precision_at_100
      value: 0.9860000000000001
    - type: precision_at_1000
      value: 0.1
    - type: precision_at_3
      value: 18.919
    - type: precision_at_5
      value: 13.825999999999999
    - type: recall_at_1
      value: 32.646
    - type: recall_at_10
      value: 83.71300000000001
    - type: recall_at_100
      value: 98.578
    - type: recall_at_1000
      value: 99.644
    - type: recall_at_3
      value: 56.757000000000005
    - type: recall_at_5
      value: 69.132
  - task:
      type: Classification
    dataset:
      name: MTEB CBD
      type: PL-MTEB/cbd
      config: default
      split: test
      revision: None
    metrics:
    - type: accuracy
      value: 68.56
    - type: ap
      value: 23.310493680488513
    - type: f1
      value: 58.85369533105693
  - task:
      type: PairClassification
    dataset:
      name: MTEB CDSC-E
      type: PL-MTEB/cdsce-pairclassification
      config: default
      split: test
      revision: None
    metrics:
    - type: cos_sim_accuracy
      value: 88.5
    - type: cos_sim_ap
      value: 72.42140924378361
    - type: cos_sim_f1
      value: 66.0919540229885
    - type: cos_sim_precision
      value: 72.78481012658227
    - type: cos_sim_recall
      value: 60.526315789473685
    - type: dot_accuracy
      value: 88.5
    - type: dot_ap
      value: 72.42140924378361
    - type: dot_f1
      value: 66.0919540229885
    - type: dot_precision
      value: 72.78481012658227
    - type: dot_recall
      value: 60.526315789473685
    - type: euclidean_accuracy
      value: 88.5
    - type: euclidean_ap
      value: 72.42140924378361
    - type: euclidean_f1
      value: 66.0919540229885
    - type: euclidean_precision
      value: 72.78481012658227
    - type: euclidean_recall
      value: 60.526315789473685
    - type: manhattan_accuracy
      value: 88.5
    - type: manhattan_ap
      value: 72.49745515311696
    - type: manhattan_f1
      value: 66.0968660968661
    - type: manhattan_precision
      value: 72.04968944099379
    - type: manhattan_recall
      value: 61.05263157894737
    - type: max_accuracy
      value: 88.5
    - type: max_ap
      value: 72.49745515311696
    - type: max_f1
      value: 66.0968660968661
  - task:
      type: STS
    dataset:
      name: MTEB CDSC-R
      type: PL-MTEB/cdscr-sts
      config: default
      split: test
      revision: None
    metrics:
    - type: cos_sim_pearson
      value: 90.32269765590145
    - type: cos_sim_spearman
      value: 89.73666311491672
    - type: euclidean_pearson
      value: 88.2933868516544
    - type: euclidean_spearman
      value: 89.73666311491672
    - type: manhattan_pearson
      value: 88.33474590219448
    - type: manhattan_spearman
      value: 89.8548364866583
  - task:
      type: Retrieval
    dataset:
      name: MTEB DBPedia-PL
      type: clarin-knext/dbpedia-pl
      config: default
      split: test
      revision: 76afe41d9af165cc40999fcaa92312b8b012064a
    metrics:
    - type: map_at_1
      value: 7.632999999999999
    - type: map_at_10
      value: 16.426
    - type: map_at_100
      value: 22.651
    - type: map_at_1000
      value: 24.372
    - type: map_at_3
      value: 11.706
    - type: map_at_5
      value: 13.529
    - type: mrr_at_1
      value: 60.75000000000001
    - type: mrr_at_10
      value: 68.613
    - type: mrr_at_100
      value: 69.001
    - type: mrr_at_1000
      value: 69.021
    - type: mrr_at_3
      value: 67.0
    - type: mrr_at_5
      value: 67.925
    - type: ndcg_at_1
      value: 49.875
    - type: ndcg_at_10
      value: 36.978
    - type: ndcg_at_100
      value: 40.031
    - type: ndcg_at_1000
      value: 47.566
    - type: ndcg_at_3
      value: 41.148
    - type: ndcg_at_5
      value: 38.702
    - type: precision_at_1
      value: 60.75000000000001
    - type: precision_at_10
      value: 29.7
    - type: precision_at_100
      value: 9.278
    - type: precision_at_1000
      value: 2.099
    - type: precision_at_3
      value: 44.0
    - type: precision_at_5
      value: 37.6
    - type: recall_at_1
      value: 7.632999999999999
    - type: recall_at_10
      value: 22.040000000000003
    - type: recall_at_100
      value: 44.024
    - type: recall_at_1000
      value: 67.848
    - type: recall_at_3
      value: 13.093
    - type: recall_at_5
      value: 15.973
  - task:
      type: Retrieval
    dataset:
      name: MTEB FiQA-PL
      type: clarin-knext/fiqa-pl
      config: default
      split: test
      revision: 2e535829717f8bf9dc829b7f911cc5bbd4e6608e
    metrics:
    - type: map_at_1
      value: 15.473
    - type: map_at_10
      value: 24.579
    - type: map_at_100
      value: 26.387
    - type: map_at_1000
      value: 26.57
    - type: map_at_3
      value: 21.278
    - type: map_at_5
      value: 23.179
    - type: mrr_at_1
      value: 30.709999999999997
    - type: mrr_at_10
      value: 38.994
    - type: mrr_at_100
      value: 39.993
    - type: mrr_at_1000
      value: 40.044999999999995
    - type: mrr_at_3
      value: 36.342999999999996
    - type: mrr_at_5
      value: 37.846999999999994
    - type: ndcg_at_1
      value: 30.709999999999997
    - type: ndcg_at_10
      value: 31.608999999999998
    - type: ndcg_at_100
      value: 38.807
    - type: ndcg_at_1000
      value: 42.208
    - type: ndcg_at_3
      value: 28.086
    - type: ndcg_at_5
      value: 29.323
    - type: precision_at_1
      value: 30.709999999999997
    - type: precision_at_10
      value: 8.688
    - type: precision_at_100
      value: 1.608
    - type: precision_at_1000
      value: 0.22100000000000003
    - type: precision_at_3
      value: 18.724
    - type: precision_at_5
      value: 13.950999999999999
    - type: recall_at_1
      value: 15.473
    - type: recall_at_10
      value: 38.361000000000004
    - type: recall_at_100
      value: 65.2
    - type: recall_at_1000
      value: 85.789
    - type: recall_at_3
      value: 25.401
    - type: recall_at_5
      value: 30.875999999999998
  - task:
      type: Retrieval
    dataset:
      name: MTEB HotpotQA-PL
      type: clarin-knext/hotpotqa-pl
      config: default
      split: test
      revision: a0bd479ac97b4ccb5bd6ce320c415d0bb4beb907
    metrics:
    - type: map_at_1
      value: 38.096000000000004
    - type: map_at_10
      value: 51.44499999999999
    - type: map_at_100
      value: 52.325
    - type: map_at_1000
      value: 52.397000000000006
    - type: map_at_3
      value: 48.626999999999995
    - type: map_at_5
      value: 50.342
    - type: mrr_at_1
      value: 76.19200000000001
    - type: mrr_at_10
      value: 81.191
    - type: mrr_at_100
      value: 81.431
    - type: mrr_at_1000
      value: 81.443
    - type: mrr_at_3
      value: 80.30199999999999
    - type: mrr_at_5
      value: 80.85900000000001
    - type: ndcg_at_1
      value: 76.19200000000001
    - type: ndcg_at_10
      value: 60.9
    - type: ndcg_at_100
      value: 64.14699999999999
    - type: ndcg_at_1000
      value: 65.647
    - type: ndcg_at_3
      value: 56.818000000000005
    - type: ndcg_at_5
      value: 59.019999999999996
    - type: precision_at_1
      value: 76.19200000000001
    - type: precision_at_10
      value: 12.203
    - type: precision_at_100
      value: 1.478
    - type: precision_at_1000
      value: 0.168
    - type: precision_at_3
      value: 34.616
    - type: precision_at_5
      value: 22.515
    - type: recall_at_1
      value: 38.096000000000004
    - type: recall_at_10
      value: 61.013
    - type: recall_at_100
      value: 73.90299999999999
    - type: recall_at_1000
      value: 83.91
    - type: recall_at_3
      value: 51.92400000000001
    - type: recall_at_5
      value: 56.286
  - task:
      type: Retrieval
    dataset:
      name: MTEB MSMARCO-PL
      type: clarin-knext/msmarco-pl
      config: default
      split: test
      revision: 8634c07806d5cce3a6138e260e59b81760a0a640
    metrics:
    - type: map_at_1
      value: 1.548
    - type: map_at_10
      value: 11.049000000000001
    - type: map_at_100
      value: 28.874
    - type: map_at_1000
      value: 34.931
    - type: map_at_3
      value: 4.162
    - type: map_at_5
      value: 6.396
    - type: mrr_at_1
      value: 90.69800000000001
    - type: mrr_at_10
      value: 92.093
    - type: mrr_at_100
      value: 92.345
    - type: mrr_at_1000
      value: 92.345
    - type: mrr_at_3
      value: 91.86
    - type: mrr_at_5
      value: 91.86
    - type: ndcg_at_1
      value: 74.031
    - type: ndcg_at_10
      value: 63.978
    - type: ndcg_at_100
      value: 53.101
    - type: ndcg_at_1000
      value: 60.675999999999995
    - type: ndcg_at_3
      value: 71.421
    - type: ndcg_at_5
      value: 68.098
    - type: precision_at_1
      value: 90.69800000000001
    - type: precision_at_10
      value: 71.86
    - type: precision_at_100
      value: 31.395
    - type: precision_at_1000
      value: 5.981
    - type: precision_at_3
      value: 84.49600000000001
    - type: precision_at_5
      value: 79.07
    - type: recall_at_1
      value: 1.548
    - type: recall_at_10
      value: 12.149000000000001
    - type: recall_at_100
      value: 40.794999999999995
    - type: recall_at_1000
      value: 67.974
    - type: recall_at_3
      value: 4.244
    - type: recall_at_5
      value: 6.608
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (pl)
      type: mteb/amazon_massive_intent
      config: pl
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 73.55413584398119
    - type: f1
      value: 69.65610882318181
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveScenarioClassification (pl)
      type: mteb/amazon_massive_scenario
      config: pl
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 76.37188971082716
    - type: f1
      value: 75.64847309941361
  - task:
      type: Retrieval
    dataset:
      name: MTEB NFCorpus-PL
      type: clarin-knext/nfcorpus-pl
      config: default
      split: test
      revision: 9a6f9567fda928260afed2de480d79c98bf0bec0
    metrics:
    - type: map_at_1
      value: 4.919
    - type: map_at_10
      value: 10.834000000000001
    - type: map_at_100
      value: 13.38
    - type: map_at_1000
      value: 14.581
    - type: map_at_3
      value: 8.198
    - type: map_at_5
      value: 9.428
    - type: mrr_at_1
      value: 41.176
    - type: mrr_at_10
      value: 50.083
    - type: mrr_at_100
      value: 50.559
    - type: mrr_at_1000
      value: 50.604000000000006
    - type: mrr_at_3
      value: 47.936
    - type: mrr_at_5
      value: 49.407000000000004
    - type: ndcg_at_1
      value: 39.628
    - type: ndcg_at_10
      value: 30.098000000000003
    - type: ndcg_at_100
      value: 27.061
    - type: ndcg_at_1000
      value: 35.94
    - type: ndcg_at_3
      value: 35.135
    - type: ndcg_at_5
      value: 33.335
    - type: precision_at_1
      value: 41.176
    - type: precision_at_10
      value: 22.259999999999998
    - type: precision_at_100
      value: 6.712
    - type: precision_at_1000
      value: 1.9060000000000001
    - type: precision_at_3
      value: 33.23
    - type: precision_at_5
      value: 29.04
    - type: recall_at_1
      value: 4.919
    - type: recall_at_10
      value: 14.196
    - type: recall_at_100
      value: 26.948
    - type: recall_at_1000
      value: 59.211000000000006
    - type: recall_at_3
      value: 9.44
    - type: recall_at_5
      value: 11.569
  - task:
      type: Retrieval
    dataset:
      name: MTEB NQ-PL
      type: clarin-knext/nq-pl
      config: default
      split: test
      revision: f171245712cf85dd4700b06bef18001578d0ca8d
    metrics:
    - type: map_at_1
      value: 25.35
    - type: map_at_10
      value: 37.884
    - type: map_at_100
      value: 38.955
    - type: map_at_1000
      value: 39.007999999999996
    - type: map_at_3
      value: 34.239999999999995
    - type: map_at_5
      value: 36.398
    - type: mrr_at_1
      value: 28.737000000000002
    - type: mrr_at_10
      value: 39.973
    - type: mrr_at_100
      value: 40.844
    - type: mrr_at_1000
      value: 40.885
    - type: mrr_at_3
      value: 36.901
    - type: mrr_at_5
      value: 38.721
    - type: ndcg_at_1
      value: 28.708
    - type: ndcg_at_10
      value: 44.204
    - type: ndcg_at_100
      value: 48.978
    - type: ndcg_at_1000
      value: 50.33
    - type: ndcg_at_3
      value: 37.36
    - type: ndcg_at_5
      value: 40.912
    - type: precision_at_1
      value: 28.708
    - type: precision_at_10
      value: 7.367
    - type: precision_at_100
      value: 1.0030000000000001
    - type: precision_at_1000
      value: 0.11299999999999999
    - type: precision_at_3
      value: 17.034
    - type: precision_at_5
      value: 12.293999999999999
    - type: recall_at_1
      value: 25.35
    - type: recall_at_10
      value: 61.411
    - type: recall_at_100
      value: 82.599
    - type: recall_at_1000
      value: 92.903
    - type: recall_at_3
      value: 43.728
    - type: recall_at_5
      value: 51.854
  - task:
      type: Classification
    dataset:
      name: MTEB PAC
      type: laugustyniak/abusive-clauses-pl
      config: default
      split: test
      revision: None
    metrics:
    - type: accuracy
      value: 69.04141326382856
    - type: ap
      value: 77.49422763833996
    - type: f1
      value: 66.73472657783407
  - task:
      type: PairClassification
    dataset:
      name: MTEB PPC
      type: PL-MTEB/ppc-pairclassification
      config: default
      split: test
      revision: None
    metrics:
    - type: cos_sim_accuracy
      value: 81.0
    - type: cos_sim_ap
      value: 91.47194213011349
    - type: cos_sim_f1
      value: 84.73767885532592
    - type: cos_sim_precision
      value: 81.49847094801224
    - type: cos_sim_recall
      value: 88.24503311258279
    - type: dot_accuracy
      value: 81.0
    - type: dot_ap
      value: 91.47194213011349
    - type: dot_f1
      value: 84.73767885532592
    - type: dot_precision
      value: 81.49847094801224
    - type: dot_recall
      value: 88.24503311258279
    - type: euclidean_accuracy
      value: 81.0
    - type: euclidean_ap
      value: 91.47194213011349
    - type: euclidean_f1
      value: 84.73767885532592
    - type: euclidean_precision
      value: 81.49847094801224
    - type: euclidean_recall
      value: 88.24503311258279
    - type: manhattan_accuracy
      value: 81.0
    - type: manhattan_ap
      value: 91.46464475050571
    - type: manhattan_f1
      value: 84.48687350835321
    - type: manhattan_precision
      value: 81.31699846860643
    - type: manhattan_recall
      value: 87.91390728476821
    - type: max_accuracy
      value: 81.0
    - type: max_ap
      value: 91.47194213011349
    - type: max_f1
      value: 84.73767885532592
  - task:
      type: PairClassification
    dataset:
      name: MTEB PSC
      type: PL-MTEB/psc-pairclassification
      config: default
      split: test
      revision: None
    metrics:
    - type: cos_sim_accuracy
      value: 97.6808905380334
    - type: cos_sim_ap
      value: 99.27948611836348
    - type: cos_sim_f1
      value: 96.15975422427034
    - type: cos_sim_precision
      value: 96.90402476780186
    - type: cos_sim_recall
      value: 95.42682926829268
    - type: dot_accuracy
      value: 97.6808905380334
    - type: dot_ap
      value: 99.2794861183635
    - type: dot_f1
      value: 96.15975422427034
    - type: dot_precision
      value: 96.90402476780186
    - type: dot_recall
      value: 95.42682926829268
    - type: euclidean_accuracy
      value: 97.6808905380334
    - type: euclidean_ap
      value: 99.2794861183635
    - type: euclidean_f1
      value: 96.15975422427034
    - type: euclidean_precision
      value: 96.90402476780186
    - type: euclidean_recall
      value: 95.42682926829268
    - type: manhattan_accuracy
      value: 97.6808905380334
    - type: manhattan_ap
      value: 99.28715055268721
    - type: manhattan_f1
      value: 96.14791987673343
    - type: manhattan_precision
      value: 97.19626168224299
    - type: manhattan_recall
      value: 95.1219512195122
    - type: max_accuracy
      value: 97.6808905380334
    - type: max_ap
      value: 99.28715055268721
    - type: max_f1
      value: 96.15975422427034
  - task:
      type: Classification
    dataset:
      name: MTEB PolEmo2.0-IN
      type: PL-MTEB/polemo2_in
      config: default
      split: test
      revision: None
    metrics:
    - type: accuracy
      value: 86.16343490304708
    - type: f1
      value: 83.3442579486744
  - task:
      type: Classification
    dataset:
      name: MTEB PolEmo2.0-OUT
      type: PL-MTEB/polemo2_out
      config: default
      split: test
      revision: None
    metrics:
    - type: accuracy
      value: 68.40080971659918
    - type: f1
      value: 53.13720751142237
  - task:
      type: Retrieval
    dataset:
      name: MTEB Quora-PL
      type: clarin-knext/quora-pl
      config: default
      split: test
      revision: 0be27e93455051e531182b85e85e425aba12e9d4
    metrics:
    - type: map_at_1
      value: 63.322
    - type: map_at_10
      value: 76.847
    - type: map_at_100
      value: 77.616
    - type: map_at_1000
      value: 77.644
    - type: map_at_3
      value: 73.624
    - type: map_at_5
      value: 75.603
    - type: mrr_at_1
      value: 72.88
    - type: mrr_at_10
      value: 80.376
    - type: mrr_at_100
      value: 80.604
    - type: mrr_at_1000
      value: 80.61
    - type: mrr_at_3
      value: 78.92
    - type: mrr_at_5
      value: 79.869
    - type: ndcg_at_1
      value: 72.89999999999999
    - type: ndcg_at_10
      value: 81.43
    - type: ndcg_at_100
      value: 83.394
    - type: ndcg_at_1000
      value: 83.685
    - type: ndcg_at_3
      value: 77.62599999999999
    - type: ndcg_at_5
      value: 79.656
    - type: precision_at_1
      value: 72.89999999999999
    - type: precision_at_10
      value: 12.548
    - type: precision_at_100
      value: 1.4869999999999999
    - type: precision_at_1000
      value: 0.155
    - type: precision_at_3
      value: 34.027
    - type: precision_at_5
      value: 22.654
    - type: recall_at_1
      value: 63.322
    - type: recall_at_10
      value: 90.664
    - type: recall_at_100
      value: 97.974
    - type: recall_at_1000
      value: 99.636
    - type: recall_at_3
      value: 80.067
    - type: recall_at_5
      value: 85.526
  - task:
      type: Retrieval
    dataset:
      name: MTEB SCIDOCS-PL
      type: clarin-knext/scidocs-pl
      config: default
      split: test
      revision: 45452b03f05560207ef19149545f168e596c9337
    metrics:
    - type: map_at_1
      value: 3.95
    - type: map_at_10
      value: 9.658999999999999
    - type: map_at_100
      value: 11.384
    - type: map_at_1000
      value: 11.677
    - type: map_at_3
      value: 7.055
    - type: map_at_5
      value: 8.244
    - type: mrr_at_1
      value: 19.5
    - type: mrr_at_10
      value: 28.777
    - type: mrr_at_100
      value: 29.936
    - type: mrr_at_1000
      value: 30.009999999999998
    - type: mrr_at_3
      value: 25.55
    - type: mrr_at_5
      value: 27.284999999999997
    - type: ndcg_at_1
      value: 19.5
    - type: ndcg_at_10
      value: 16.589000000000002
    - type: ndcg_at_100
      value: 23.879
    - type: ndcg_at_1000
      value: 29.279
    - type: ndcg_at_3
      value: 15.719
    - type: ndcg_at_5
      value: 13.572000000000001
    - type: precision_at_1
      value: 19.5
    - type: precision_at_10
      value: 8.62
    - type: precision_at_100
      value: 1.924
    - type: precision_at_1000
      value: 0.322
    - type: precision_at_3
      value: 14.6
    - type: precision_at_5
      value: 11.78
    - type: recall_at_1
      value: 3.95
    - type: recall_at_10
      value: 17.477999999999998
    - type: recall_at_100
      value: 38.99
    - type: recall_at_1000
      value: 65.417
    - type: recall_at_3
      value: 8.883000000000001
    - type: recall_at_5
      value: 11.933
  - task:
      type: PairClassification
    dataset:
      name: MTEB SICK-E-PL
      type: PL-MTEB/sicke-pl-pairclassification
      config: default
      split: test
      revision: None
    metrics:
    - type: cos_sim_accuracy
      value: 83.48960456583775
    - type: cos_sim_ap
      value: 76.31522115825375
    - type: cos_sim_f1
      value: 70.35573122529645
    - type: cos_sim_precision
      value: 70.9934735315446
    - type: cos_sim_recall
      value: 69.72934472934473
    - type: dot_accuracy
      value: 83.48960456583775
    - type: dot_ap
      value: 76.31522115825373
    - type: dot_f1
      value: 70.35573122529645
    - type: dot_precision
      value: 70.9934735315446
    - type: dot_recall
      value: 69.72934472934473
    - type: euclidean_accuracy
      value: 83.48960456583775
    - type: euclidean_ap
      value: 76.31522115825373
    - type: euclidean_f1
      value: 70.35573122529645
    - type: euclidean_precision
      value: 70.9934735315446
    - type: euclidean_recall
      value: 69.72934472934473
    - type: manhattan_accuracy
      value: 83.46922136159804
    - type: manhattan_ap
      value: 76.18474601388084
    - type: manhattan_f1
      value: 70.34779490856937
    - type: manhattan_precision
      value: 70.83032490974729
    - type: manhattan_recall
      value: 69.87179487179486
    - type: max_accuracy
      value: 83.48960456583775
    - type: max_ap
      value: 76.31522115825375
    - type: max_f1
      value: 70.35573122529645
  - task:
      type: STS
    dataset:
      name: MTEB SICK-R-PL
      type: PL-MTEB/sickr-pl-sts
      config: default
      split: test
      revision: None
    metrics:
    - type: cos_sim_pearson
      value: 77.95374883876302
    - type: cos_sim_spearman
      value: 73.77630219171942
    - type: euclidean_pearson
      value: 75.81927069594934
    - type: euclidean_spearman
      value: 73.7763211303831
    - type: manhattan_pearson
      value: 76.03126859057528
    - type: manhattan_spearman
      value: 73.96528138013369
  - task:
      type: STS
    dataset:
      name: MTEB STS22 (pl)
      type: mteb/sts22-crosslingual-sts
      config: pl
      split: test
      revision: eea2b4fe26a775864c896887d910b76a8098ad3f
    metrics:
    - type: cos_sim_pearson
      value: 37.388282764841826
    - type: cos_sim_spearman
      value: 40.83477184710897
    - type: euclidean_pearson
      value: 26.754737044177805
    - type: euclidean_spearman
      value: 40.83477184710897
    - type: manhattan_pearson
      value: 26.760453110872458
    - type: manhattan_spearman
      value: 41.034477441383856
  - task:
      type: Retrieval
    dataset:
      name: MTEB SciFact-PL
      type: clarin-knext/scifact-pl
      config: default
      split: test
      revision: 47932a35f045ef8ed01ba82bf9ff67f6e109207e
    metrics:
    - type: map_at_1
      value: 49.15
    - type: map_at_10
      value: 61.690999999999995
    - type: map_at_100
      value: 62.348000000000006
    - type: map_at_1000
      value: 62.38
    - type: map_at_3
      value: 58.824
    - type: map_at_5
      value: 60.662000000000006
    - type: mrr_at_1
      value: 51.333
    - type: mrr_at_10
      value: 62.731
    - type: mrr_at_100
      value: 63.245
    - type: mrr_at_1000
      value: 63.275000000000006
    - type: mrr_at_3
      value: 60.667
    - type: mrr_at_5
      value: 61.93300000000001
    - type: ndcg_at_1
      value: 51.333
    - type: ndcg_at_10
      value: 67.168
    - type: ndcg_at_100
      value: 69.833
    - type: ndcg_at_1000
      value: 70.56700000000001
    - type: ndcg_at_3
      value: 62.40599999999999
    - type: ndcg_at_5
      value: 65.029
    - type: precision_at_1
      value: 51.333
    - type: precision_at_10
      value: 9.333
    - type: precision_at_100
      value: 1.0699999999999998
    - type: precision_at_1000
      value: 0.11299999999999999
    - type: precision_at_3
      value: 25.333
    - type: precision_at_5
      value: 17.067
    - type: recall_at_1
      value: 49.15
    - type: recall_at_10
      value: 82.533
    - type: recall_at_100
      value: 94.167
    - type: recall_at_1000
      value: 99.667
    - type: recall_at_3
      value: 69.917
    - type: recall_at_5
      value: 76.356
  - task:
      type: Retrieval
    dataset:
      name: MTEB TRECCOVID-PL
      type: clarin-knext/trec-covid-pl
      config: default
      split: test
      revision: 81bcb408f33366c2a20ac54adafad1ae7e877fdd
    metrics:
    - type: map_at_1
      value: 0.261
    - type: map_at_10
      value: 2.1260000000000003
    - type: map_at_100
      value: 12.171999999999999
    - type: map_at_1000
      value: 26.884999999999998
    - type: map_at_3
      value: 0.695
    - type: map_at_5
      value: 1.134
    - type: mrr_at_1
      value: 96.0
    - type: mrr_at_10
      value: 96.952
    - type: mrr_at_100
      value: 96.952
    - type: mrr_at_1000
      value: 96.952
    - type: mrr_at_3
      value: 96.667
    - type: mrr_at_5
      value: 96.667
    - type: ndcg_at_1
      value: 92.0
    - type: ndcg_at_10
      value: 81.193
    - type: ndcg_at_100
      value: 61.129
    - type: ndcg_at_1000
      value: 51.157
    - type: ndcg_at_3
      value: 85.693
    - type: ndcg_at_5
      value: 84.129
    - type: precision_at_1
      value: 96.0
    - type: precision_at_10
      value: 85.39999999999999
    - type: precision_at_100
      value: 62.03999999999999
    - type: precision_at_1000
      value: 22.224
    - type: precision_at_3
      value: 88.0
    - type: precision_at_5
      value: 88.0
    - type: recall_at_1
      value: 0.261
    - type: recall_at_10
      value: 2.262
    - type: recall_at_100
      value: 14.981
    - type: recall_at_1000
      value: 46.837
    - type: recall_at_3
      value: 0.703
    - type: recall_at_5
      value: 1.172
  - task:
      type: Clustering
    dataset:
      name: MTEB AlloProfClusteringP2P
      type: lyon-nlp/alloprof
      config: default
      split: test
      revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
    metrics:
    - type: v_measure
      value: 70.55290063940157
    - type: v_measure
      value: 55.41500719337263
  - task:
      type: Reranking
    dataset:
      name: MTEB AlloprofReranking
      type: lyon-nlp/mteb-fr-reranking-alloprof-s2p
      config: default
      split: test
      revision: 666fdacebe0291776e86f29345663dfaf80a0db9
    metrics:
    - type: map
      value: 73.48697375332002
    - type: mrr
      value: 75.01836585523822
  - task:
      type: Retrieval
    dataset:
      name: MTEB AlloprofRetrieval
      type: lyon-nlp/alloprof
      config: default
      split: test
      revision: 392ba3f5bcc8c51f578786c1fc3dae648662cb9b
    metrics:
    - type: map_at_1
      value: 38.454
    - type: map_at_10
      value: 51.605000000000004
    - type: map_at_100
      value: 52.653000000000006
    - type: map_at_1000
      value: 52.697
    - type: map_at_3
      value: 48.304
    - type: map_at_5
      value: 50.073
    - type: mrr_at_1
      value: 43.307
    - type: mrr_at_10
      value: 54.400000000000006
    - type: mrr_at_100
      value: 55.147999999999996
    - type: mrr_at_1000
      value: 55.174
    - type: mrr_at_3
      value: 51.77
    - type: mrr_at_5
      value: 53.166999999999994
    - type: ndcg_at_1
      value: 43.307
    - type: ndcg_at_10
      value: 57.891000000000005
    - type: ndcg_at_100
      value: 62.161
    - type: ndcg_at_1000
      value: 63.083
    - type: ndcg_at_3
      value: 51.851
    - type: ndcg_at_5
      value: 54.605000000000004
    - type: precision_at_1
      value: 43.307
    - type: precision_at_10
      value: 9.033
    - type: precision_at_100
      value: 1.172
    - type: precision_at_1000
      value: 0.127
    - type: precision_at_3
      value: 22.798
    - type: precision_at_5
      value: 15.492
    - type: recall_at_1
      value: 38.454
    - type: recall_at_10
      value: 74.166
    - type: recall_at_100
      value: 92.43599999999999
    - type: recall_at_1000
      value: 99.071
    - type: recall_at_3
      value: 58.087
    - type: recall_at_5
      value: 64.568
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonReviewsClassification (fr)
      type: mteb/amazon_reviews_multi
      config: fr
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 53.474
    - type: f1
      value: 50.38275392350236
  - task:
      type: Retrieval
    dataset:
      name: MTEB BSARDRetrieval
      type: maastrichtlawtech/bsard
      config: default
      split: test
      revision: 5effa1b9b5fa3b0f9e12523e6e43e5f86a6e6d59
    metrics:
    - type: map_at_1
      value: 2.252
    - type: map_at_10
      value: 4.661
    - type: map_at_100
      value: 5.271
    - type: map_at_1000
      value: 5.3629999999999995
    - type: map_at_3
      value: 3.604
    - type: map_at_5
      value: 4.3020000000000005
    - type: mrr_at_1
      value: 2.252
    - type: mrr_at_10
      value: 4.661
    - type: mrr_at_100
      value: 5.271
    - type: mrr_at_1000
      value: 5.3629999999999995
    - type: mrr_at_3
      value: 3.604
    - type: mrr_at_5
      value: 4.3020000000000005
    - type: ndcg_at_1
      value: 2.252
    - type: ndcg_at_10
      value: 6.3020000000000005
    - type: ndcg_at_100
      value: 10.342
    - type: ndcg_at_1000
      value: 13.475999999999999
    - type: ndcg_at_3
      value: 4.0649999999999995
    - type: ndcg_at_5
      value: 5.344
    - type: precision_at_1
      value: 2.252
    - type: precision_at_10
      value: 1.171
    - type: precision_at_100
      value: 0.333
    - type: precision_at_1000
      value: 0.059000000000000004
    - type: precision_at_3
      value: 1.802
    - type: precision_at_5
      value: 1.712
    - type: recall_at_1
      value: 2.252
    - type: recall_at_10
      value: 11.712
    - type: recall_at_100
      value: 33.333
    - type: recall_at_1000
      value: 59.458999999999996
    - type: recall_at_3
      value: 5.405
    - type: recall_at_5
      value: 8.559
  - task:
      type: Clustering
    dataset:
      name: MTEB HALClusteringS2S
      type: lyon-nlp/clustering-hal-s2s
      config: default
      split: test
      revision: e06ebbbb123f8144bef1a5d18796f3dec9ae2915
    metrics:
    - type: v_measure
      value: 28.301882091023288
  - task:
      type: Clustering
    dataset:
      name: MTEB MLSUMClusteringP2P
      type: mlsum
      config: default
      split: test
      revision: b5d54f8f3b61ae17845046286940f03c6bc79bc7
    metrics:
    - type: v_measure
      value: 45.26992995191701
    - type: v_measure
      value: 42.773174876871145
  - task:
      type: Classification
    dataset:
      name: MTEB MTOPDomainClassification (fr)
      type: mteb/mtop_domain
      config: fr
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
    - type: accuracy
      value: 93.47635452552458
    - type: f1
      value: 93.19922617577213
  - task:
      type: Classification
    dataset:
      name: MTEB MTOPIntentClassification (fr)
      type: mteb/mtop_intent
      config: fr
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 80.2317569683683
    - type: f1
      value: 56.18060418621901
  - task:
      type: Classification
    dataset:
      name: MTEB MasakhaNEWSClassification (fra)
      type: masakhane/masakhanews
      config: fra
      split: test
      revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
    metrics:
    - type: accuracy
      value: 85.18957345971565
    - type: f1
      value: 80.829981537394
  - task:
      type: Clustering
    dataset:
      name: MTEB MasakhaNEWSClusteringP2P (fra)
      type: masakhane/masakhanews
      config: fra
      split: test
      revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
    metrics:
    - type: v_measure
      value: 71.04138999801822
    - type: v_measure
      value: 71.7056263158008
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (fr)
      type: mteb/amazon_massive_intent
      config: fr
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 76.65097511768661
    - type: f1
      value: 73.82441070598712
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveScenarioClassification (fr)
      type: mteb/amazon_massive_scenario
      config: fr
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 79.09885675857431
    - type: f1
      value: 78.28407777434224
  - task:
      type: Retrieval
    dataset:
      name: MTEB MintakaRetrieval (fr)
      type: jinaai/mintakaqa
      config: fr
      split: test
      revision: efa78cc2f74bbcd21eff2261f9e13aebe40b814e
    metrics:
    - type: map_at_1
      value: 25.307000000000002
    - type: map_at_10
      value: 36.723
    - type: map_at_100
      value: 37.713
    - type: map_at_1000
      value: 37.769000000000005
    - type: map_at_3
      value: 33.77
    - type: map_at_5
      value: 35.463
    - type: mrr_at_1
      value: 25.307000000000002
    - type: mrr_at_10
      value: 36.723
    - type: mrr_at_100
      value: 37.713
    - type: mrr_at_1000
      value: 37.769000000000005
    - type: mrr_at_3
      value: 33.77
    - type: mrr_at_5
      value: 35.463
    - type: ndcg_at_1
      value: 25.307000000000002
    - type: ndcg_at_10
      value: 42.559999999999995
    - type: ndcg_at_100
      value: 47.457
    - type: ndcg_at_1000
      value: 49.162
    - type: ndcg_at_3
      value: 36.461
    - type: ndcg_at_5
      value: 39.504
    - type: precision_at_1
      value: 25.307000000000002
    - type: precision_at_10
      value: 6.106
    - type: precision_at_100
      value: 0.8420000000000001
    - type: precision_at_1000
      value: 0.098
    - type: precision_at_3
      value: 14.741999999999999
    - type: precision_at_5
      value: 10.319
    - type: recall_at_1
      value: 25.307000000000002
    - type: recall_at_10
      value: 61.056999999999995
    - type: recall_at_100
      value: 84.152
    - type: recall_at_1000
      value: 98.03399999999999
    - type: recall_at_3
      value: 44.226
    - type: recall_at_5
      value: 51.597
  - task:
      type: PairClassification
    dataset:
      name: MTEB OpusparcusPC (fr)
      type: GEM/opusparcus
      config: fr
      split: test
      revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
    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:
      name: MTEB PawsX (fr)
      type: paws-x
      config: fr
      split: test
      revision: 8a04d940a42cd40658986fdd8e3da561533a3646
    metrics:
    - type: cos_sim_accuracy
      value: 70.8
    - type: cos_sim_ap
      value: 73.7671529695957
    - type: cos_sim_f1
      value: 68.80964339527875
    - type: cos_sim_precision
      value: 62.95955882352941
    - type: cos_sim_recall
      value: 75.85825027685493
    - type: dot_accuracy
      value: 70.8
    - type: dot_ap
      value: 73.78345265366947
    - type: dot_f1
      value: 68.80964339527875
    - type: dot_precision
      value: 62.95955882352941
    - type: dot_recall
      value: 75.85825027685493
    - type: euclidean_accuracy
      value: 70.8
    - type: euclidean_ap
      value: 73.7671529695957
    - type: euclidean_f1
      value: 68.80964339527875
    - type: euclidean_precision
      value: 62.95955882352941
    - type: euclidean_recall
      value: 75.85825027685493
    - type: manhattan_accuracy
      value: 70.75
    - type: manhattan_ap
      value: 73.78996383615953
    - type: manhattan_f1
      value: 68.79432624113475
    - type: manhattan_precision
      value: 63.39869281045751
    - type: manhattan_recall
      value: 75.1937984496124
    - type: max_accuracy
      value: 70.8
    - type: max_ap
      value: 73.78996383615953
    - type: max_f1
      value: 68.80964339527875
  - task:
      type: STS
    dataset:
      name: MTEB SICKFr
      type: Lajavaness/SICK-fr
      config: default
      split: test
      revision: e077ab4cf4774a1e36d86d593b150422fafd8e8a
    metrics:
    - type: cos_sim_pearson
      value: 84.03253762760392
    - type: cos_sim_spearman
      value: 79.68280105762004
    - type: euclidean_pearson
      value: 80.98265050044444
    - type: euclidean_spearman
      value: 79.68233242682867
    - type: manhattan_pearson
      value: 80.9678911810704
    - type: manhattan_spearman
      value: 79.70264097683109
  - task:
      type: STS
    dataset:
      name: MTEB STS22 (fr)
      type: mteb/sts22-crosslingual-sts
      config: fr
      split: test
      revision: eea2b4fe26a775864c896887d910b76a8098ad3f
    metrics:
    - type: cos_sim_pearson
      value: 80.56896987572884
    - type: cos_sim_spearman
      value: 81.84352499523287
    - type: euclidean_pearson
      value: 80.40831759421305
    - type: euclidean_spearman
      value: 81.84352499523287
    - type: manhattan_pearson
      value: 80.74333857561238
    - type: manhattan_spearman
      value: 82.41503246733892
  - task:
      type: STS
    dataset:
      name: MTEB STSBenchmarkMultilingualSTS (fr)
      type: stsb_multi_mt
      config: fr
      split: test
      revision: 93d57ef91790589e3ce9c365164337a8a78b7632
    metrics:
    - type: cos_sim_pearson
      value: 82.71826762276979
    - type: cos_sim_spearman
      value: 82.25433354916042
    - type: euclidean_pearson
      value: 81.87115571724316
    - type: euclidean_spearman
      value: 82.25322342890107
    - type: manhattan_pearson
      value: 82.11174867527224
    - type: manhattan_spearman
      value: 82.55905365203084
  - task:
      type: Summarization
    dataset:
      name: MTEB SummEvalFr
      type: lyon-nlp/summarization-summeval-fr-p2p
      config: default
      split: test
      revision: b385812de6a9577b6f4d0f88c6a6e35395a94054
    metrics:
    - type: cos_sim_pearson
      value: 30.659441623392887
    - type: cos_sim_spearman
      value: 30.501134097353315
    - type: dot_pearson
      value: 30.659444768851056
    - type: dot_spearman
      value: 30.501134097353315
  - task:
      type: Reranking
    dataset:
      name: MTEB SyntecReranking
      type: lyon-nlp/mteb-fr-reranking-syntec-s2p
      config: default
      split: test
      revision: b205c5084a0934ce8af14338bf03feb19499c84d
    metrics:
    - type: map
      value: 94.03333333333333
    - type: mrr
      value: 94.03333333333333
  - task:
      type: Retrieval
    dataset:
      name: MTEB SyntecRetrieval
      type: lyon-nlp/mteb-fr-retrieval-syntec-s2p
      config: default
      split: test
      revision: 77f7e271bf4a92b24fce5119f3486b583ca016ff
    metrics:
    - type: map_at_1
      value: 79.0
    - type: map_at_10
      value: 87.61
    - type: map_at_100
      value: 87.655
    - type: map_at_1000
      value: 87.655
    - type: map_at_3
      value: 87.167
    - type: map_at_5
      value: 87.36699999999999
    - type: mrr_at_1
      value: 79.0
    - type: mrr_at_10
      value: 87.61
    - type: mrr_at_100
      value: 87.655
    - type: mrr_at_1000
      value: 87.655
    - type: mrr_at_3
      value: 87.167
    - type: mrr_at_5
      value: 87.36699999999999
    - type: ndcg_at_1
      value: 79.0
    - type: ndcg_at_10
      value: 90.473
    - type: ndcg_at_100
      value: 90.694
    - type: ndcg_at_1000
      value: 90.694
    - type: ndcg_at_3
      value: 89.464
    - type: ndcg_at_5
      value: 89.851
    - type: precision_at_1
      value: 79.0
    - type: precision_at_10
      value: 9.9
    - type: precision_at_100
      value: 1.0
    - type: precision_at_1000
      value: 0.1
    - type: precision_at_3
      value: 32.0
    - type: precision_at_5
      value: 19.400000000000002
    - type: recall_at_1
      value: 79.0
    - type: recall_at_10
      value: 99.0
    - type: recall_at_100
      value: 100.0
    - type: recall_at_1000
      value: 100.0
    - type: recall_at_3
      value: 96.0
    - type: recall_at_5
      value: 97.0
  - task:
      type: Retrieval
    dataset:
      name: MTEB XPQARetrieval (fr)
      type: jinaai/xpqa
      config: fr
      split: test
      revision: c99d599f0a6ab9b85b065da6f9d94f9cf731679f
    metrics:
    - type: map_at_1
      value: 39.395
    - type: map_at_10
      value: 59.123999999999995
    - type: map_at_100
      value: 60.704
    - type: map_at_1000
      value: 60.760000000000005
    - type: map_at_3
      value: 53.187
    - type: map_at_5
      value: 56.863
    - type: mrr_at_1
      value: 62.083
    - type: mrr_at_10
      value: 68.87299999999999
    - type: mrr_at_100
      value: 69.46900000000001
    - type: mrr_at_1000
      value: 69.48299999999999
    - type: mrr_at_3
      value: 66.8
    - type: mrr_at_5
      value: 67.928
    - type: ndcg_at_1
      value: 62.083
    - type: ndcg_at_10
      value: 65.583
    - type: ndcg_at_100
      value: 70.918
    - type: ndcg_at_1000
      value: 71.72800000000001
    - type: ndcg_at_3
      value: 60.428000000000004
    - type: ndcg_at_5
      value: 61.853
    - type: precision_at_1
      value: 62.083
    - type: precision_at_10
      value: 15.033
    - type: precision_at_100
      value: 1.9529999999999998
    - type: precision_at_1000
      value: 0.207
    - type: precision_at_3
      value: 36.315
    - type: precision_at_5
      value: 25.955000000000002
    - type: recall_at_1
      value: 39.395
    - type: recall_at_10
      value: 74.332
    - type: recall_at_100
      value: 94.729
    - type: recall_at_1000
      value: 99.75500000000001
    - type: recall_at_3
      value: 57.679
    - type: recall_at_5
      value: 65.036
---

# arisylafeta/gte-Qwen2-1.5B-instruct-Q4_0-GGUF
This model was converted to GGUF format from [`Alibaba-NLP/gte-Qwen2-1.5B-instruct`](https://huggingface.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct) for more details on the model.

## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)

```bash
brew install llama.cpp

```
Invoke the llama.cpp server or the CLI.

### CLI:
```bash
llama-cli --hf-repo arisylafeta/gte-Qwen2-1.5B-instruct-Q4_0-GGUF --hf-file gte-qwen2-1.5b-instruct-q4_0.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo arisylafeta/gte-Qwen2-1.5B-instruct-Q4_0-GGUF --hf-file gte-qwen2-1.5b-instruct-q4_0.gguf -c 2048
```

Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.

Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```

Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```

Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo arisylafeta/gte-Qwen2-1.5B-instruct-Q4_0-GGUF --hf-file gte-qwen2-1.5b-instruct-q4_0.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo arisylafeta/gte-Qwen2-1.5B-instruct-Q4_0-GGUF --hf-file gte-qwen2-1.5b-instruct-q4_0.gguf -c 2048
```
