sentence-transformers gguf feature-extraction sentence-similarity mteb llama-cpp gguf-my-repo en base_model:infgrad/stella-base-en-v2 base_model:quantized:infgrad/stella-base-en-v2 license:mit model-index autotrain_compatible endpoints_compatible region:us
---
base_model: infgrad/stella-base-en-v2
language:
- en
license: mit
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
- llama-cpp
- gguf-my-repo
model-index:
- name: stella-base-en-v2
  results:
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonCounterfactualClassification (en)
      type: mteb/amazon_counterfactual
      config: en
      split: test
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
    metrics:
    - type: accuracy
      value: 77.19402985074628
    - type: ap
      value: 40.43267503017359
    - type: f1
      value: 71.15585210518594
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonPolarityClassification
      type: mteb/amazon_polarity
      config: default
      split: test
      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
    metrics:
    - type: accuracy
      value: 93.256675
    - type: ap
      value: 90.00824833079179
    - type: f1
      value: 93.2473146151734
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonReviewsClassification (en)
      type: mteb/amazon_reviews_multi
      config: en
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 49.612
    - type: f1
      value: 48.530785631574304
  - task:
      type: Retrieval
    dataset:
      name: MTEB ArguAna
      type: arguana
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 37.411
    - type: map_at_10
      value: 52.673
    - type: map_at_100
      value: 53.410999999999994
    - type: map_at_1000
      value: 53.415
    - type: map_at_3
      value: 48.495
    - type: map_at_5
      value: 51.183
    - type: mrr_at_1
      value: 37.838
    - type: mrr_at_10
      value: 52.844
    - type: mrr_at_100
      value: 53.581999999999994
    - type: mrr_at_1000
      value: 53.586
    - type: mrr_at_3
      value: 48.672
    - type: mrr_at_5
      value: 51.272
    - type: ndcg_at_1
      value: 37.411
    - type: ndcg_at_10
      value: 60.626999999999995
    - type: ndcg_at_100
      value: 63.675000000000004
    - type: ndcg_at_1000
      value: 63.776999999999994
    - type: ndcg_at_3
      value: 52.148
    - type: ndcg_at_5
      value: 57.001999999999995
    - type: precision_at_1
      value: 37.411
    - type: precision_at_10
      value: 8.578
    - type: precision_at_100
      value: 0.989
    - type: precision_at_1000
      value: 0.1
    - type: precision_at_3
      value: 20.91
    - type: precision_at_5
      value: 14.908
    - type: recall_at_1
      value: 37.411
    - type: recall_at_10
      value: 85.775
    - type: recall_at_100
      value: 98.86200000000001
    - type: recall_at_1000
      value: 99.644
    - type: recall_at_3
      value: 62.731
    - type: recall_at_5
      value: 74.53800000000001
  - task:
      type: Clustering
    dataset:
      name: MTEB ArxivClusteringP2P
      type: mteb/arxiv-clustering-p2p
      config: default
      split: test
      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
    metrics:
    - type: v_measure
      value: 47.24219029437865
  - task:
      type: Clustering
    dataset:
      name: MTEB ArxivClusteringS2S
      type: mteb/arxiv-clustering-s2s
      config: default
      split: test
      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
    metrics:
    - type: v_measure
      value: 40.474604844291726
  - task:
      type: Reranking
    dataset:
      name: MTEB AskUbuntuDupQuestions
      type: mteb/askubuntudupquestions-reranking
      config: default
      split: test
      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
    metrics:
    - type: map
      value: 62.720542706366054
    - type: mrr
      value: 75.59633733456448
  - task:
      type: STS
    dataset:
      name: MTEB BIOSSES
      type: mteb/biosses-sts
      config: default
      split: test
      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
    metrics:
    - type: cos_sim_pearson
      value: 86.31345008397868
    - type: cos_sim_spearman
      value: 85.94292212320399
    - type: euclidean_pearson
      value: 85.03974302774525
    - type: euclidean_spearman
      value: 85.88087251659051
    - type: manhattan_pearson
      value: 84.91900996712951
    - type: manhattan_spearman
      value: 85.96701905781116
  - task:
      type: Classification
    dataset:
      name: MTEB Banking77Classification
      type: mteb/banking77
      config: default
      split: test
      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
    metrics:
    - type: accuracy
      value: 84.72727272727273
    - type: f1
      value: 84.29572512364581
  - task:
      type: Clustering
    dataset:
      name: MTEB BiorxivClusteringP2P
      type: mteb/biorxiv-clustering-p2p
      config: default
      split: test
      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
    metrics:
    - type: v_measure
      value: 39.55532460397536
  - task:
      type: Clustering
    dataset:
      name: MTEB BiorxivClusteringS2S
      type: mteb/biorxiv-clustering-s2s
      config: default
      split: test
      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
    metrics:
    - type: v_measure
      value: 35.91195973591251
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackAndroidRetrieval
      type: BeIR/cqadupstack
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 32.822
    - type: map_at_10
      value: 44.139
    - type: map_at_100
      value: 45.786
    - type: map_at_1000
      value: 45.906000000000006
    - type: map_at_3
      value: 40.637
    - type: map_at_5
      value: 42.575
    - type: mrr_at_1
      value: 41.059
    - type: mrr_at_10
      value: 50.751000000000005
    - type: mrr_at_100
      value: 51.548
    - type: mrr_at_1000
      value: 51.583999999999996
    - type: mrr_at_3
      value: 48.236000000000004
    - type: mrr_at_5
      value: 49.838
    - type: ndcg_at_1
      value: 41.059
    - type: ndcg_at_10
      value: 50.573
    - type: ndcg_at_100
      value: 56.25
    - type: ndcg_at_1000
      value: 58.004
    - type: ndcg_at_3
      value: 45.995000000000005
    - type: ndcg_at_5
      value: 48.18
    - type: precision_at_1
      value: 41.059
    - type: precision_at_10
      value: 9.757
    - type: precision_at_100
      value: 1.609
    - type: precision_at_1000
      value: 0.20600000000000002
    - type: precision_at_3
      value: 22.222
    - type: precision_at_5
      value: 16.023
    - type: recall_at_1
      value: 32.822
    - type: recall_at_10
      value: 61.794000000000004
    - type: recall_at_100
      value: 85.64699999999999
    - type: recall_at_1000
      value: 96.836
    - type: recall_at_3
      value: 47.999
    - type: recall_at_5
      value: 54.376999999999995
    - type: map_at_1
      value: 29.579
    - type: map_at_10
      value: 39.787
    - type: map_at_100
      value: 40.976
    - type: map_at_1000
      value: 41.108
    - type: map_at_3
      value: 36.819
    - type: map_at_5
      value: 38.437
    - type: mrr_at_1
      value: 37.516
    - type: mrr_at_10
      value: 45.822
    - type: mrr_at_100
      value: 46.454
    - type: mrr_at_1000
      value: 46.495999999999995
    - type: mrr_at_3
      value: 43.556
    - type: mrr_at_5
      value: 44.814
    - type: ndcg_at_1
      value: 37.516
    - type: ndcg_at_10
      value: 45.5
    - type: ndcg_at_100
      value: 49.707
    - type: ndcg_at_1000
      value: 51.842
    - type: ndcg_at_3
      value: 41.369
    - type: ndcg_at_5
      value: 43.161
    - type: precision_at_1
      value: 37.516
    - type: precision_at_10
      value: 8.713
    - type: precision_at_100
      value: 1.38
    - type: precision_at_1000
      value: 0.188
    - type: precision_at_3
      value: 20.233999999999998
    - type: precision_at_5
      value: 14.280000000000001
    - type: recall_at_1
      value: 29.579
    - type: recall_at_10
      value: 55.458
    - type: recall_at_100
      value: 73.49799999999999
    - type: recall_at_1000
      value: 87.08200000000001
    - type: recall_at_3
      value: 42.858000000000004
    - type: recall_at_5
      value: 48.215
    - type: map_at_1
      value: 40.489999999999995
    - type: map_at_10
      value: 53.313
    - type: map_at_100
      value: 54.290000000000006
    - type: map_at_1000
      value: 54.346000000000004
    - type: map_at_3
      value: 49.983
    - type: map_at_5
      value: 51.867
    - type: mrr_at_1
      value: 46.27
    - type: mrr_at_10
      value: 56.660999999999994
    - type: mrr_at_100
      value: 57.274
    - type: mrr_at_1000
      value: 57.301
    - type: mrr_at_3
      value: 54.138
    - type: mrr_at_5
      value: 55.623999999999995
    - type: ndcg_at_1
      value: 46.27
    - type: ndcg_at_10
      value: 59.192
    - type: ndcg_at_100
      value: 63.026
    - type: ndcg_at_1000
      value: 64.079
    - type: ndcg_at_3
      value: 53.656000000000006
    - type: ndcg_at_5
      value: 56.387
    - type: precision_at_1
      value: 46.27
    - type: precision_at_10
      value: 9.511
    - type: precision_at_100
      value: 1.23
    - type: precision_at_1000
      value: 0.136
    - type: precision_at_3
      value: 24.096
    - type: precision_at_5
      value: 16.476
    - type: recall_at_1
      value: 40.489999999999995
    - type: recall_at_10
      value: 73.148
    - type: recall_at_100
      value: 89.723
    - type: recall_at_1000
      value: 97.073
    - type: recall_at_3
      value: 58.363
    - type: recall_at_5
      value: 65.083
    - type: map_at_1
      value: 26.197
    - type: map_at_10
      value: 35.135
    - type: map_at_100
      value: 36.14
    - type: map_at_1000
      value: 36.216
    - type: map_at_3
      value: 32.358
    - type: map_at_5
      value: 33.814
    - type: mrr_at_1
      value: 28.475
    - type: mrr_at_10
      value: 37.096000000000004
    - type: mrr_at_100
      value: 38.006
    - type: mrr_at_1000
      value: 38.06
    - type: mrr_at_3
      value: 34.52
    - type: mrr_at_5
      value: 35.994
    - type: ndcg_at_1
      value: 28.475
    - type: ndcg_at_10
      value: 40.263
    - type: ndcg_at_100
      value: 45.327
    - type: ndcg_at_1000
      value: 47.225
    - type: ndcg_at_3
      value: 34.882000000000005
    - type: ndcg_at_5
      value: 37.347
    - type: precision_at_1
      value: 28.475
    - type: precision_at_10
      value: 6.249
    - type: precision_at_100
      value: 0.919
    - type: precision_at_1000
      value: 0.11199999999999999
    - type: precision_at_3
      value: 14.689
    - type: precision_at_5
      value: 10.237
    - type: recall_at_1
      value: 26.197
    - type: recall_at_10
      value: 54.17999999999999
    - type: recall_at_100
      value: 77.768
    - type: recall_at_1000
      value: 91.932
    - type: recall_at_3
      value: 39.804
    - type: recall_at_5
      value: 45.660000000000004
    - type: map_at_1
      value: 16.683
    - type: map_at_10
      value: 25.013999999999996
    - type: map_at_100
      value: 26.411
    - type: map_at_1000
      value: 26.531
    - type: map_at_3
      value: 22.357
    - type: map_at_5
      value: 23.982999999999997
    - type: mrr_at_1
      value: 20.896
    - type: mrr_at_10
      value: 29.758000000000003
    - type: mrr_at_100
      value: 30.895
    - type: mrr_at_1000
      value: 30.964999999999996
    - type: mrr_at_3
      value: 27.177
    - type: mrr_at_5
      value: 28.799999999999997
    - type: ndcg_at_1
      value: 20.896
    - type: ndcg_at_10
      value: 30.294999999999998
    - type: ndcg_at_100
      value: 36.68
    - type: ndcg_at_1000
      value: 39.519
    - type: ndcg_at_3
      value: 25.480999999999998
    - type: ndcg_at_5
      value: 28.027
    - type: precision_at_1
      value: 20.896
    - type: precision_at_10
      value: 5.56
    - type: precision_at_100
      value: 1.006
    - type: precision_at_1000
      value: 0.13899999999999998
    - type: precision_at_3
      value: 12.231
    - type: precision_at_5
      value: 9.104
    - type: recall_at_1
      value: 16.683
    - type: recall_at_10
      value: 41.807
    - type: recall_at_100
      value: 69.219
    - type: recall_at_1000
      value: 89.178
    - type: recall_at_3
      value: 28.772
    - type: recall_at_5
      value: 35.167
    - type: map_at_1
      value: 30.653000000000002
    - type: map_at_10
      value: 41.21
    - type: map_at_100
      value: 42.543
    - type: map_at_1000
      value: 42.657000000000004
    - type: map_at_3
      value: 38.094
    - type: map_at_5
      value: 39.966
    - type: mrr_at_1
      value: 37.824999999999996
    - type: mrr_at_10
      value: 47.087
    - type: mrr_at_100
      value: 47.959
    - type: mrr_at_1000
      value: 48.003
    - type: mrr_at_3
      value: 45.043
    - type: mrr_at_5
      value: 46.352
    - type: ndcg_at_1
      value: 37.824999999999996
    - type: ndcg_at_10
      value: 47.158
    - type: ndcg_at_100
      value: 52.65
    - type: ndcg_at_1000
      value: 54.644999999999996
    - type: ndcg_at_3
      value: 42.632999999999996
    - type: ndcg_at_5
      value: 44.994
    - type: precision_at_1
      value: 37.824999999999996
    - type: precision_at_10
      value: 8.498999999999999
    - type: precision_at_100
      value: 1.308
    - type: precision_at_1000
      value: 0.166
    - type: precision_at_3
      value: 20.308
    - type: precision_at_5
      value: 14.283000000000001
    - type: recall_at_1
      value: 30.653000000000002
    - type: recall_at_10
      value: 58.826
    - type: recall_at_100
      value: 81.94
    - type: recall_at_1000
      value: 94.71000000000001
    - type: recall_at_3
      value: 45.965
    - type: recall_at_5
      value: 52.294
    - type: map_at_1
      value: 26.71
    - type: map_at_10
      value: 36.001
    - type: map_at_100
      value: 37.416
    - type: map_at_1000
      value: 37.522
    - type: map_at_3
      value: 32.841
    - type: map_at_5
      value: 34.515
    - type: mrr_at_1
      value: 32.647999999999996
    - type: mrr_at_10
      value: 41.43
    - type: mrr_at_100
      value: 42.433
    - type: mrr_at_1000
      value: 42.482
    - type: mrr_at_3
      value: 39.117000000000004
    - type: mrr_at_5
      value: 40.35
    - type: ndcg_at_1
      value: 32.647999999999996
    - type: ndcg_at_10
      value: 41.629
    - type: ndcg_at_100
      value: 47.707
    - type: ndcg_at_1000
      value: 49.913000000000004
    - type: ndcg_at_3
      value: 36.598000000000006
    - type: ndcg_at_5
      value: 38.696000000000005
    - type: precision_at_1
      value: 32.647999999999996
    - type: precision_at_10
      value: 7.704999999999999
    - type: precision_at_100
      value: 1.242
    - type: precision_at_1000
      value: 0.16
    - type: precision_at_3
      value: 17.314
    - type: precision_at_5
      value: 12.374
    - type: recall_at_1
      value: 26.71
    - type: recall_at_10
      value: 52.898
    - type: recall_at_100
      value: 79.08
    - type: recall_at_1000
      value: 93.94
    - type: recall_at_3
      value: 38.731
    - type: recall_at_5
      value: 44.433
    - type: map_at_1
      value: 26.510999999999996
    - type: map_at_10
      value: 35.755333333333326
    - type: map_at_100
      value: 36.97525
    - type: map_at_1000
      value: 37.08741666666667
    - type: map_at_3
      value: 32.921
    - type: map_at_5
      value: 34.45041666666667
    - type: mrr_at_1
      value: 31.578416666666666
    - type: mrr_at_10
      value: 40.06066666666667
    - type: mrr_at_100
      value: 40.93350000000001
    - type: mrr_at_1000
      value: 40.98716666666667
    - type: mrr_at_3
      value: 37.710499999999996
    - type: mrr_at_5
      value: 39.033249999999995
    - type: ndcg_at_1
      value: 31.578416666666666
    - type: ndcg_at_10
      value: 41.138666666666666
    - type: ndcg_at_100
      value: 46.37291666666666
    - type: ndcg_at_1000
      value: 48.587500000000006
    - type: ndcg_at_3
      value: 36.397083333333335
    - type: ndcg_at_5
      value: 38.539
    - type: precision_at_1
      value: 31.578416666666666
    - type: precision_at_10
      value: 7.221583333333332
    - type: precision_at_100
      value: 1.1581666666666668
    - type: precision_at_1000
      value: 0.15416666666666667
    - type: precision_at_3
      value: 16.758
    - type: precision_at_5
      value: 11.830916666666665
    - type: recall_at_1
      value: 26.510999999999996
    - type: recall_at_10
      value: 52.7825
    - type: recall_at_100
      value: 75.79675
    - type: recall_at_1000
      value: 91.10483333333335
    - type: recall_at_3
      value: 39.48233333333334
    - type: recall_at_5
      value: 45.07116666666667
    - type: map_at_1
      value: 24.564
    - type: map_at_10
      value: 31.235000000000003
    - type: map_at_100
      value: 32.124
    - type: map_at_1000
      value: 32.216
    - type: map_at_3
      value: 29.330000000000002
    - type: map_at_5
      value: 30.379
    - type: mrr_at_1
      value: 27.761000000000003
    - type: mrr_at_10
      value: 34.093
    - type: mrr_at_100
      value: 34.885
    - type: mrr_at_1000
      value: 34.957
    - type: mrr_at_3
      value: 32.388
    - type: mrr_at_5
      value: 33.269
    - type: ndcg_at_1
      value: 27.761000000000003
    - type: ndcg_at_10
      value: 35.146
    - type: ndcg_at_100
      value: 39.597
    - type: ndcg_at_1000
      value: 42.163000000000004
    - type: ndcg_at_3
      value: 31.674000000000003
    - type: ndcg_at_5
      value: 33.224
    - type: precision_at_1
      value: 27.761000000000003
    - type: precision_at_10
      value: 5.383
    - type: precision_at_100
      value: 0.836
    - type: precision_at_1000
      value: 0.11199999999999999
    - type: precision_at_3
      value: 13.599
    - type: precision_at_5
      value: 9.202
    - type: recall_at_1
      value: 24.564
    - type: recall_at_10
      value: 44.36
    - type: recall_at_100
      value: 64.408
    - type: recall_at_1000
      value: 83.892
    - type: recall_at_3
      value: 34.653
    - type: recall_at_5
      value: 38.589
    - type: map_at_1
      value: 17.01
    - type: map_at_10
      value: 24.485
    - type: map_at_100
      value: 25.573
    - type: map_at_1000
      value: 25.703
    - type: map_at_3
      value: 21.953
    - type: map_at_5
      value: 23.294999999999998
    - type: mrr_at_1
      value: 20.544
    - type: mrr_at_10
      value: 28.238000000000003
    - type: mrr_at_100
      value: 29.142000000000003
    - type: mrr_at_1000
      value: 29.219
    - type: mrr_at_3
      value: 25.802999999999997
    - type: mrr_at_5
      value: 27.105
    - type: ndcg_at_1
      value: 20.544
    - type: ndcg_at_10
      value: 29.387999999999998
    - type: ndcg_at_100
      value: 34.603
    - type: ndcg_at_1000
      value: 37.564
    - type: ndcg_at_3
      value: 24.731
    - type: ndcg_at_5
      value: 26.773000000000003
    - type: precision_at_1
      value: 20.544
    - type: precision_at_10
      value: 5.509
    - type: precision_at_100
      value: 0.9450000000000001
    - type: precision_at_1000
      value: 0.13799999999999998
    - type: precision_at_3
      value: 11.757
    - type: precision_at_5
      value: 8.596
    - type: recall_at_1
      value: 17.01
    - type: recall_at_10
      value: 40.392
    - type: recall_at_100
      value: 64.043
    - type: recall_at_1000
      value: 85.031
    - type: recall_at_3
      value: 27.293
    - type: recall_at_5
      value: 32.586999999999996
    - type: map_at_1
      value: 27.155
    - type: map_at_10
      value: 35.92
    - type: map_at_100
      value: 37.034
    - type: map_at_1000
      value: 37.139
    - type: map_at_3
      value: 33.263999999999996
    - type: map_at_5
      value: 34.61
    - type: mrr_at_1
      value: 32.183
    - type: mrr_at_10
      value: 40.099000000000004
    - type: mrr_at_100
      value: 41.001
    - type: mrr_at_1000
      value: 41.059
    - type: mrr_at_3
      value: 37.889
    - type: mrr_at_5
      value: 39.007999999999996
    - type: ndcg_at_1
      value: 32.183
    - type: ndcg_at_10
      value: 41.127
    - type: ndcg_at_100
      value: 46.464
    - type: ndcg_at_1000
      value: 48.67
    - type: ndcg_at_3
      value: 36.396
    - type: ndcg_at_5
      value: 38.313
    - type: precision_at_1
      value: 32.183
    - type: precision_at_10
      value: 6.847
    - type: precision_at_100
      value: 1.0739999999999998
    - type: precision_at_1000
      value: 0.13699999999999998
    - type: precision_at_3
      value: 16.356
    - type: precision_at_5
      value: 11.362
    - type: recall_at_1
      value: 27.155
    - type: recall_at_10
      value: 52.922000000000004
    - type: recall_at_100
      value: 76.39
    - type: recall_at_1000
      value: 91.553
    - type: recall_at_3
      value: 39.745999999999995
    - type: recall_at_5
      value: 44.637
    - type: map_at_1
      value: 25.523
    - type: map_at_10
      value: 34.268
    - type: map_at_100
      value: 35.835
    - type: map_at_1000
      value: 36.046
    - type: map_at_3
      value: 31.662000000000003
    - type: map_at_5
      value: 32.71
    - type: mrr_at_1
      value: 31.028
    - type: mrr_at_10
      value: 38.924
    - type: mrr_at_100
      value: 39.95
    - type: mrr_at_1000
      value: 40.003
    - type: mrr_at_3
      value: 36.594
    - type: mrr_at_5
      value: 37.701
    - type: ndcg_at_1
      value: 31.028
    - type: ndcg_at_10
      value: 39.848
    - type: ndcg_at_100
      value: 45.721000000000004
    - type: ndcg_at_1000
      value: 48.424
    - type: ndcg_at_3
      value: 35.329
    - type: ndcg_at_5
      value: 36.779
    - type: precision_at_1
      value: 31.028
    - type: precision_at_10
      value: 7.51
    - type: precision_at_100
      value: 1.478
    - type: precision_at_1000
      value: 0.24
    - type: precision_at_3
      value: 16.337
    - type: precision_at_5
      value: 11.383000000000001
    - type: recall_at_1
      value: 25.523
    - type: recall_at_10
      value: 50.735
    - type: recall_at_100
      value: 76.593
    - type: recall_at_1000
      value: 93.771
    - type: recall_at_3
      value: 37.574000000000005
    - type: recall_at_5
      value: 41.602
    - type: map_at_1
      value: 20.746000000000002
    - type: map_at_10
      value: 28.557
    - type: map_at_100
      value: 29.575000000000003
    - type: map_at_1000
      value: 29.659000000000002
    - type: map_at_3
      value: 25.753999999999998
    - type: map_at_5
      value: 27.254
    - type: mrr_at_1
      value: 22.736
    - type: mrr_at_10
      value: 30.769000000000002
    - type: mrr_at_100
      value: 31.655
    - type: mrr_at_1000
      value: 31.717000000000002
    - type: mrr_at_3
      value: 28.065
    - type: mrr_at_5
      value: 29.543999999999997
    - type: ndcg_at_1
      value: 22.736
    - type: ndcg_at_10
      value: 33.545
    - type: ndcg_at_100
      value: 38.743
    - type: ndcg_at_1000
      value: 41.002
    - type: ndcg_at_3
      value: 28.021
    - type: ndcg_at_5
      value: 30.586999999999996
    - type: precision_at_1
      value: 22.736
    - type: precision_at_10
      value: 5.416
    - type: precision_at_100
      value: 0.8710000000000001
    - type: precision_at_1000
      value: 0.116
    - type: precision_at_3
      value: 11.953
    - type: precision_at_5
      value: 8.651
    - type: recall_at_1
      value: 20.746000000000002
    - type: recall_at_10
      value: 46.87
    - type: recall_at_100
      value: 71.25200000000001
    - type: recall_at_1000
      value: 88.26
    - type: recall_at_3
      value: 32.029999999999994
    - type: recall_at_5
      value: 38.21
  - task:
      type: Retrieval
    dataset:
      name: MTEB ClimateFEVER
      type: climate-fever
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 12.105
    - type: map_at_10
      value: 20.577
    - type: map_at_100
      value: 22.686999999999998
    - type: map_at_1000
      value: 22.889
    - type: map_at_3
      value: 17.174
    - type: map_at_5
      value: 18.807
    - type: mrr_at_1
      value: 27.101
    - type: mrr_at_10
      value: 38.475
    - type: mrr_at_100
      value: 39.491
    - type: mrr_at_1000
      value: 39.525
    - type: mrr_at_3
      value: 34.886
    - type: mrr_at_5
      value: 36.922
    - type: ndcg_at_1
      value: 27.101
    - type: ndcg_at_10
      value: 29.002
    - type: ndcg_at_100
      value: 37.218
    - type: ndcg_at_1000
      value: 40.644000000000005
    - type: ndcg_at_3
      value: 23.464
    - type: ndcg_at_5
      value: 25.262
    - type: precision_at_1
      value: 27.101
    - type: precision_at_10
      value: 9.179
    - type: precision_at_100
      value: 1.806
    - type: precision_at_1000
      value: 0.244
    - type: precision_at_3
      value: 17.394000000000002
    - type: precision_at_5
      value: 13.342
    - type: recall_at_1
      value: 12.105
    - type: recall_at_10
      value: 35.143
    - type: recall_at_100
      value: 63.44499999999999
    - type: recall_at_1000
      value: 82.49499999999999
    - type: recall_at_3
      value: 21.489
    - type: recall_at_5
      value: 26.82
  - task:
      type: Retrieval
    dataset:
      name: MTEB DBPedia
      type: dbpedia-entity
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 8.769
    - type: map_at_10
      value: 18.619
    - type: map_at_100
      value: 26.3
    - type: map_at_1000
      value: 28.063
    - type: map_at_3
      value: 13.746
    - type: map_at_5
      value: 16.035
    - type: mrr_at_1
      value: 65.25
    - type: mrr_at_10
      value: 73.678
    - type: mrr_at_100
      value: 73.993
    - type: mrr_at_1000
      value: 74.003
    - type: mrr_at_3
      value: 72.042
    - type: mrr_at_5
      value: 72.992
    - type: ndcg_at_1
      value: 53.625
    - type: ndcg_at_10
      value: 39.638
    - type: ndcg_at_100
      value: 44.601
    - type: ndcg_at_1000
      value: 52.80200000000001
    - type: ndcg_at_3
      value: 44.727
    - type: ndcg_at_5
      value: 42.199
    - type: precision_at_1
      value: 65.25
    - type: precision_at_10
      value: 31.025000000000002
    - type: precision_at_100
      value: 10.174999999999999
    - type: precision_at_1000
      value: 2.0740000000000003
    - type: precision_at_3
      value: 48.083
    - type: precision_at_5
      value: 40.6
    - type: recall_at_1
      value: 8.769
    - type: recall_at_10
      value: 23.910999999999998
    - type: recall_at_100
      value: 51.202999999999996
    - type: recall_at_1000
      value: 77.031
    - type: recall_at_3
      value: 15.387999999999998
    - type: recall_at_5
      value: 18.919
  - task:
      type: Classification
    dataset:
      name: MTEB EmotionClassification
      type: mteb/emotion
      config: default
      split: test
      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
    metrics:
    - type: accuracy
      value: 54.47
    - type: f1
      value: 48.21839043361556
  - task:
      type: Retrieval
    dataset:
      name: MTEB FEVER
      type: fever
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 63.564
    - type: map_at_10
      value: 74.236
    - type: map_at_100
      value: 74.53699999999999
    - type: map_at_1000
      value: 74.557
    - type: map_at_3
      value: 72.556
    - type: map_at_5
      value: 73.656
    - type: mrr_at_1
      value: 68.497
    - type: mrr_at_10
      value: 78.373
    - type: mrr_at_100
      value: 78.54299999999999
    - type: mrr_at_1000
      value: 78.549
    - type: mrr_at_3
      value: 77.03
    - type: mrr_at_5
      value: 77.938
    - type: ndcg_at_1
      value: 68.497
    - type: ndcg_at_10
      value: 79.12599999999999
    - type: ndcg_at_100
      value: 80.319
    - type: ndcg_at_1000
      value: 80.71199999999999
    - type: ndcg_at_3
      value: 76.209
    - type: ndcg_at_5
      value: 77.90700000000001
    - type: precision_at_1
      value: 68.497
    - type: precision_at_10
      value: 9.958
    - type: precision_at_100
      value: 1.077
    - type: precision_at_1000
      value: 0.11299999999999999
    - type: precision_at_3
      value: 29.908
    - type: precision_at_5
      value: 18.971
    - type: recall_at_1
      value: 63.564
    - type: recall_at_10
      value: 90.05199999999999
    - type: recall_at_100
      value: 95.028
    - type: recall_at_1000
      value: 97.667
    - type: recall_at_3
      value: 82.17999999999999
    - type: recall_at_5
      value: 86.388
  - task:
      type: Retrieval
    dataset:
      name: MTEB FiQA2018
      type: fiqa
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 19.042
    - type: map_at_10
      value: 30.764999999999997
    - type: map_at_100
      value: 32.678000000000004
    - type: map_at_1000
      value: 32.881
    - type: map_at_3
      value: 26.525
    - type: map_at_5
      value: 28.932000000000002
    - type: mrr_at_1
      value: 37.653999999999996
    - type: mrr_at_10
      value: 46.597
    - type: mrr_at_100
      value: 47.413
    - type: mrr_at_1000
      value: 47.453
    - type: mrr_at_3
      value: 43.775999999999996
    - type: mrr_at_5
      value: 45.489000000000004
    - type: ndcg_at_1
      value: 37.653999999999996
    - type: ndcg_at_10
      value: 38.615
    - type: ndcg_at_100
      value: 45.513999999999996
    - type: ndcg_at_1000
      value: 48.815999999999995
    - type: ndcg_at_3
      value: 34.427
    - type: ndcg_at_5
      value: 35.954
    - type: precision_at_1
      value: 37.653999999999996
    - type: precision_at_10
      value: 10.864
    - type: precision_at_100
      value: 1.7850000000000001
    - type: precision_at_1000
      value: 0.23800000000000002
    - type: precision_at_3
      value: 22.788
    - type: precision_at_5
      value: 17.346
    - type: recall_at_1
      value: 19.042
    - type: recall_at_10
      value: 45.707
    - type: recall_at_100
      value: 71.152
    - type: recall_at_1000
      value: 90.7
    - type: recall_at_3
      value: 30.814000000000004
    - type: recall_at_5
      value: 37.478
  - task:
      type: Retrieval
    dataset:
      name: MTEB HotpotQA
      type: hotpotqa
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 38.001000000000005
    - type: map_at_10
      value: 59.611000000000004
    - type: map_at_100
      value: 60.582
    - type: map_at_1000
      value: 60.646
    - type: map_at_3
      value: 56.031
    - type: map_at_5
      value: 58.243
    - type: mrr_at_1
      value: 76.003
    - type: mrr_at_10
      value: 82.15400000000001
    - type: mrr_at_100
      value: 82.377
    - type: mrr_at_1000
      value: 82.383
    - type: mrr_at_3
      value: 81.092
    - type: mrr_at_5
      value: 81.742
    - type: ndcg_at_1
      value: 76.003
    - type: ndcg_at_10
      value: 68.216
    - type: ndcg_at_100
      value: 71.601
    - type: ndcg_at_1000
      value: 72.821
    - type: ndcg_at_3
      value: 63.109
    - type: ndcg_at_5
      value: 65.902
    - type: precision_at_1
      value: 76.003
    - type: precision_at_10
      value: 14.379
    - type: precision_at_100
      value: 1.702
    - type: precision_at_1000
      value: 0.186
    - type: precision_at_3
      value: 40.396
    - type: precision_at_5
      value: 26.442
    - type: recall_at_1
      value: 38.001000000000005
    - type: recall_at_10
      value: 71.897
    - type: recall_at_100
      value: 85.105
    - type: recall_at_1000
      value: 93.133
    - type: recall_at_3
      value: 60.594
    - type: recall_at_5
      value: 66.104
  - task:
      type: Classification
    dataset:
      name: MTEB ImdbClassification
      type: mteb/imdb
      config: default
      split: test
      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
    metrics:
    - type: accuracy
      value: 91.31280000000001
    - type: ap
      value: 87.53723467501632
    - type: f1
      value: 91.30282906596291
  - task:
      type: Retrieval
    dataset:
      name: MTEB MSMARCO
      type: msmarco
      config: default
      split: dev
      revision: None
    metrics:
    - type: map_at_1
      value: 21.917
    - type: map_at_10
      value: 34.117999999999995
    - type: map_at_100
      value: 35.283
    - type: map_at_1000
      value: 35.333999999999996
    - type: map_at_3
      value: 30.330000000000002
    - type: map_at_5
      value: 32.461
    - type: mrr_at_1
      value: 22.579
    - type: mrr_at_10
      value: 34.794000000000004
    - type: mrr_at_100
      value: 35.893
    - type: mrr_at_1000
      value: 35.937000000000005
    - type: mrr_at_3
      value: 31.091
    - type: mrr_at_5
      value: 33.173
    - type: ndcg_at_1
      value: 22.579
    - type: ndcg_at_10
      value: 40.951
    - type: ndcg_at_100
      value: 46.558
    - type: ndcg_at_1000
      value: 47.803000000000004
    - type: ndcg_at_3
      value: 33.262
    - type: ndcg_at_5
      value: 37.036
    - type: precision_at_1
      value: 22.579
    - type: precision_at_10
      value: 6.463000000000001
    - type: precision_at_100
      value: 0.928
    - type: precision_at_1000
      value: 0.104
    - type: precision_at_3
      value: 14.174000000000001
    - type: precision_at_5
      value: 10.421
    - type: recall_at_1
      value: 21.917
    - type: recall_at_10
      value: 61.885
    - type: recall_at_100
      value: 87.847
    - type: recall_at_1000
      value: 97.322
    - type: recall_at_3
      value: 41.010000000000005
    - type: recall_at_5
      value: 50.031000000000006
  - task:
      type: Classification
    dataset:
      name: MTEB MTOPDomainClassification (en)
      type: mteb/mtop_domain
      config: en
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
    - type: accuracy
      value: 93.49521203830369
    - type: f1
      value: 93.30882341740241
  - task:
      type: Classification
    dataset:
      name: MTEB MTOPIntentClassification (en)
      type: mteb/mtop_intent
      config: en
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 71.0579115367077
    - type: f1
      value: 51.2368258319339
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (en)
      type: mteb/amazon_massive_intent
      config: en
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 73.88029589778077
    - type: f1
      value: 72.34422048584663
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveScenarioClassification (en)
      type: mteb/amazon_massive_scenario
      config: en
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 78.2817753866846
    - type: f1
      value: 77.87746050004304
  - task:
      type: Clustering
    dataset:
      name: MTEB MedrxivClusteringP2P
      type: mteb/medrxiv-clustering-p2p
      config: default
      split: test
      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
    metrics:
    - type: v_measure
      value: 33.247341454119216
  - task:
      type: Clustering
    dataset:
      name: MTEB MedrxivClusteringS2S
      type: mteb/medrxiv-clustering-s2s
      config: default
      split: test
      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
    metrics:
    - type: v_measure
      value: 31.9647477166234
  - task:
      type: Reranking
    dataset:
      name: MTEB MindSmallReranking
      type: mteb/mind_small
      config: default
      split: test
      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
    metrics:
    - type: map
      value: 31.90698374676892
    - type: mrr
      value: 33.07523683771251
  - task:
      type: Retrieval
    dataset:
      name: MTEB NFCorpus
      type: nfcorpus
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 6.717
    - type: map_at_10
      value: 14.566
    - type: map_at_100
      value: 18.465999999999998
    - type: map_at_1000
      value: 20.033
    - type: map_at_3
      value: 10.863
    - type: map_at_5
      value: 12.589
    - type: mrr_at_1
      value: 49.845
    - type: mrr_at_10
      value: 58.385
    - type: mrr_at_100
      value: 58.989999999999995
    - type: mrr_at_1000
      value: 59.028999999999996
    - type: mrr_at_3
      value: 56.76
    - type: mrr_at_5
      value: 57.766
    - type: ndcg_at_1
      value: 47.678
    - type: ndcg_at_10
      value: 37.511
    - type: ndcg_at_100
      value: 34.537
    - type: ndcg_at_1000
      value: 43.612
    - type: ndcg_at_3
      value: 43.713
    - type: ndcg_at_5
      value: 41.303
    - type: precision_at_1
      value: 49.845
    - type: precision_at_10
      value: 27.307
    - type: precision_at_100
      value: 8.746
    - type: precision_at_1000
      value: 2.182
    - type: precision_at_3
      value: 40.764
    - type: precision_at_5
      value: 35.232
    - type: recall_at_1
      value: 6.717
    - type: recall_at_10
      value: 18.107
    - type: recall_at_100
      value: 33.759
    - type: recall_at_1000
      value: 67.31
    - type: recall_at_3
      value: 11.68
    - type: recall_at_5
      value: 14.557999999999998
  - task:
      type: Retrieval
    dataset:
      name: MTEB NQ
      type: nq
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 27.633999999999997
    - type: map_at_10
      value: 42.400999999999996
    - type: map_at_100
      value: 43.561
    - type: map_at_1000
      value: 43.592
    - type: map_at_3
      value: 37.865
    - type: map_at_5
      value: 40.650999999999996
    - type: mrr_at_1
      value: 31.286
    - type: mrr_at_10
      value: 44.996
    - type: mrr_at_100
      value: 45.889
    - type: mrr_at_1000
      value: 45.911
    - type: mrr_at_3
      value: 41.126000000000005
    - type: mrr_at_5
      value: 43.536
    - type: ndcg_at_1
      value: 31.257
    - type: ndcg_at_10
      value: 50.197
    - type: ndcg_at_100
      value: 55.062
    - type: ndcg_at_1000
      value: 55.81700000000001
    - type: ndcg_at_3
      value: 41.650999999999996
    - type: ndcg_at_5
      value: 46.324
    - type: precision_at_1
      value: 31.257
    - type: precision_at_10
      value: 8.508000000000001
    - type: precision_at_100
      value: 1.121
    - type: precision_at_1000
      value: 0.11900000000000001
    - type: precision_at_3
      value: 19.1
    - type: precision_at_5
      value: 14.16
    - type: recall_at_1
      value: 27.633999999999997
    - type: recall_at_10
      value: 71.40100000000001
    - type: recall_at_100
      value: 92.463
    - type: recall_at_1000
      value: 98.13199999999999
    - type: recall_at_3
      value: 49.382
    - type: recall_at_5
      value: 60.144
  - task:
      type: Retrieval
    dataset:
      name: MTEB QuoraRetrieval
      type: quora
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 71.17099999999999
    - type: map_at_10
      value: 85.036
    - type: map_at_100
      value: 85.67099999999999
    - type: map_at_1000
      value: 85.68599999999999
    - type: map_at_3
      value: 82.086
    - type: map_at_5
      value: 83.956
    - type: mrr_at_1
      value: 82.04
    - type: mrr_at_10
      value: 88.018
    - type: mrr_at_100
      value: 88.114
    - type: mrr_at_1000
      value: 88.115
    - type: mrr_at_3
      value: 87.047
    - type: mrr_at_5
      value: 87.73100000000001
    - type: ndcg_at_1
      value: 82.03
    - type: ndcg_at_10
      value: 88.717
    - type: ndcg_at_100
      value: 89.904
    - type: ndcg_at_1000
      value: 89.991
    - type: ndcg_at_3
      value: 85.89099999999999
    - type: ndcg_at_5
      value: 87.485
    - type: precision_at_1
      value: 82.03
    - type: precision_at_10
      value: 13.444999999999999
    - type: precision_at_100
      value: 1.533
    - type: precision_at_1000
      value: 0.157
    - type: precision_at_3
      value: 37.537
    - type: precision_at_5
      value: 24.692
    - type: recall_at_1
      value: 71.17099999999999
    - type: recall_at_10
      value: 95.634
    - type: recall_at_100
      value: 99.614
    - type: recall_at_1000
      value: 99.99
    - type: recall_at_3
      value: 87.48
    - type: recall_at_5
      value: 91.996
  - task:
      type: Clustering
    dataset:
      name: MTEB RedditClustering
      type: mteb/reddit-clustering
      config: default
      split: test
      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
    metrics:
    - type: v_measure
      value: 55.067219624685315
  - task:
      type: Clustering
    dataset:
      name: MTEB RedditClusteringP2P
      type: mteb/reddit-clustering-p2p
      config: default
      split: test
      revision: 282350215ef01743dc01b456c7f5241fa8937f16
    metrics:
    - type: v_measure
      value: 62.121822992300444
  - task:
      type: Retrieval
    dataset:
      name: MTEB SCIDOCS
      type: scidocs
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 4.153
    - type: map_at_10
      value: 11.024000000000001
    - type: map_at_100
      value: 13.233
    - type: map_at_1000
      value: 13.62
    - type: map_at_3
      value: 7.779999999999999
    - type: map_at_5
      value: 9.529
    - type: mrr_at_1
      value: 20.599999999999998
    - type: mrr_at_10
      value: 31.361
    - type: mrr_at_100
      value: 32.738
    - type: mrr_at_1000
      value: 32.792
    - type: mrr_at_3
      value: 28.15
    - type: mrr_at_5
      value: 30.085
    - type: ndcg_at_1
      value: 20.599999999999998
    - type: ndcg_at_10
      value: 18.583
    - type: ndcg_at_100
      value: 27.590999999999998
    - type: ndcg_at_1000
      value: 34.001
    - type: ndcg_at_3
      value: 17.455000000000002
    - type: ndcg_at_5
      value: 15.588
    - type: precision_at_1
      value: 20.599999999999998
    - type: precision_at_10
      value: 9.74
    - type: precision_at_100
      value: 2.284
    - type: precision_at_1000
      value: 0.381
    - type: precision_at_3
      value: 16.533
    - type: precision_at_5
      value: 14.02
    - type: recall_at_1
      value: 4.153
    - type: recall_at_10
      value: 19.738
    - type: recall_at_100
      value: 46.322
    - type: recall_at_1000
      value: 77.378
    - type: recall_at_3
      value: 10.048
    - type: recall_at_5
      value: 14.233
  - task:
      type: STS
    dataset:
      name: MTEB SICK-R
      type: mteb/sickr-sts
      config: default
      split: test
      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
    metrics:
    - type: cos_sim_pearson
      value: 85.07097501003639
    - type: cos_sim_spearman
      value: 81.05827848407056
    - type: euclidean_pearson
      value: 82.6279003372546
    - type: euclidean_spearman
      value: 81.00031515279802
    - type: manhattan_pearson
      value: 82.59338284959495
    - type: manhattan_spearman
      value: 80.97432711064945
  - task:
      type: STS
    dataset:
      name: MTEB STS12
      type: mteb/sts12-sts
      config: default
      split: test
      revision: a0d554a64d88156834ff5ae9920b964011b16384
    metrics:
    - type: cos_sim_pearson
      value: 86.28991993621685
    - type: cos_sim_spearman
      value: 78.71828082424351
    - type: euclidean_pearson
      value: 83.4881331520832
    - type: euclidean_spearman
      value: 78.51746826842316
    - type: manhattan_pearson
      value: 83.4109223774324
    - type: manhattan_spearman
      value: 78.431544382179
  - task:
      type: STS
    dataset:
      name: MTEB STS13
      type: mteb/sts13-sts
      config: default
      split: test
      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
    metrics:
    - type: cos_sim_pearson
      value: 83.16651661072123
    - type: cos_sim_spearman
      value: 84.88094386637867
    - type: euclidean_pearson
      value: 84.3547603585416
    - type: euclidean_spearman
      value: 84.85148665860193
    - type: manhattan_pearson
      value: 84.29648369879266
    - type: manhattan_spearman
      value: 84.76074870571124
  - task:
      type: STS
    dataset:
      name: MTEB STS14
      type: mteb/sts14-sts
      config: default
      split: test
      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
    metrics:
    - type: cos_sim_pearson
      value: 83.40596254292149
    - type: cos_sim_spearman
      value: 83.10699573133829
    - type: euclidean_pearson
      value: 83.22794776876958
    - type: euclidean_spearman
      value: 83.22583316084712
    - type: manhattan_pearson
      value: 83.15899233935681
    - type: manhattan_spearman
      value: 83.17668293648019
  - task:
      type: STS
    dataset:
      name: MTEB STS15
      type: mteb/sts15-sts
      config: default
      split: test
      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
    metrics:
    - type: cos_sim_pearson
      value: 87.27977121352563
    - type: cos_sim_spearman
      value: 88.73903130248591
    - type: euclidean_pearson
      value: 88.30685958438735
    - type: euclidean_spearman
      value: 88.79755484280406
    - type: manhattan_pearson
      value: 88.30305607758652
    - type: manhattan_spearman
      value: 88.80096577072784
  - task:
      type: STS
    dataset:
      name: MTEB STS16
      type: mteb/sts16-sts
      config: default
      split: test
      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
    metrics:
    - type: cos_sim_pearson
      value: 84.08819031430218
    - type: cos_sim_spearman
      value: 86.35414445951125
    - type: euclidean_pearson
      value: 85.4683192388315
    - type: euclidean_spearman
      value: 86.2079674669473
    - type: manhattan_pearson
      value: 85.35835702257341
    - type: manhattan_spearman
      value: 86.08483380002187
  - 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: 87.36149449801478
    - type: cos_sim_spearman
      value: 87.7102980757725
    - type: euclidean_pearson
      value: 88.16457177837161
    - type: euclidean_spearman
      value: 87.6598652482716
    - type: manhattan_pearson
      value: 88.23894728971618
    - type: manhattan_spearman
      value: 87.74470156709361
  - 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: 64.54023758394433
    - type: cos_sim_spearman
      value: 66.28491960187773
    - type: euclidean_pearson
      value: 67.0853128483472
    - type: euclidean_spearman
      value: 66.10307543766307
    - type: manhattan_pearson
      value: 66.7635365592556
    - type: manhattan_spearman
      value: 65.76408004780167
  - task:
      type: STS
    dataset:
      name: MTEB STSBenchmark
      type: mteb/stsbenchmark-sts
      config: default
      split: test
      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
    metrics:
    - type: cos_sim_pearson
      value: 85.15858398195317
    - type: cos_sim_spearman
      value: 87.44850004752102
    - type: euclidean_pearson
      value: 86.60737082550408
    - type: euclidean_spearman
      value: 87.31591549824242
    - type: manhattan_pearson
      value: 86.56187011429977
    - type: manhattan_spearman
      value: 87.23854795795319
  - task:
      type: Reranking
    dataset:
      name: MTEB SciDocsRR
      type: mteb/scidocs-reranking
      config: default
      split: test
      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
    metrics:
    - type: map
      value: 86.66210488769109
    - type: mrr
      value: 96.23100664767331
  - task:
      type: Retrieval
    dataset:
      name: MTEB SciFact
      type: scifact
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 56.094
    - type: map_at_10
      value: 67.486
    - type: map_at_100
      value: 67.925
    - type: map_at_1000
      value: 67.949
    - type: map_at_3
      value: 64.857
    - type: map_at_5
      value: 66.31
    - type: mrr_at_1
      value: 58.667
    - type: mrr_at_10
      value: 68.438
    - type: mrr_at_100
      value: 68.733
    - type: mrr_at_1000
      value: 68.757
    - type: mrr_at_3
      value: 66.389
    - type: mrr_at_5
      value: 67.456
    - type: ndcg_at_1
      value: 58.667
    - type: ndcg_at_10
      value: 72.506
    - type: ndcg_at_100
      value: 74.27
    - type: ndcg_at_1000
      value: 74.94800000000001
    - type: ndcg_at_3
      value: 67.977
    - type: ndcg_at_5
      value: 70.028
    - type: precision_at_1
      value: 58.667
    - type: precision_at_10
      value: 9.767000000000001
    - type: precision_at_100
      value: 1.073
    - type: precision_at_1000
      value: 0.11299999999999999
    - type: precision_at_3
      value: 27.0
    - type: precision_at_5
      value: 17.666999999999998
    - type: recall_at_1
      value: 56.094
    - type: recall_at_10
      value: 86.68900000000001
    - type: recall_at_100
      value: 94.333
    - type: recall_at_1000
      value: 99.667
    - type: recall_at_3
      value: 74.522
    - type: recall_at_5
      value: 79.611
  - task:
      type: PairClassification
    dataset:
      name: MTEB SprintDuplicateQuestions
      type: mteb/sprintduplicatequestions-pairclassification
      config: default
      split: test
      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
    metrics:
    - type: cos_sim_accuracy
      value: 99.83069306930693
    - type: cos_sim_ap
      value: 95.69184662911199
    - type: cos_sim_f1
      value: 91.4027149321267
    - type: cos_sim_precision
      value: 91.91102123356926
    - type: cos_sim_recall
      value: 90.9
    - type: dot_accuracy
      value: 99.69405940594059
    - type: dot_ap
      value: 90.21674151456216
    - type: dot_f1
      value: 84.4489179667841
    - type: dot_precision
      value: 85.00506585612969
    - type: dot_recall
      value: 83.89999999999999
    - type: euclidean_accuracy
      value: 99.83069306930693
    - type: euclidean_ap
      value: 95.67760109671087
    - type: euclidean_f1
      value: 91.19754350051177
    - type: euclidean_precision
      value: 93.39622641509435
    - type: euclidean_recall
      value: 89.1
    - type: manhattan_accuracy
      value: 99.83267326732673
    - type: manhattan_ap
      value: 95.69771347732625
    - type: manhattan_f1
      value: 91.32420091324201
    - type: manhattan_precision
      value: 92.68795056642637
    - type: manhattan_recall
      value: 90.0
    - type: max_accuracy
      value: 99.83267326732673
    - type: max_ap
      value: 95.69771347732625
    - type: max_f1
      value: 91.4027149321267
  - task:
      type: Clustering
    dataset:
      name: MTEB StackExchangeClustering
      type: mteb/stackexchange-clustering
      config: default
      split: test
      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
    metrics:
    - type: v_measure
      value: 64.47378332953092
  - task:
      type: Clustering
    dataset:
      name: MTEB StackExchangeClusteringP2P
      type: mteb/stackexchange-clustering-p2p
      config: default
      split: test
      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
    metrics:
    - type: v_measure
      value: 33.79602531604151
  - task:
      type: Reranking
    dataset:
      name: MTEB StackOverflowDupQuestions
      type: mteb/stackoverflowdupquestions-reranking
      config: default
      split: test
      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
    metrics:
    - type: map
      value: 53.80707639107175
    - type: mrr
      value: 54.64886522790935
  - task:
      type: Summarization
    dataset:
      name: MTEB SummEval
      type: mteb/summeval
      config: default
      split: test
      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
    metrics:
    - type: cos_sim_pearson
      value: 30.852448373051395
    - type: cos_sim_spearman
      value: 32.51821499493775
    - type: dot_pearson
      value: 30.390650062190456
    - type: dot_spearman
      value: 30.588836159667636
  - task:
      type: Retrieval
    dataset:
      name: MTEB TRECCOVID
      type: trec-covid
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 0.198
    - type: map_at_10
      value: 1.51
    - type: map_at_100
      value: 8.882
    - type: map_at_1000
      value: 22.181
    - type: map_at_3
      value: 0.553
    - type: map_at_5
      value: 0.843
    - type: mrr_at_1
      value: 74.0
    - type: mrr_at_10
      value: 84.89999999999999
    - type: mrr_at_100
      value: 84.89999999999999
    - type: mrr_at_1000
      value: 84.89999999999999
    - type: mrr_at_3
      value: 84.0
    - type: mrr_at_5
      value: 84.89999999999999
    - type: ndcg_at_1
      value: 68.0
    - type: ndcg_at_10
      value: 64.792
    - type: ndcg_at_100
      value: 51.37199999999999
    - type: ndcg_at_1000
      value: 47.392
    - type: ndcg_at_3
      value: 68.46900000000001
    - type: ndcg_at_5
      value: 67.084
    - type: precision_at_1
      value: 74.0
    - type: precision_at_10
      value: 69.39999999999999
    - type: precision_at_100
      value: 53.080000000000005
    - type: precision_at_1000
      value: 21.258
    - type: precision_at_3
      value: 76.0
    - type: precision_at_5
      value: 73.2
    - type: recall_at_1
      value: 0.198
    - type: recall_at_10
      value: 1.7950000000000002
    - type: recall_at_100
      value: 12.626999999999999
    - type: recall_at_1000
      value: 44.84
    - type: recall_at_3
      value: 0.611
    - type: recall_at_5
      value: 0.959
  - task:
      type: Retrieval
    dataset:
      name: MTEB Touche2020
      type: webis-touche2020
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 1.4949999999999999
    - type: map_at_10
      value: 8.797
    - type: map_at_100
      value: 14.889
    - type: map_at_1000
      value: 16.309
    - type: map_at_3
      value: 4.389
    - type: map_at_5
      value: 6.776
    - type: mrr_at_1
      value: 18.367
    - type: mrr_at_10
      value: 35.844
    - type: mrr_at_100
      value: 37.119
    - type: mrr_at_1000
      value: 37.119
    - type: mrr_at_3
      value: 30.612000000000002
    - type: mrr_at_5
      value: 33.163
    - type: ndcg_at_1
      value: 16.326999999999998
    - type: ndcg_at_10
      value: 21.9
    - type: ndcg_at_100
      value: 34.705000000000005
    - type: ndcg_at_1000
      value: 45.709
    - type: ndcg_at_3
      value: 22.7
    - type: ndcg_at_5
      value: 23.197000000000003
    - type: precision_at_1
      value: 18.367
    - type: precision_at_10
      value: 21.02
    - type: precision_at_100
      value: 7.714
    - type: precision_at_1000
      value: 1.504
    - type: precision_at_3
      value: 26.531
    - type: precision_at_5
      value: 26.122
    - type: recall_at_1
      value: 1.4949999999999999
    - type: recall_at_10
      value: 15.504000000000001
    - type: recall_at_100
      value: 47.978
    - type: recall_at_1000
      value: 81.56
    - type: recall_at_3
      value: 5.569
    - type: recall_at_5
      value: 9.821
  - task:
      type: Classification
    dataset:
      name: MTEB ToxicConversationsClassification
      type: mteb/toxic_conversations_50k
      config: default
      split: test
      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
    metrics:
    - type: accuracy
      value: 72.99279999999999
    - type: ap
      value: 15.459189680101492
    - type: f1
      value: 56.33023271441895
  - task:
      type: Classification
    dataset:
      name: MTEB TweetSentimentExtractionClassification
      type: mteb/tweet_sentiment_extraction
      config: default
      split: test
      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
    metrics:
    - type: accuracy
      value: 63.070175438596486
    - type: f1
      value: 63.28070758709465
  - task:
      type: Clustering
    dataset:
      name: MTEB TwentyNewsgroupsClustering
      type: mteb/twentynewsgroups-clustering
      config: default
      split: test
      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
    metrics:
    - type: v_measure
      value: 50.076231309703054
  - task:
      type: PairClassification
    dataset:
      name: MTEB TwitterSemEval2015
      type: mteb/twittersemeval2015-pairclassification
      config: default
      split: test
      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
    metrics:
    - type: cos_sim_accuracy
      value: 87.21463908922931
    - type: cos_sim_ap
      value: 77.67287017966282
    - type: cos_sim_f1
      value: 70.34412955465588
    - type: cos_sim_precision
      value: 67.57413709285368
    - type: cos_sim_recall
      value: 73.35092348284961
    - type: dot_accuracy
      value: 85.04500208618943
    - type: dot_ap
      value: 70.4075203869744
    - type: dot_f1
      value: 66.18172537008678
    - type: dot_precision
      value: 64.08798813643104
    - type: dot_recall
      value: 68.41688654353561
    - type: euclidean_accuracy
      value: 87.17887584192646
    - type: euclidean_ap
      value: 77.5774128274464
    - type: euclidean_f1
      value: 70.09307972480777
    - type: euclidean_precision
      value: 71.70852884349986
    - type: euclidean_recall
      value: 68.54881266490766
    - type: manhattan_accuracy
      value: 87.28020504261787
    - type: manhattan_ap
      value: 77.57835820297892
    - type: manhattan_f1
      value: 70.23063591521131
    - type: manhattan_precision
      value: 70.97817299919159
    - type: manhattan_recall
      value: 69.49868073878628
    - type: max_accuracy
      value: 87.28020504261787
    - type: max_ap
      value: 77.67287017966282
    - type: max_f1
      value: 70.34412955465588
  - task:
      type: PairClassification
    dataset:
      name: MTEB TwitterURLCorpus
      type: mteb/twitterurlcorpus-pairclassification
      config: default
      split: test
      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
    metrics:
    - type: cos_sim_accuracy
      value: 88.96650754841464
    - type: cos_sim_ap
      value: 86.00185968965064
    - type: cos_sim_f1
      value: 77.95861256351718
    - type: cos_sim_precision
      value: 74.70712773465067
    - type: cos_sim_recall
      value: 81.50600554357868
    - type: dot_accuracy
      value: 87.36950362867233
    - type: dot_ap
      value: 82.22071181147555
    - type: dot_f1
      value: 74.85680716698488
    - type: dot_precision
      value: 71.54688377316114
    - type: dot_recall
      value: 78.48783492454572
    - type: euclidean_accuracy
      value: 88.99561454573679
    - type: euclidean_ap
      value: 86.15882097229648
    - type: euclidean_f1
      value: 78.18463125322332
    - type: euclidean_precision
      value: 74.95408956067241
    - type: euclidean_recall
      value: 81.70619032953496
    - type: manhattan_accuracy
      value: 88.96650754841464
    - type: manhattan_ap
      value: 86.13133111232099
    - type: manhattan_f1
      value: 78.10771470160115
    - type: manhattan_precision
      value: 74.05465084184377
    - type: manhattan_recall
      value: 82.63012011087157
    - type: max_accuracy
      value: 88.99561454573679
    - type: max_ap
      value: 86.15882097229648
    - type: max_f1
      value: 78.18463125322332
---

# djuna/stella-base-en-v2-Q5_K_M-GGUF
This model was converted to GGUF format from [`infgrad/stella-base-en-v2`](https://huggingface.co/infgrad/stella-base-en-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/infgrad/stella-base-en-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 djuna/stella-base-en-v2-Q5_K_M-GGUF --hf-file stella-base-en-v2-q5_k_m.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo djuna/stella-base-en-v2-Q5_K_M-GGUF --hf-file stella-base-en-v2-q5_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 djuna/stella-base-en-v2-Q5_K_M-GGUF --hf-file stella-base-en-v2-q5_k_m.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo djuna/stella-base-en-v2-Q5_K_M-GGUF --hf-file stella-base-en-v2-q5_k_m.gguf -c 2048
```
