sentence-transformers gguf mteb Sentence Transformers sentence-similarity llama-cpp gguf-my-repo en base_model:intfloat/e5-base-v2 base_model:quantized:intfloat/e5-base-v2 license:mit model-index autotrain_compatible endpoints_compatible region:us feature-extraction
---
base_model: intfloat/e5-base-v2
language:
- en
license: mit
tags:
- mteb
- Sentence Transformers
- sentence-similarity
- sentence-transformers
- llama-cpp
- gguf-my-repo
model-index:
- name: e5-base-v2
  results:
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonCounterfactualClassification (en)
      type: mteb/amazon_counterfactual
      config: en
      split: test
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
    metrics:
    - type: accuracy
      value: 77.77611940298506
    - type: ap
      value: 42.052710266606056
    - type: f1
      value: 72.12040628266567
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonPolarityClassification
      type: mteb/amazon_polarity
      config: default
      split: test
      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
    metrics:
    - type: accuracy
      value: 92.81012500000001
    - type: ap
      value: 89.4213700757244
    - type: f1
      value: 92.8039091197065
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonReviewsClassification (en)
      type: mteb/amazon_reviews_multi
      config: en
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 46.711999999999996
    - type: f1
      value: 46.11544975436018
  - task:
      type: Retrieval
    dataset:
      name: MTEB ArguAna
      type: arguana
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 23.186
    - type: map_at_10
      value: 36.632999999999996
    - type: map_at_100
      value: 37.842
    - type: map_at_1000
      value: 37.865
    - type: map_at_3
      value: 32.278
    - type: map_at_5
      value: 34.760999999999996
    - type: mrr_at_1
      value: 23.400000000000002
    - type: mrr_at_10
      value: 36.721
    - type: mrr_at_100
      value: 37.937
    - type: mrr_at_1000
      value: 37.96
    - type: mrr_at_3
      value: 32.302
    - type: mrr_at_5
      value: 34.894
    - type: ndcg_at_1
      value: 23.186
    - type: ndcg_at_10
      value: 44.49
    - type: ndcg_at_100
      value: 50.065000000000005
    - type: ndcg_at_1000
      value: 50.629999999999995
    - type: ndcg_at_3
      value: 35.461
    - type: ndcg_at_5
      value: 39.969
    - type: precision_at_1
      value: 23.186
    - type: precision_at_10
      value: 6.97
    - type: precision_at_100
      value: 0.951
    - type: precision_at_1000
      value: 0.099
    - type: precision_at_3
      value: 14.912
    - type: precision_at_5
      value: 11.152
    - type: recall_at_1
      value: 23.186
    - type: recall_at_10
      value: 69.70100000000001
    - type: recall_at_100
      value: 95.092
    - type: recall_at_1000
      value: 99.431
    - type: recall_at_3
      value: 44.737
    - type: recall_at_5
      value: 55.761
  - task:
      type: Clustering
    dataset:
      name: MTEB ArxivClusteringP2P
      type: mteb/arxiv-clustering-p2p
      config: default
      split: test
      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
    metrics:
    - type: v_measure
      value: 46.10312401440185
  - task:
      type: Clustering
    dataset:
      name: MTEB ArxivClusteringS2S
      type: mteb/arxiv-clustering-s2s
      config: default
      split: test
      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
    metrics:
    - type: v_measure
      value: 39.67275326095384
  - task:
      type: Reranking
    dataset:
      name: MTEB AskUbuntuDupQuestions
      type: mteb/askubuntudupquestions-reranking
      config: default
      split: test
      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
    metrics:
    - type: map
      value: 58.97793816337376
    - type: mrr
      value: 72.76832431957087
  - task:
      type: STS
    dataset:
      name: MTEB BIOSSES
      type: mteb/biosses-sts
      config: default
      split: test
      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
    metrics:
    - type: cos_sim_pearson
      value: 83.11646947018187
    - type: cos_sim_spearman
      value: 81.40064994975234
    - type: euclidean_pearson
      value: 82.37355689019232
    - type: euclidean_spearman
      value: 81.6777646977348
    - type: manhattan_pearson
      value: 82.61101422716945
    - type: manhattan_spearman
      value: 81.80427360442245
  - task:
      type: Classification
    dataset:
      name: MTEB Banking77Classification
      type: mteb/banking77
      config: default
      split: test
      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
    metrics:
    - type: accuracy
      value: 83.52922077922076
    - type: f1
      value: 83.45298679360866
  - task:
      type: Clustering
    dataset:
      name: MTEB BiorxivClusteringP2P
      type: mteb/biorxiv-clustering-p2p
      config: default
      split: test
      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
    metrics:
    - type: v_measure
      value: 37.495115019668496
  - task:
      type: Clustering
    dataset:
      name: MTEB BiorxivClusteringS2S
      type: mteb/biorxiv-clustering-s2s
      config: default
      split: test
      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
    metrics:
    - type: v_measure
      value: 32.724792944166765
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackAndroidRetrieval
      type: BeIR/cqadupstack
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 32.361000000000004
    - type: map_at_10
      value: 43.765
    - type: map_at_100
      value: 45.224
    - type: map_at_1000
      value: 45.35
    - type: map_at_3
      value: 40.353
    - type: map_at_5
      value: 42.195
    - type: mrr_at_1
      value: 40.629
    - type: mrr_at_10
      value: 50.458000000000006
    - type: mrr_at_100
      value: 51.06699999999999
    - type: mrr_at_1000
      value: 51.12
    - type: mrr_at_3
      value: 47.902
    - type: mrr_at_5
      value: 49.447
    - type: ndcg_at_1
      value: 40.629
    - type: ndcg_at_10
      value: 50.376
    - type: ndcg_at_100
      value: 55.065
    - type: ndcg_at_1000
      value: 57.196000000000005
    - type: ndcg_at_3
      value: 45.616
    - type: ndcg_at_5
      value: 47.646
    - type: precision_at_1
      value: 40.629
    - type: precision_at_10
      value: 9.785
    - type: precision_at_100
      value: 1.562
    - type: precision_at_1000
      value: 0.2
    - type: precision_at_3
      value: 22.031
    - type: precision_at_5
      value: 15.737000000000002
    - type: recall_at_1
      value: 32.361000000000004
    - type: recall_at_10
      value: 62.214000000000006
    - type: recall_at_100
      value: 81.464
    - type: recall_at_1000
      value: 95.905
    - type: recall_at_3
      value: 47.5
    - type: recall_at_5
      value: 53.69500000000001
    - type: map_at_1
      value: 27.971
    - type: map_at_10
      value: 37.444
    - type: map_at_100
      value: 38.607
    - type: map_at_1000
      value: 38.737
    - type: map_at_3
      value: 34.504000000000005
    - type: map_at_5
      value: 36.234
    - type: mrr_at_1
      value: 35.35
    - type: mrr_at_10
      value: 43.441
    - type: mrr_at_100
      value: 44.147999999999996
    - type: mrr_at_1000
      value: 44.196000000000005
    - type: mrr_at_3
      value: 41.285
    - type: mrr_at_5
      value: 42.552
    - type: ndcg_at_1
      value: 35.35
    - type: ndcg_at_10
      value: 42.903999999999996
    - type: ndcg_at_100
      value: 47.406
    - type: ndcg_at_1000
      value: 49.588
    - type: ndcg_at_3
      value: 38.778
    - type: ndcg_at_5
      value: 40.788000000000004
    - type: precision_at_1
      value: 35.35
    - type: precision_at_10
      value: 8.083
    - type: precision_at_100
      value: 1.313
    - type: precision_at_1000
      value: 0.18
    - type: precision_at_3
      value: 18.769
    - type: precision_at_5
      value: 13.439
    - type: recall_at_1
      value: 27.971
    - type: recall_at_10
      value: 52.492000000000004
    - type: recall_at_100
      value: 71.642
    - type: recall_at_1000
      value: 85.488
    - type: recall_at_3
      value: 40.1
    - type: recall_at_5
      value: 45.800000000000004
    - type: map_at_1
      value: 39.898
    - type: map_at_10
      value: 51.819
    - type: map_at_100
      value: 52.886
    - type: map_at_1000
      value: 52.941
    - type: map_at_3
      value: 48.619
    - type: map_at_5
      value: 50.493
    - type: mrr_at_1
      value: 45.391999999999996
    - type: mrr_at_10
      value: 55.230000000000004
    - type: mrr_at_100
      value: 55.887
    - type: mrr_at_1000
      value: 55.916
    - type: mrr_at_3
      value: 52.717000000000006
    - type: mrr_at_5
      value: 54.222
    - type: ndcg_at_1
      value: 45.391999999999996
    - type: ndcg_at_10
      value: 57.586999999999996
    - type: ndcg_at_100
      value: 61.745000000000005
    - type: ndcg_at_1000
      value: 62.83800000000001
    - type: ndcg_at_3
      value: 52.207
    - type: ndcg_at_5
      value: 54.925999999999995
    - type: precision_at_1
      value: 45.391999999999996
    - type: precision_at_10
      value: 9.21
    - type: precision_at_100
      value: 1.226
    - type: precision_at_1000
      value: 0.136
    - type: precision_at_3
      value: 23.177
    - type: precision_at_5
      value: 16.038
    - type: recall_at_1
      value: 39.898
    - type: recall_at_10
      value: 71.18900000000001
    - type: recall_at_100
      value: 89.082
    - type: recall_at_1000
      value: 96.865
    - type: recall_at_3
      value: 56.907
    - type: recall_at_5
      value: 63.397999999999996
    - type: map_at_1
      value: 22.706
    - type: map_at_10
      value: 30.818
    - type: map_at_100
      value: 32.038
    - type: map_at_1000
      value: 32.123000000000005
    - type: map_at_3
      value: 28.077
    - type: map_at_5
      value: 29.709999999999997
    - type: mrr_at_1
      value: 24.407
    - type: mrr_at_10
      value: 32.555
    - type: mrr_at_100
      value: 33.692
    - type: mrr_at_1000
      value: 33.751
    - type: mrr_at_3
      value: 29.848999999999997
    - type: mrr_at_5
      value: 31.509999999999998
    - type: ndcg_at_1
      value: 24.407
    - type: ndcg_at_10
      value: 35.624
    - type: ndcg_at_100
      value: 41.454
    - type: ndcg_at_1000
      value: 43.556
    - type: ndcg_at_3
      value: 30.217
    - type: ndcg_at_5
      value: 33.111000000000004
    - type: precision_at_1
      value: 24.407
    - type: precision_at_10
      value: 5.548
    - type: precision_at_100
      value: 0.8869999999999999
    - type: precision_at_1000
      value: 0.11100000000000002
    - type: precision_at_3
      value: 12.731
    - type: precision_at_5
      value: 9.22
    - type: recall_at_1
      value: 22.706
    - type: recall_at_10
      value: 48.772
    - type: recall_at_100
      value: 75.053
    - type: recall_at_1000
      value: 90.731
    - type: recall_at_3
      value: 34.421
    - type: recall_at_5
      value: 41.427
    - type: map_at_1
      value: 13.424
    - type: map_at_10
      value: 21.09
    - type: map_at_100
      value: 22.264999999999997
    - type: map_at_1000
      value: 22.402
    - type: map_at_3
      value: 18.312
    - type: map_at_5
      value: 19.874
    - type: mrr_at_1
      value: 16.915
    - type: mrr_at_10
      value: 25.258000000000003
    - type: mrr_at_100
      value: 26.228
    - type: mrr_at_1000
      value: 26.31
    - type: mrr_at_3
      value: 22.492
    - type: mrr_at_5
      value: 24.04
    - type: ndcg_at_1
      value: 16.915
    - type: ndcg_at_10
      value: 26.266000000000002
    - type: ndcg_at_100
      value: 32.08
    - type: ndcg_at_1000
      value: 35.086
    - type: ndcg_at_3
      value: 21.049
    - type: ndcg_at_5
      value: 23.508000000000003
    - type: precision_at_1
      value: 16.915
    - type: precision_at_10
      value: 5.1
    - type: precision_at_100
      value: 0.9329999999999999
    - type: precision_at_1000
      value: 0.131
    - type: precision_at_3
      value: 10.282
    - type: precision_at_5
      value: 7.836
    - type: recall_at_1
      value: 13.424
    - type: recall_at_10
      value: 38.179
    - type: recall_at_100
      value: 63.906
    - type: recall_at_1000
      value: 84.933
    - type: recall_at_3
      value: 23.878
    - type: recall_at_5
      value: 30.037999999999997
    - type: map_at_1
      value: 26.154
    - type: map_at_10
      value: 35.912
    - type: map_at_100
      value: 37.211
    - type: map_at_1000
      value: 37.327
    - type: map_at_3
      value: 32.684999999999995
    - type: map_at_5
      value: 34.562
    - type: mrr_at_1
      value: 32.435
    - type: mrr_at_10
      value: 41.411
    - type: mrr_at_100
      value: 42.297000000000004
    - type: mrr_at_1000
      value: 42.345
    - type: mrr_at_3
      value: 38.771
    - type: mrr_at_5
      value: 40.33
    - type: ndcg_at_1
      value: 32.435
    - type: ndcg_at_10
      value: 41.785
    - type: ndcg_at_100
      value: 47.469
    - type: ndcg_at_1000
      value: 49.685
    - type: ndcg_at_3
      value: 36.618
    - type: ndcg_at_5
      value: 39.101
    - type: precision_at_1
      value: 32.435
    - type: precision_at_10
      value: 7.642
    - type: precision_at_100
      value: 1.244
    - type: precision_at_1000
      value: 0.163
    - type: precision_at_3
      value: 17.485
    - type: precision_at_5
      value: 12.57
    - type: recall_at_1
      value: 26.154
    - type: recall_at_10
      value: 54.111
    - type: recall_at_100
      value: 78.348
    - type: recall_at_1000
      value: 92.996
    - type: recall_at_3
      value: 39.189
    - type: recall_at_5
      value: 45.852
    - type: map_at_1
      value: 26.308999999999997
    - type: map_at_10
      value: 35.524
    - type: map_at_100
      value: 36.774
    - type: map_at_1000
      value: 36.891
    - type: map_at_3
      value: 32.561
    - type: map_at_5
      value: 34.034
    - type: mrr_at_1
      value: 31.735000000000003
    - type: mrr_at_10
      value: 40.391
    - type: mrr_at_100
      value: 41.227000000000004
    - type: mrr_at_1000
      value: 41.288000000000004
    - type: mrr_at_3
      value: 37.938
    - type: mrr_at_5
      value: 39.193
    - type: ndcg_at_1
      value: 31.735000000000003
    - type: ndcg_at_10
      value: 41.166000000000004
    - type: ndcg_at_100
      value: 46.702
    - type: ndcg_at_1000
      value: 49.157000000000004
    - type: ndcg_at_3
      value: 36.274
    - type: ndcg_at_5
      value: 38.177
    - type: precision_at_1
      value: 31.735000000000003
    - type: precision_at_10
      value: 7.5569999999999995
    - type: precision_at_100
      value: 1.2109999999999999
    - type: precision_at_1000
      value: 0.16
    - type: precision_at_3
      value: 17.199
    - type: precision_at_5
      value: 12.123000000000001
    - type: recall_at_1
      value: 26.308999999999997
    - type: recall_at_10
      value: 53.083000000000006
    - type: recall_at_100
      value: 76.922
    - type: recall_at_1000
      value: 93.767
    - type: recall_at_3
      value: 39.262
    - type: recall_at_5
      value: 44.413000000000004
    - type: map_at_1
      value: 24.391250000000003
    - type: map_at_10
      value: 33.280166666666666
    - type: map_at_100
      value: 34.49566666666667
    - type: map_at_1000
      value: 34.61533333333333
    - type: map_at_3
      value: 30.52183333333333
    - type: map_at_5
      value: 32.06608333333333
    - type: mrr_at_1
      value: 29.105083333333337
    - type: mrr_at_10
      value: 37.44766666666666
    - type: mrr_at_100
      value: 38.32491666666667
    - type: mrr_at_1000
      value: 38.385666666666665
    - type: mrr_at_3
      value: 35.06883333333333
    - type: mrr_at_5
      value: 36.42066666666667
    - type: ndcg_at_1
      value: 29.105083333333337
    - type: ndcg_at_10
      value: 38.54358333333333
    - type: ndcg_at_100
      value: 43.833583333333344
    - type: ndcg_at_1000
      value: 46.215333333333334
    - type: ndcg_at_3
      value: 33.876
    - type: ndcg_at_5
      value: 36.05208333333333
    - type: precision_at_1
      value: 29.105083333333337
    - type: precision_at_10
      value: 6.823416666666665
    - type: precision_at_100
      value: 1.1270833333333334
    - type: precision_at_1000
      value: 0.15208333333333332
    - type: precision_at_3
      value: 15.696750000000002
    - type: precision_at_5
      value: 11.193499999999998
    - type: recall_at_1
      value: 24.391250000000003
    - type: recall_at_10
      value: 49.98808333333333
    - type: recall_at_100
      value: 73.31616666666666
    - type: recall_at_1000
      value: 89.96291666666667
    - type: recall_at_3
      value: 36.86666666666667
    - type: recall_at_5
      value: 42.54350000000001
    - type: map_at_1
      value: 21.995
    - type: map_at_10
      value: 28.807
    - type: map_at_100
      value: 29.813000000000002
    - type: map_at_1000
      value: 29.903000000000002
    - type: map_at_3
      value: 26.636
    - type: map_at_5
      value: 27.912
    - type: mrr_at_1
      value: 24.847
    - type: mrr_at_10
      value: 31.494
    - type: mrr_at_100
      value: 32.381
    - type: mrr_at_1000
      value: 32.446999999999996
    - type: mrr_at_3
      value: 29.473
    - type: mrr_at_5
      value: 30.7
    - type: ndcg_at_1
      value: 24.847
    - type: ndcg_at_10
      value: 32.818999999999996
    - type: ndcg_at_100
      value: 37.835
    - type: ndcg_at_1000
      value: 40.226
    - type: ndcg_at_3
      value: 28.811999999999998
    - type: ndcg_at_5
      value: 30.875999999999998
    - type: precision_at_1
      value: 24.847
    - type: precision_at_10
      value: 5.244999999999999
    - type: precision_at_100
      value: 0.856
    - type: precision_at_1000
      value: 0.11299999999999999
    - type: precision_at_3
      value: 12.577
    - type: precision_at_5
      value: 8.895999999999999
    - type: recall_at_1
      value: 21.995
    - type: recall_at_10
      value: 42.479
    - type: recall_at_100
      value: 65.337
    - type: recall_at_1000
      value: 83.23700000000001
    - type: recall_at_3
      value: 31.573
    - type: recall_at_5
      value: 36.684
    - type: map_at_1
      value: 15.751000000000001
    - type: map_at_10
      value: 21.909
    - type: map_at_100
      value: 23.064
    - type: map_at_1000
      value: 23.205000000000002
    - type: map_at_3
      value: 20.138
    - type: map_at_5
      value: 20.973
    - type: mrr_at_1
      value: 19.305
    - type: mrr_at_10
      value: 25.647
    - type: mrr_at_100
      value: 26.659
    - type: mrr_at_1000
      value: 26.748
    - type: mrr_at_3
      value: 23.933
    - type: mrr_at_5
      value: 24.754
    - type: ndcg_at_1
      value: 19.305
    - type: ndcg_at_10
      value: 25.886
    - type: ndcg_at_100
      value: 31.56
    - type: ndcg_at_1000
      value: 34.799
    - type: ndcg_at_3
      value: 22.708000000000002
    - type: ndcg_at_5
      value: 23.838
    - type: precision_at_1
      value: 19.305
    - type: precision_at_10
      value: 4.677
    - type: precision_at_100
      value: 0.895
    - type: precision_at_1000
      value: 0.136
    - type: precision_at_3
      value: 10.771
    - type: precision_at_5
      value: 7.46
    - type: recall_at_1
      value: 15.751000000000001
    - type: recall_at_10
      value: 34.156
    - type: recall_at_100
      value: 59.899
    - type: recall_at_1000
      value: 83.08
    - type: recall_at_3
      value: 24.772
    - type: recall_at_5
      value: 28.009
    - type: map_at_1
      value: 23.34
    - type: map_at_10
      value: 32.383
    - type: map_at_100
      value: 33.629999999999995
    - type: map_at_1000
      value: 33.735
    - type: map_at_3
      value: 29.68
    - type: map_at_5
      value: 31.270999999999997
    - type: mrr_at_1
      value: 27.612
    - type: mrr_at_10
      value: 36.381
    - type: mrr_at_100
      value: 37.351
    - type: mrr_at_1000
      value: 37.411
    - type: mrr_at_3
      value: 33.893
    - type: mrr_at_5
      value: 35.353
    - type: ndcg_at_1
      value: 27.612
    - type: ndcg_at_10
      value: 37.714999999999996
    - type: ndcg_at_100
      value: 43.525000000000006
    - type: ndcg_at_1000
      value: 45.812999999999995
    - type: ndcg_at_3
      value: 32.796
    - type: ndcg_at_5
      value: 35.243
    - type: precision_at_1
      value: 27.612
    - type: precision_at_10
      value: 6.465
    - type: precision_at_100
      value: 1.0619999999999998
    - type: precision_at_1000
      value: 0.13699999999999998
    - type: precision_at_3
      value: 15.049999999999999
    - type: precision_at_5
      value: 10.764999999999999
    - type: recall_at_1
      value: 23.34
    - type: recall_at_10
      value: 49.856
    - type: recall_at_100
      value: 75.334
    - type: recall_at_1000
      value: 91.156
    - type: recall_at_3
      value: 36.497
    - type: recall_at_5
      value: 42.769
    - type: map_at_1
      value: 25.097
    - type: map_at_10
      value: 34.599999999999994
    - type: map_at_100
      value: 36.174
    - type: map_at_1000
      value: 36.398
    - type: map_at_3
      value: 31.781
    - type: map_at_5
      value: 33.22
    - type: mrr_at_1
      value: 31.225
    - type: mrr_at_10
      value: 39.873
    - type: mrr_at_100
      value: 40.853
    - type: mrr_at_1000
      value: 40.904
    - type: mrr_at_3
      value: 37.681
    - type: mrr_at_5
      value: 38.669
    - type: ndcg_at_1
      value: 31.225
    - type: ndcg_at_10
      value: 40.586
    - type: ndcg_at_100
      value: 46.226
    - type: ndcg_at_1000
      value: 48.788
    - type: ndcg_at_3
      value: 36.258
    - type: ndcg_at_5
      value: 37.848
    - type: precision_at_1
      value: 31.225
    - type: precision_at_10
      value: 7.707999999999999
    - type: precision_at_100
      value: 1.536
    - type: precision_at_1000
      value: 0.242
    - type: precision_at_3
      value: 17.26
    - type: precision_at_5
      value: 12.253
    - type: recall_at_1
      value: 25.097
    - type: recall_at_10
      value: 51.602000000000004
    - type: recall_at_100
      value: 76.854
    - type: recall_at_1000
      value: 93.303
    - type: recall_at_3
      value: 38.68
    - type: recall_at_5
      value: 43.258
    - type: map_at_1
      value: 17.689
    - type: map_at_10
      value: 25.291000000000004
    - type: map_at_100
      value: 26.262
    - type: map_at_1000
      value: 26.372
    - type: map_at_3
      value: 22.916
    - type: map_at_5
      value: 24.315
    - type: mrr_at_1
      value: 19.409000000000002
    - type: mrr_at_10
      value: 27.233
    - type: mrr_at_100
      value: 28.109
    - type: mrr_at_1000
      value: 28.192
    - type: mrr_at_3
      value: 24.892
    - type: mrr_at_5
      value: 26.278000000000002
    - type: ndcg_at_1
      value: 19.409000000000002
    - type: ndcg_at_10
      value: 29.809
    - type: ndcg_at_100
      value: 34.936
    - type: ndcg_at_1000
      value: 37.852000000000004
    - type: ndcg_at_3
      value: 25.179000000000002
    - type: ndcg_at_5
      value: 27.563
    - type: precision_at_1
      value: 19.409000000000002
    - type: precision_at_10
      value: 4.861
    - type: precision_at_100
      value: 0.8
    - type: precision_at_1000
      value: 0.116
    - type: precision_at_3
      value: 11.029
    - type: precision_at_5
      value: 7.985
    - type: recall_at_1
      value: 17.689
    - type: recall_at_10
      value: 41.724
    - type: recall_at_100
      value: 65.95299999999999
    - type: recall_at_1000
      value: 88.094
    - type: recall_at_3
      value: 29.621
    - type: recall_at_5
      value: 35.179
  - task:
      type: Retrieval
    dataset:
      name: MTEB ClimateFEVER
      type: climate-fever
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 10.581
    - type: map_at_10
      value: 18.944
    - type: map_at_100
      value: 20.812
    - type: map_at_1000
      value: 21.002000000000002
    - type: map_at_3
      value: 15.661
    - type: map_at_5
      value: 17.502000000000002
    - type: mrr_at_1
      value: 23.388
    - type: mrr_at_10
      value: 34.263
    - type: mrr_at_100
      value: 35.364000000000004
    - type: mrr_at_1000
      value: 35.409
    - type: mrr_at_3
      value: 30.586000000000002
    - type: mrr_at_5
      value: 32.928000000000004
    - type: ndcg_at_1
      value: 23.388
    - type: ndcg_at_10
      value: 26.56
    - type: ndcg_at_100
      value: 34.248
    - type: ndcg_at_1000
      value: 37.779
    - type: ndcg_at_3
      value: 21.179000000000002
    - type: ndcg_at_5
      value: 23.504
    - type: precision_at_1
      value: 23.388
    - type: precision_at_10
      value: 8.476
    - type: precision_at_100
      value: 1.672
    - type: precision_at_1000
      value: 0.233
    - type: precision_at_3
      value: 15.852
    - type: precision_at_5
      value: 12.73
    - type: recall_at_1
      value: 10.581
    - type: recall_at_10
      value: 32.512
    - type: recall_at_100
      value: 59.313
    - type: recall_at_1000
      value: 79.25
    - type: recall_at_3
      value: 19.912
    - type: recall_at_5
      value: 25.832
  - task:
      type: Retrieval
    dataset:
      name: MTEB DBPedia
      type: dbpedia-entity
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 9.35
    - type: map_at_10
      value: 20.134
    - type: map_at_100
      value: 28.975
    - type: map_at_1000
      value: 30.709999999999997
    - type: map_at_3
      value: 14.513000000000002
    - type: map_at_5
      value: 16.671
    - type: mrr_at_1
      value: 69.75
    - type: mrr_at_10
      value: 77.67699999999999
    - type: mrr_at_100
      value: 77.97500000000001
    - type: mrr_at_1000
      value: 77.985
    - type: mrr_at_3
      value: 76.292
    - type: mrr_at_5
      value: 77.179
    - type: ndcg_at_1
      value: 56.49999999999999
    - type: ndcg_at_10
      value: 42.226
    - type: ndcg_at_100
      value: 47.562
    - type: ndcg_at_1000
      value: 54.923
    - type: ndcg_at_3
      value: 46.564
    - type: ndcg_at_5
      value: 43.830000000000005
    - type: precision_at_1
      value: 69.75
    - type: precision_at_10
      value: 33.525
    - type: precision_at_100
      value: 11.035
    - type: precision_at_1000
      value: 2.206
    - type: precision_at_3
      value: 49.75
    - type: precision_at_5
      value: 42
    - type: recall_at_1
      value: 9.35
    - type: recall_at_10
      value: 25.793
    - type: recall_at_100
      value: 54.186
    - type: recall_at_1000
      value: 77.81
    - type: recall_at_3
      value: 15.770000000000001
    - type: recall_at_5
      value: 19.09
  - task:
      type: Classification
    dataset:
      name: MTEB EmotionClassification
      type: mteb/emotion
      config: default
      split: test
      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
    metrics:
    - type: accuracy
      value: 46.945
    - type: f1
      value: 42.07407842992542
  - task:
      type: Retrieval
    dataset:
      name: MTEB FEVER
      type: fever
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 71.04599999999999
    - type: map_at_10
      value: 80.718
    - type: map_at_100
      value: 80.961
    - type: map_at_1000
      value: 80.974
    - type: map_at_3
      value: 79.49199999999999
    - type: map_at_5
      value: 80.32000000000001
    - type: mrr_at_1
      value: 76.388
    - type: mrr_at_10
      value: 85.214
    - type: mrr_at_100
      value: 85.302
    - type: mrr_at_1000
      value: 85.302
    - type: mrr_at_3
      value: 84.373
    - type: mrr_at_5
      value: 84.979
    - type: ndcg_at_1
      value: 76.388
    - type: ndcg_at_10
      value: 84.987
    - type: ndcg_at_100
      value: 85.835
    - type: ndcg_at_1000
      value: 86.04899999999999
    - type: ndcg_at_3
      value: 83.04
    - type: ndcg_at_5
      value: 84.22500000000001
    - type: precision_at_1
      value: 76.388
    - type: precision_at_10
      value: 10.35
    - type: precision_at_100
      value: 1.099
    - type: precision_at_1000
      value: 0.11399999999999999
    - type: precision_at_3
      value: 32.108
    - type: precision_at_5
      value: 20.033
    - type: recall_at_1
      value: 71.04599999999999
    - type: recall_at_10
      value: 93.547
    - type: recall_at_100
      value: 96.887
    - type: recall_at_1000
      value: 98.158
    - type: recall_at_3
      value: 88.346
    - type: recall_at_5
      value: 91.321
  - task:
      type: Retrieval
    dataset:
      name: MTEB FiQA2018
      type: fiqa
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 19.8
    - type: map_at_10
      value: 31.979999999999997
    - type: map_at_100
      value: 33.876
    - type: map_at_1000
      value: 34.056999999999995
    - type: map_at_3
      value: 28.067999999999998
    - type: map_at_5
      value: 30.066
    - type: mrr_at_1
      value: 38.735
    - type: mrr_at_10
      value: 47.749
    - type: mrr_at_100
      value: 48.605
    - type: mrr_at_1000
      value: 48.644999999999996
    - type: mrr_at_3
      value: 45.165
    - type: mrr_at_5
      value: 46.646
    - type: ndcg_at_1
      value: 38.735
    - type: ndcg_at_10
      value: 39.883
    - type: ndcg_at_100
      value: 46.983000000000004
    - type: ndcg_at_1000
      value: 50.043000000000006
    - type: ndcg_at_3
      value: 35.943000000000005
    - type: ndcg_at_5
      value: 37.119
    - type: precision_at_1
      value: 38.735
    - type: precision_at_10
      value: 10.940999999999999
    - type: precision_at_100
      value: 1.836
    - type: precision_at_1000
      value: 0.23900000000000002
    - type: precision_at_3
      value: 23.817
    - type: precision_at_5
      value: 17.346
    - type: recall_at_1
      value: 19.8
    - type: recall_at_10
      value: 47.082
    - type: recall_at_100
      value: 73.247
    - type: recall_at_1000
      value: 91.633
    - type: recall_at_3
      value: 33.201
    - type: recall_at_5
      value: 38.81
  - task:
      type: Retrieval
    dataset:
      name: MTEB HotpotQA
      type: hotpotqa
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 38.102999999999994
    - type: map_at_10
      value: 60.547
    - type: map_at_100
      value: 61.466
    - type: map_at_1000
      value: 61.526
    - type: map_at_3
      value: 56.973
    - type: map_at_5
      value: 59.244
    - type: mrr_at_1
      value: 76.205
    - type: mrr_at_10
      value: 82.816
    - type: mrr_at_100
      value: 83.002
    - type: mrr_at_1000
      value: 83.009
    - type: mrr_at_3
      value: 81.747
    - type: mrr_at_5
      value: 82.467
    - type: ndcg_at_1
      value: 76.205
    - type: ndcg_at_10
      value: 69.15
    - type: ndcg_at_100
      value: 72.297
    - type: ndcg_at_1000
      value: 73.443
    - type: ndcg_at_3
      value: 64.07000000000001
    - type: ndcg_at_5
      value: 66.96600000000001
    - type: precision_at_1
      value: 76.205
    - type: precision_at_10
      value: 14.601
    - type: precision_at_100
      value: 1.7049999999999998
    - type: precision_at_1000
      value: 0.186
    - type: precision_at_3
      value: 41.202
    - type: precision_at_5
      value: 27.006000000000004
    - type: recall_at_1
      value: 38.102999999999994
    - type: recall_at_10
      value: 73.005
    - type: recall_at_100
      value: 85.253
    - type: recall_at_1000
      value: 92.795
    - type: recall_at_3
      value: 61.803
    - type: recall_at_5
      value: 67.515
  - task:
      type: Classification
    dataset:
      name: MTEB ImdbClassification
      type: mteb/imdb
      config: default
      split: test
      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
    metrics:
    - type: accuracy
      value: 86.15
    - type: ap
      value: 80.36282825265391
    - type: f1
      value: 86.07368510726472
  - task:
      type: Retrieval
    dataset:
      name: MTEB MSMARCO
      type: msmarco
      config: default
      split: dev
      revision: None
    metrics:
    - type: map_at_1
      value: 22.6
    - type: map_at_10
      value: 34.887
    - type: map_at_100
      value: 36.069
    - type: map_at_1000
      value: 36.115
    - type: map_at_3
      value: 31.067
    - type: map_at_5
      value: 33.300000000000004
    - type: mrr_at_1
      value: 23.238
    - type: mrr_at_10
      value: 35.47
    - type: mrr_at_100
      value: 36.599
    - type: mrr_at_1000
      value: 36.64
    - type: mrr_at_3
      value: 31.735999999999997
    - type: mrr_at_5
      value: 33.939
    - type: ndcg_at_1
      value: 23.252
    - type: ndcg_at_10
      value: 41.765
    - type: ndcg_at_100
      value: 47.402
    - type: ndcg_at_1000
      value: 48.562
    - type: ndcg_at_3
      value: 34.016999999999996
    - type: ndcg_at_5
      value: 38.016
    - type: precision_at_1
      value: 23.252
    - type: precision_at_10
      value: 6.569
    - type: precision_at_100
      value: 0.938
    - type: precision_at_1000
      value: 0.104
    - type: precision_at_3
      value: 14.479000000000001
    - type: precision_at_5
      value: 10.722
    - type: recall_at_1
      value: 22.6
    - type: recall_at_10
      value: 62.919000000000004
    - type: recall_at_100
      value: 88.82
    - type: recall_at_1000
      value: 97.71600000000001
    - type: recall_at_3
      value: 41.896
    - type: recall_at_5
      value: 51.537
  - task:
      type: Classification
    dataset:
      name: MTEB MTOPDomainClassification (en)
      type: mteb/mtop_domain
      config: en
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
    - type: accuracy
      value: 93.69357045143639
    - type: f1
      value: 93.55489858177597
  - task:
      type: Classification
    dataset:
      name: MTEB MTOPIntentClassification (en)
      type: mteb/mtop_intent
      config: en
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 75.31235750114
    - type: f1
      value: 57.891491963121155
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (en)
      type: mteb/amazon_massive_intent
      config: en
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 73.04303967720243
    - type: f1
      value: 70.51516022297616
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveScenarioClassification (en)
      type: mteb/amazon_massive_scenario
      config: en
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 77.65299260255549
    - type: f1
      value: 77.49059766538576
  - task:
      type: Clustering
    dataset:
      name: MTEB MedrxivClusteringP2P
      type: mteb/medrxiv-clustering-p2p
      config: default
      split: test
      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
    metrics:
    - type: v_measure
      value: 31.458906115906597
  - task:
      type: Clustering
    dataset:
      name: MTEB MedrxivClusteringS2S
      type: mteb/medrxiv-clustering-s2s
      config: default
      split: test
      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
    metrics:
    - type: v_measure
      value: 28.9851513122443
  - task:
      type: Reranking
    dataset:
      name: MTEB MindSmallReranking
      type: mteb/mind_small
      config: default
      split: test
      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
    metrics:
    - type: map
      value: 31.2916268497217
    - type: mrr
      value: 32.328276715593816
  - task:
      type: Retrieval
    dataset:
      name: MTEB NFCorpus
      type: nfcorpus
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 6.3740000000000006
    - type: map_at_10
      value: 13.089999999999998
    - type: map_at_100
      value: 16.512
    - type: map_at_1000
      value: 18.014
    - type: map_at_3
      value: 9.671000000000001
    - type: map_at_5
      value: 11.199
    - type: mrr_at_1
      value: 46.749
    - type: mrr_at_10
      value: 55.367
    - type: mrr_at_100
      value: 56.021
    - type: mrr_at_1000
      value: 56.058
    - type: mrr_at_3
      value: 53.30200000000001
    - type: mrr_at_5
      value: 54.773
    - type: ndcg_at_1
      value: 45.046
    - type: ndcg_at_10
      value: 35.388999999999996
    - type: ndcg_at_100
      value: 32.175
    - type: ndcg_at_1000
      value: 41.018
    - type: ndcg_at_3
      value: 40.244
    - type: ndcg_at_5
      value: 38.267
    - type: precision_at_1
      value: 46.749
    - type: precision_at_10
      value: 26.563
    - type: precision_at_100
      value: 8.074
    - type: precision_at_1000
      value: 2.099
    - type: precision_at_3
      value: 37.358000000000004
    - type: precision_at_5
      value: 33.003
    - type: recall_at_1
      value: 6.3740000000000006
    - type: recall_at_10
      value: 16.805999999999997
    - type: recall_at_100
      value: 31.871
    - type: recall_at_1000
      value: 64.098
    - type: recall_at_3
      value: 10.383000000000001
    - type: recall_at_5
      value: 13.166
  - task:
      type: Retrieval
    dataset:
      name: MTEB NQ
      type: nq
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 34.847
    - type: map_at_10
      value: 50.532
    - type: map_at_100
      value: 51.504000000000005
    - type: map_at_1000
      value: 51.528
    - type: map_at_3
      value: 46.219
    - type: map_at_5
      value: 48.868
    - type: mrr_at_1
      value: 39.137
    - type: mrr_at_10
      value: 53.157
    - type: mrr_at_100
      value: 53.839999999999996
    - type: mrr_at_1000
      value: 53.857
    - type: mrr_at_3
      value: 49.667
    - type: mrr_at_5
      value: 51.847
    - type: ndcg_at_1
      value: 39.108
    - type: ndcg_at_10
      value: 58.221000000000004
    - type: ndcg_at_100
      value: 62.021
    - type: ndcg_at_1000
      value: 62.57
    - type: ndcg_at_3
      value: 50.27199999999999
    - type: ndcg_at_5
      value: 54.623999999999995
    - type: precision_at_1
      value: 39.108
    - type: precision_at_10
      value: 9.397
    - type: precision_at_100
      value: 1.1520000000000001
    - type: precision_at_1000
      value: 0.12
    - type: precision_at_3
      value: 22.644000000000002
    - type: precision_at_5
      value: 16.141
    - type: recall_at_1
      value: 34.847
    - type: recall_at_10
      value: 78.945
    - type: recall_at_100
      value: 94.793
    - type: recall_at_1000
      value: 98.904
    - type: recall_at_3
      value: 58.56
    - type: recall_at_5
      value: 68.535
  - task:
      type: Retrieval
    dataset:
      name: MTEB QuoraRetrieval
      type: quora
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 68.728
    - type: map_at_10
      value: 82.537
    - type: map_at_100
      value: 83.218
    - type: map_at_1000
      value: 83.238
    - type: map_at_3
      value: 79.586
    - type: map_at_5
      value: 81.416
    - type: mrr_at_1
      value: 79.17999999999999
    - type: mrr_at_10
      value: 85.79299999999999
    - type: mrr_at_100
      value: 85.937
    - type: mrr_at_1000
      value: 85.938
    - type: mrr_at_3
      value: 84.748
    - type: mrr_at_5
      value: 85.431
    - type: ndcg_at_1
      value: 79.17
    - type: ndcg_at_10
      value: 86.555
    - type: ndcg_at_100
      value: 88.005
    - type: ndcg_at_1000
      value: 88.146
    - type: ndcg_at_3
      value: 83.557
    - type: ndcg_at_5
      value: 85.152
    - type: precision_at_1
      value: 79.17
    - type: precision_at_10
      value: 13.163
    - type: precision_at_100
      value: 1.52
    - type: precision_at_1000
      value: 0.156
    - type: precision_at_3
      value: 36.53
    - type: precision_at_5
      value: 24.046
    - type: recall_at_1
      value: 68.728
    - type: recall_at_10
      value: 94.217
    - type: recall_at_100
      value: 99.295
    - type: recall_at_1000
      value: 99.964
    - type: recall_at_3
      value: 85.646
    - type: recall_at_5
      value: 90.113
  - task:
      type: Clustering
    dataset:
      name: MTEB RedditClustering
      type: mteb/reddit-clustering
      config: default
      split: test
      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
    metrics:
    - type: v_measure
      value: 56.15680266226348
  - task:
      type: Clustering
    dataset:
      name: MTEB RedditClusteringP2P
      type: mteb/reddit-clustering-p2p
      config: default
      split: test
      revision: 282350215ef01743dc01b456c7f5241fa8937f16
    metrics:
    - type: v_measure
      value: 63.4318549229047
  - task:
      type: Retrieval
    dataset:
      name: MTEB SCIDOCS
      type: scidocs
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 4.353
    - type: map_at_10
      value: 10.956000000000001
    - type: map_at_100
      value: 12.873999999999999
    - type: map_at_1000
      value: 13.177
    - type: map_at_3
      value: 7.854
    - type: map_at_5
      value: 9.327
    - type: mrr_at_1
      value: 21.4
    - type: mrr_at_10
      value: 31.948999999999998
    - type: mrr_at_100
      value: 33.039
    - type: mrr_at_1000
      value: 33.106
    - type: mrr_at_3
      value: 28.449999999999996
    - type: mrr_at_5
      value: 30.535
    - type: ndcg_at_1
      value: 21.4
    - type: ndcg_at_10
      value: 18.694
    - type: ndcg_at_100
      value: 26.275
    - type: ndcg_at_1000
      value: 31.836
    - type: ndcg_at_3
      value: 17.559
    - type: ndcg_at_5
      value: 15.372
    - type: precision_at_1
      value: 21.4
    - type: precision_at_10
      value: 9.790000000000001
    - type: precision_at_100
      value: 2.0709999999999997
    - type: precision_at_1000
      value: 0.34099999999999997
    - type: precision_at_3
      value: 16.467000000000002
    - type: precision_at_5
      value: 13.54
    - type: recall_at_1
      value: 4.353
    - type: recall_at_10
      value: 19.892000000000003
    - type: recall_at_100
      value: 42.067
    - type: recall_at_1000
      value: 69.268
    - type: recall_at_3
      value: 10.042
    - type: recall_at_5
      value: 13.741999999999999
  - task:
      type: STS
    dataset:
      name: MTEB SICK-R
      type: mteb/sickr-sts
      config: default
      split: test
      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
    metrics:
    - type: cos_sim_pearson
      value: 83.75433886279843
    - type: cos_sim_spearman
      value: 78.29727771767095
    - type: euclidean_pearson
      value: 80.83057828506621
    - type: euclidean_spearman
      value: 78.35203149750356
    - type: manhattan_pearson
      value: 80.7403553891142
    - type: manhattan_spearman
      value: 78.33670488531051
  - task:
      type: STS
    dataset:
      name: MTEB STS12
      type: mteb/sts12-sts
      config: default
      split: test
      revision: a0d554a64d88156834ff5ae9920b964011b16384
    metrics:
    - type: cos_sim_pearson
      value: 84.59999465280839
    - type: cos_sim_spearman
      value: 75.79279003980383
    - type: euclidean_pearson
      value: 82.29895375956758
    - type: euclidean_spearman
      value: 77.33856514102094
    - type: manhattan_pearson
      value: 82.22694214534756
    - type: manhattan_spearman
      value: 77.3028993008695
  - task:
      type: STS
    dataset:
      name: MTEB STS13
      type: mteb/sts13-sts
      config: default
      split: test
      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
    metrics:
    - type: cos_sim_pearson
      value: 83.09296929691297
    - type: cos_sim_spearman
      value: 83.58056936846941
    - type: euclidean_pearson
      value: 83.84067483060005
    - type: euclidean_spearman
      value: 84.45155680480985
    - type: manhattan_pearson
      value: 83.82353052971942
    - type: manhattan_spearman
      value: 84.43030567861112
  - task:
      type: STS
    dataset:
      name: MTEB STS14
      type: mteb/sts14-sts
      config: default
      split: test
      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
    metrics:
    - type: cos_sim_pearson
      value: 82.74616852320915
    - type: cos_sim_spearman
      value: 79.948683747966
    - type: euclidean_pearson
      value: 81.55702283757084
    - type: euclidean_spearman
      value: 80.1721505114231
    - type: manhattan_pearson
      value: 81.52251518619441
    - type: manhattan_spearman
      value: 80.1469800135577
  - task:
      type: STS
    dataset:
      name: MTEB STS15
      type: mteb/sts15-sts
      config: default
      split: test
      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
    metrics:
    - type: cos_sim_pearson
      value: 87.97170104226318
    - type: cos_sim_spearman
      value: 88.82021731518206
    - type: euclidean_pearson
      value: 87.92950547187615
    - type: euclidean_spearman
      value: 88.67043634645866
    - type: manhattan_pearson
      value: 87.90668112827639
    - type: manhattan_spearman
      value: 88.64471082785317
  - task:
      type: STS
    dataset:
      name: MTEB STS16
      type: mteb/sts16-sts
      config: default
      split: test
      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
    metrics:
    - type: cos_sim_pearson
      value: 83.02790375770599
    - type: cos_sim_spearman
      value: 84.46308496590792
    - type: euclidean_pearson
      value: 84.29430000414911
    - type: euclidean_spearman
      value: 84.77298303589936
    - type: manhattan_pearson
      value: 84.23919291368665
    - type: manhattan_spearman
      value: 84.75272234871308
  - 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: 87.62885108477064
    - type: cos_sim_spearman
      value: 87.58456196391622
    - type: euclidean_pearson
      value: 88.2602775281007
    - type: euclidean_spearman
      value: 87.51556278299846
    - type: manhattan_pearson
      value: 88.11224053672842
    - type: manhattan_spearman
      value: 87.4336094383095
  - task:
      type: STS
    dataset:
      name: MTEB STS22 (en)
      type: mteb/sts22-crosslingual-sts
      config: en
      split: test
      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
    metrics:
    - type: cos_sim_pearson
      value: 63.98187965128411
    - type: cos_sim_spearman
      value: 64.0653163219731
    - type: euclidean_pearson
      value: 62.30616725924099
    - type: euclidean_spearman
      value: 61.556971332295916
    - type: manhattan_pearson
      value: 62.07642330128549
    - type: manhattan_spearman
      value: 61.155494129828
  - task:
      type: STS
    dataset:
      name: MTEB STSBenchmark
      type: mteb/stsbenchmark-sts
      config: default
      split: test
      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
    metrics:
    - type: cos_sim_pearson
      value: 85.6089703921826
    - type: cos_sim_spearman
      value: 86.52303197250791
    - type: euclidean_pearson
      value: 85.95801955963246
    - type: euclidean_spearman
      value: 86.25242424112962
    - type: manhattan_pearson
      value: 85.88829100470312
    - type: manhattan_spearman
      value: 86.18742955805165
  - task:
      type: Reranking
    dataset:
      name: MTEB SciDocsRR
      type: mteb/scidocs-reranking
      config: default
      split: test
      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
    metrics:
    - type: map
      value: 83.02282098487036
    - type: mrr
      value: 95.05126409538174
  - task:
      type: Retrieval
    dataset:
      name: MTEB SciFact
      type: scifact
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 55.928
    - type: map_at_10
      value: 67.308
    - type: map_at_100
      value: 67.89500000000001
    - type: map_at_1000
      value: 67.91199999999999
    - type: map_at_3
      value: 65.091
    - type: map_at_5
      value: 66.412
    - type: mrr_at_1
      value: 58.667
    - type: mrr_at_10
      value: 68.401
    - type: mrr_at_100
      value: 68.804
    - type: mrr_at_1000
      value: 68.819
    - type: mrr_at_3
      value: 66.72200000000001
    - type: mrr_at_5
      value: 67.72200000000001
    - type: ndcg_at_1
      value: 58.667
    - type: ndcg_at_10
      value: 71.944
    - type: ndcg_at_100
      value: 74.464
    - type: ndcg_at_1000
      value: 74.82799999999999
    - type: ndcg_at_3
      value: 68.257
    - type: ndcg_at_5
      value: 70.10300000000001
    - type: precision_at_1
      value: 58.667
    - type: precision_at_10
      value: 9.533
    - type: precision_at_100
      value: 1.09
    - type: precision_at_1000
      value: 0.11199999999999999
    - type: precision_at_3
      value: 27.222
    - type: precision_at_5
      value: 17.533
    - type: recall_at_1
      value: 55.928
    - type: recall_at_10
      value: 84.65
    - type: recall_at_100
      value: 96.267
    - type: recall_at_1000
      value: 99
    - type: recall_at_3
      value: 74.656
    - type: recall_at_5
      value: 79.489
  - 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: 94.5795129511524
    - type: cos_sim_f1
      value: 89.34673366834171
    - type: cos_sim_precision
      value: 89.79797979797979
    - type: cos_sim_recall
      value: 88.9
    - type: dot_accuracy
      value: 99.53465346534654
    - type: dot_ap
      value: 81.56492504352725
    - type: dot_f1
      value: 76.33816908454227
    - type: dot_precision
      value: 76.37637637637637
    - type: dot_recall
      value: 76.3
    - type: euclidean_accuracy
      value: 99.78514851485149
    - type: euclidean_ap
      value: 94.59134620408962
    - type: euclidean_f1
      value: 88.96484375
    - type: euclidean_precision
      value: 86.92748091603053
    - type: euclidean_recall
      value: 91.10000000000001
    - type: manhattan_accuracy
      value: 99.78415841584159
    - type: manhattan_ap
      value: 94.5190197328845
    - type: manhattan_f1
      value: 88.84462151394423
    - type: manhattan_precision
      value: 88.4920634920635
    - type: manhattan_recall
      value: 89.2
    - type: max_accuracy
      value: 99.79009900990098
    - type: max_ap
      value: 94.59134620408962
    - type: max_f1
      value: 89.34673366834171
  - task:
      type: Clustering
    dataset:
      name: MTEB StackExchangeClustering
      type: mteb/stackexchange-clustering
      config: default
      split: test
      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
    metrics:
    - type: v_measure
      value: 65.1487505617497
  - task:
      type: Clustering
    dataset:
      name: MTEB StackExchangeClusteringP2P
      type: mteb/stackexchange-clustering-p2p
      config: default
      split: test
      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
    metrics:
    - type: v_measure
      value: 32.502518166001856
  - task:
      type: Reranking
    dataset:
      name: MTEB StackOverflowDupQuestions
      type: mteb/stackoverflowdupquestions-reranking
      config: default
      split: test
      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
    metrics:
    - type: map
      value: 50.33775480236701
    - type: mrr
      value: 51.17302223919871
  - task:
      type: Summarization
    dataset:
      name: MTEB SummEval
      type: mteb/summeval
      config: default
      split: test
      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
    metrics:
    - type: cos_sim_pearson
      value: 30.561111309808208
    - type: cos_sim_spearman
      value: 30.2839254379273
    - type: dot_pearson
      value: 29.560242291401973
    - type: dot_spearman
      value: 30.51527274679116
  - task:
      type: Retrieval
    dataset:
      name: MTEB TRECCOVID
      type: trec-covid
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 0.215
    - type: map_at_10
      value: 1.752
    - type: map_at_100
      value: 9.258
    - type: map_at_1000
      value: 23.438
    - type: map_at_3
      value: 0.6
    - type: map_at_5
      value: 0.968
    - type: mrr_at_1
      value: 84
    - type: mrr_at_10
      value: 91.333
    - type: mrr_at_100
      value: 91.333
    - type: mrr_at_1000
      value: 91.333
    - type: mrr_at_3
      value: 91.333
    - type: mrr_at_5
      value: 91.333
    - type: ndcg_at_1
      value: 75
    - type: ndcg_at_10
      value: 69.596
    - type: ndcg_at_100
      value: 51.970000000000006
    - type: ndcg_at_1000
      value: 48.864999999999995
    - type: ndcg_at_3
      value: 73.92699999999999
    - type: ndcg_at_5
      value: 73.175
    - type: precision_at_1
      value: 84
    - type: precision_at_10
      value: 74
    - type: precision_at_100
      value: 53.2
    - type: precision_at_1000
      value: 21.836
    - type: precision_at_3
      value: 79.333
    - type: precision_at_5
      value: 78.4
    - type: recall_at_1
      value: 0.215
    - type: recall_at_10
      value: 1.9609999999999999
    - type: recall_at_100
      value: 12.809999999999999
    - type: recall_at_1000
      value: 46.418
    - type: recall_at_3
      value: 0.6479999999999999
    - type: recall_at_5
      value: 1.057
  - task:
      type: Retrieval
    dataset:
      name: MTEB Touche2020
      type: webis-touche2020
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 3.066
    - type: map_at_10
      value: 10.508000000000001
    - type: map_at_100
      value: 16.258
    - type: map_at_1000
      value: 17.705000000000002
    - type: map_at_3
      value: 6.157
    - type: map_at_5
      value: 7.510999999999999
    - type: mrr_at_1
      value: 34.694
    - type: mrr_at_10
      value: 48.786
    - type: mrr_at_100
      value: 49.619
    - type: mrr_at_1000
      value: 49.619
    - type: mrr_at_3
      value: 45.918
    - type: mrr_at_5
      value: 46.837
    - type: ndcg_at_1
      value: 31.633
    - type: ndcg_at_10
      value: 26.401999999999997
    - type: ndcg_at_100
      value: 37.139
    - type: ndcg_at_1000
      value: 48.012
    - type: ndcg_at_3
      value: 31.875999999999998
    - type: ndcg_at_5
      value: 27.383000000000003
    - type: precision_at_1
      value: 34.694
    - type: precision_at_10
      value: 22.857
    - type: precision_at_100
      value: 7.611999999999999
    - type: precision_at_1000
      value: 1.492
    - type: precision_at_3
      value: 33.333
    - type: precision_at_5
      value: 26.122
    - type: recall_at_1
      value: 3.066
    - type: recall_at_10
      value: 16.239
    - type: recall_at_100
      value: 47.29
    - type: recall_at_1000
      value: 81.137
    - type: recall_at_3
      value: 7.069
    - type: recall_at_5
      value: 9.483
  - task:
      type: Classification
    dataset:
      name: MTEB ToxicConversationsClassification
      type: mteb/toxic_conversations_50k
      config: default
      split: test
      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
    metrics:
    - type: accuracy
      value: 72.1126
    - type: ap
      value: 14.710862719285753
    - type: f1
      value: 55.437808972378846
  - task:
      type: Classification
    dataset:
      name: MTEB TweetSentimentExtractionClassification
      type: mteb/tweet_sentiment_extraction
      config: default
      split: test
      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
    metrics:
    - type: accuracy
      value: 60.39049235993209
    - type: f1
      value: 60.69810537250234
  - task:
      type: Clustering
    dataset:
      name: MTEB TwentyNewsgroupsClustering
      type: mteb/twentynewsgroups-clustering
      config: default
      split: test
      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
    metrics:
    - type: v_measure
      value: 48.15576640316866
  - task:
      type: PairClassification
    dataset:
      name: MTEB TwitterSemEval2015
      type: mteb/twittersemeval2015-pairclassification
      config: default
      split: test
      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
    metrics:
    - type: cos_sim_accuracy
      value: 86.52917684925792
    - type: cos_sim_ap
      value: 75.97497873817315
    - type: cos_sim_f1
      value: 70.01151926276718
    - type: cos_sim_precision
      value: 67.98409147402435
    - type: cos_sim_recall
      value: 72.16358839050132
    - type: dot_accuracy
      value: 82.47004828038385
    - type: dot_ap
      value: 62.48739894974198
    - type: dot_f1
      value: 59.13107511045656
    - type: dot_precision
      value: 55.27765029830197
    - type: dot_recall
      value: 63.562005277044854
    - type: euclidean_accuracy
      value: 86.46361089586935
    - type: euclidean_ap
      value: 75.59282886839452
    - type: euclidean_f1
      value: 69.6465443945099
    - type: euclidean_precision
      value: 64.52847175331982
    - type: euclidean_recall
      value: 75.64643799472296
    - type: manhattan_accuracy
      value: 86.43380818978363
    - type: manhattan_ap
      value: 75.5742420974403
    - type: manhattan_f1
      value: 69.8636926889715
    - type: manhattan_precision
      value: 65.8644859813084
    - type: manhattan_recall
      value: 74.37994722955145
    - type: max_accuracy
      value: 86.52917684925792
    - type: max_ap
      value: 75.97497873817315
    - type: max_f1
      value: 70.01151926276718
  - task:
      type: PairClassification
    dataset:
      name: MTEB TwitterURLCorpus
      type: mteb/twitterurlcorpus-pairclassification
      config: default
      split: test
      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
    metrics:
    - type: cos_sim_accuracy
      value: 89.29056545193464
    - type: cos_sim_ap
      value: 86.63028865482376
    - type: cos_sim_f1
      value: 79.18166458532285
    - type: cos_sim_precision
      value: 75.70585756426465
    - type: cos_sim_recall
      value: 82.99199260856174
    - type: dot_accuracy
      value: 85.23305002522606
    - type: dot_ap
      value: 76.0482687263196
    - type: dot_f1
      value: 70.80484330484332
    - type: dot_precision
      value: 65.86933474688577
    - type: dot_recall
      value: 76.53988296889437
    - type: euclidean_accuracy
      value: 89.26145845461248
    - type: euclidean_ap
      value: 86.54073288416006
    - type: euclidean_f1
      value: 78.9721371479794
    - type: euclidean_precision
      value: 76.68649354417525
    - type: euclidean_recall
      value: 81.39821373575609
    - type: manhattan_accuracy
      value: 89.22847052431405
    - type: manhattan_ap
      value: 86.51250729037905
    - type: manhattan_f1
      value: 78.94601825044894
    - type: manhattan_precision
      value: 75.32694594027555
    - type: manhattan_recall
      value: 82.93039728980598
    - type: max_accuracy
      value: 89.29056545193464
    - type: max_ap
      value: 86.63028865482376
    - type: max_f1
      value: 79.18166458532285
---

# abhiraj1/e5-base-v2-Q4_K_M-GGUF
This model was converted to GGUF format from [`intfloat/e5-base-v2`](https://huggingface.co/intfloat/e5-base-v2) 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/intfloat/e5-base-v2) 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 abhiraj1/e5-base-v2-Q4_K_M-GGUF --hf-file e5-base-v2-q4_k_m.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo abhiraj1/e5-base-v2-Q4_K_M-GGUF --hf-file e5-base-v2-q4_k_m.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 abhiraj1/e5-base-v2-Q4_K_M-GGUF --hf-file e5-base-v2-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo abhiraj1/e5-base-v2-Q4_K_M-GGUF --hf-file e5-base-v2-q4_k_m.gguf -c 2048
```
