sentence-transformers gguf feature-extraction sentence-similarity mteb transformers transformers.js llama-cpp gguf-my-repo en base_model:nomic-ai/nomic-embed-text-v1.5 base_model:quantized:nomic-ai/nomic-embed-text-v1.5 license:apache-2.0 model-index autotrain_compatible endpoints_compatible region:us
---
base_model: nomic-ai/nomic-embed-text-v1.5
language:
- en
library_name: sentence-transformers
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- feature-extraction
- sentence-similarity
- mteb
- transformers
- transformers.js
- llama-cpp
- gguf-my-repo
model-index:
- name: epoch_0_model
  results:
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonCounterfactualClassification (en)
      type: mteb/amazon_counterfactual
      config: en
      split: test
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
    metrics:
    - type: accuracy
      value: 75.20895522388058
    - type: ap
      value: 38.57605549557802
    - type: f1
      value: 69.35586565857854
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonPolarityClassification
      type: mteb/amazon_polarity
      config: default
      split: test
      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
    metrics:
    - type: accuracy
      value: 91.8144
    - type: ap
      value: 88.65222882032363
    - type: f1
      value: 91.80426301643274
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonReviewsClassification (en)
      type: mteb/amazon_reviews_multi
      config: en
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 47.162000000000006
    - type: f1
      value: 46.59329642263158
  - task:
      type: Retrieval
    dataset:
      name: MTEB ArguAna
      type: arguana
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 24.253
    - type: map_at_10
      value: 38.962
    - type: map_at_100
      value: 40.081
    - type: map_at_1000
      value: 40.089000000000006
    - type: map_at_3
      value: 33.499
    - type: map_at_5
      value: 36.351
    - type: mrr_at_1
      value: 24.609
    - type: mrr_at_10
      value: 39.099000000000004
    - type: mrr_at_100
      value: 40.211000000000006
    - type: mrr_at_1000
      value: 40.219
    - type: mrr_at_3
      value: 33.677
    - type: mrr_at_5
      value: 36.469
    - type: ndcg_at_1
      value: 24.253
    - type: ndcg_at_10
      value: 48.010999999999996
    - type: ndcg_at_100
      value: 52.756
    - type: ndcg_at_1000
      value: 52.964999999999996
    - type: ndcg_at_3
      value: 36.564
    - type: ndcg_at_5
      value: 41.711999999999996
    - type: precision_at_1
      value: 24.253
    - type: precision_at_10
      value: 7.738
    - type: precision_at_100
      value: 0.98
    - type: precision_at_1000
      value: 0.1
    - type: precision_at_3
      value: 15.149000000000001
    - type: precision_at_5
      value: 11.593
    - type: recall_at_1
      value: 24.253
    - type: recall_at_10
      value: 77.383
    - type: recall_at_100
      value: 98.009
    - type: recall_at_1000
      value: 99.644
    - type: recall_at_3
      value: 45.448
    - type: recall_at_5
      value: 57.965999999999994
  - task:
      type: Clustering
    dataset:
      name: MTEB ArxivClusteringP2P
      type: mteb/arxiv-clustering-p2p
      config: default
      split: test
      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
    metrics:
    - type: v_measure
      value: 45.69069567851087
  - task:
      type: Clustering
    dataset:
      name: MTEB ArxivClusteringS2S
      type: mteb/arxiv-clustering-s2s
      config: default
      split: test
      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
    metrics:
    - type: v_measure
      value: 36.35185490976283
  - task:
      type: Reranking
    dataset:
      name: MTEB AskUbuntuDupQuestions
      type: mteb/askubuntudupquestions-reranking
      config: default
      split: test
      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
    metrics:
    - type: map
      value: 61.71274951450321
    - type: mrr
      value: 76.06032625423207
  - task:
      type: STS
    dataset:
      name: MTEB BIOSSES
      type: mteb/biosses-sts
      config: default
      split: test
      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
    metrics:
    - type: cos_sim_pearson
      value: 86.73980520022269
    - type: cos_sim_spearman
      value: 84.24649792685918
    - type: euclidean_pearson
      value: 85.85197641158186
    - type: euclidean_spearman
      value: 84.24649792685918
    - type: manhattan_pearson
      value: 86.26809552711346
    - type: manhattan_spearman
      value: 84.56397504030865
  - task:
      type: Classification
    dataset:
      name: MTEB Banking77Classification
      type: mteb/banking77
      config: default
      split: test
      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
    metrics:
    - type: accuracy
      value: 84.25324675324674
    - type: f1
      value: 84.17872280892557
  - task:
      type: Clustering
    dataset:
      name: MTEB BiorxivClusteringP2P
      type: mteb/biorxiv-clustering-p2p
      config: default
      split: test
      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
    metrics:
    - type: v_measure
      value: 38.770253446400886
  - task:
      type: Clustering
    dataset:
      name: MTEB BiorxivClusteringS2S
      type: mteb/biorxiv-clustering-s2s
      config: default
      split: test
      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
    metrics:
    - type: v_measure
      value: 32.94307095497281
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackAndroidRetrieval
      type: BeIR/cqadupstack
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 32.164
    - type: map_at_10
      value: 42.641
    - type: map_at_100
      value: 43.947
    - type: map_at_1000
      value: 44.074999999999996
    - type: map_at_3
      value: 39.592
    - type: map_at_5
      value: 41.204
    - type: mrr_at_1
      value: 39.628
    - type: mrr_at_10
      value: 48.625
    - type: mrr_at_100
      value: 49.368
    - type: mrr_at_1000
      value: 49.413000000000004
    - type: mrr_at_3
      value: 46.400000000000006
    - type: mrr_at_5
      value: 47.68
    - type: ndcg_at_1
      value: 39.628
    - type: ndcg_at_10
      value: 48.564
    - type: ndcg_at_100
      value: 53.507000000000005
    - type: ndcg_at_1000
      value: 55.635999999999996
    - type: ndcg_at_3
      value: 44.471
    - type: ndcg_at_5
      value: 46.137
    - type: precision_at_1
      value: 39.628
    - type: precision_at_10
      value: 8.856
    - type: precision_at_100
      value: 1.429
    - type: precision_at_1000
      value: 0.191
    - type: precision_at_3
      value: 21.268
    - type: precision_at_5
      value: 14.649000000000001
    - type: recall_at_1
      value: 32.164
    - type: recall_at_10
      value: 59.609
    - type: recall_at_100
      value: 80.521
    - type: recall_at_1000
      value: 94.245
    - type: recall_at_3
      value: 46.521
    - type: recall_at_5
      value: 52.083999999999996
    - type: map_at_1
      value: 31.526
    - type: map_at_10
      value: 41.581
    - type: map_at_100
      value: 42.815999999999995
    - type: map_at_1000
      value: 42.936
    - type: map_at_3
      value: 38.605000000000004
    - type: map_at_5
      value: 40.351
    - type: mrr_at_1
      value: 39.489999999999995
    - type: mrr_at_10
      value: 47.829
    - type: mrr_at_100
      value: 48.512
    - type: mrr_at_1000
      value: 48.552
    - type: mrr_at_3
      value: 45.754
    - type: mrr_at_5
      value: 46.986
    - type: ndcg_at_1
      value: 39.489999999999995
    - type: ndcg_at_10
      value: 47.269
    - type: ndcg_at_100
      value: 51.564
    - type: ndcg_at_1000
      value: 53.53099999999999
    - type: ndcg_at_3
      value: 43.301
    - type: ndcg_at_5
      value: 45.239000000000004
    - type: precision_at_1
      value: 39.489999999999995
    - type: precision_at_10
      value: 8.93
    - type: precision_at_100
      value: 1.415
    - type: precision_at_1000
      value: 0.188
    - type: precision_at_3
      value: 20.892
    - type: precision_at_5
      value: 14.865999999999998
    - type: recall_at_1
      value: 31.526
    - type: recall_at_10
      value: 56.76
    - type: recall_at_100
      value: 75.029
    - type: recall_at_1000
      value: 87.491
    - type: recall_at_3
      value: 44.786
    - type: recall_at_5
      value: 50.254
    - type: map_at_1
      value: 40.987
    - type: map_at_10
      value: 52.827
    - type: map_at_100
      value: 53.751000000000005
    - type: map_at_1000
      value: 53.81
    - type: map_at_3
      value: 49.844
    - type: map_at_5
      value: 51.473
    - type: mrr_at_1
      value: 46.833999999999996
    - type: mrr_at_10
      value: 56.389
    - type: mrr_at_100
      value: 57.003
    - type: mrr_at_1000
      value: 57.034
    - type: mrr_at_3
      value: 54.17999999999999
    - type: mrr_at_5
      value: 55.486999999999995
    - type: ndcg_at_1
      value: 46.833999999999996
    - type: ndcg_at_10
      value: 58.372
    - type: ndcg_at_100
      value: 62.068
    - type: ndcg_at_1000
      value: 63.288
    - type: ndcg_at_3
      value: 53.400000000000006
    - type: ndcg_at_5
      value: 55.766000000000005
    - type: precision_at_1
      value: 46.833999999999996
    - type: precision_at_10
      value: 9.191
    - type: precision_at_100
      value: 1.192
    - type: precision_at_1000
      value: 0.134
    - type: precision_at_3
      value: 23.448
    - type: precision_at_5
      value: 15.862000000000002
    - type: recall_at_1
      value: 40.987
    - type: recall_at_10
      value: 71.146
    - type: recall_at_100
      value: 87.035
    - type: recall_at_1000
      value: 95.633
    - type: recall_at_3
      value: 58.025999999999996
    - type: recall_at_5
      value: 63.815999999999995
    - type: map_at_1
      value: 24.587
    - type: map_at_10
      value: 33.114
    - type: map_at_100
      value: 34.043
    - type: map_at_1000
      value: 34.123999999999995
    - type: map_at_3
      value: 30.45
    - type: map_at_5
      value: 31.813999999999997
    - type: mrr_at_1
      value: 26.554
    - type: mrr_at_10
      value: 35.148
    - type: mrr_at_100
      value: 35.926
    - type: mrr_at_1000
      value: 35.991
    - type: mrr_at_3
      value: 32.599000000000004
    - type: mrr_at_5
      value: 33.893
    - type: ndcg_at_1
      value: 26.554
    - type: ndcg_at_10
      value: 38.132
    - type: ndcg_at_100
      value: 42.78
    - type: ndcg_at_1000
      value: 44.919
    - type: ndcg_at_3
      value: 32.833
    - type: ndcg_at_5
      value: 35.168
    - type: precision_at_1
      value: 26.554
    - type: precision_at_10
      value: 5.921
    - type: precision_at_100
      value: 0.8659999999999999
    - type: precision_at_1000
      value: 0.109
    - type: precision_at_3
      value: 13.861
    - type: precision_at_5
      value: 9.605
    - type: recall_at_1
      value: 24.587
    - type: recall_at_10
      value: 51.690000000000005
    - type: recall_at_100
      value: 73.428
    - type: recall_at_1000
      value: 89.551
    - type: recall_at_3
      value: 37.336999999999996
    - type: recall_at_5
      value: 43.047000000000004
    - type: map_at_1
      value: 16.715
    - type: map_at_10
      value: 24.251
    - type: map_at_100
      value: 25.326999999999998
    - type: map_at_1000
      value: 25.455
    - type: map_at_3
      value: 21.912000000000003
    - type: map_at_5
      value: 23.257
    - type: mrr_at_1
      value: 20.274
    - type: mrr_at_10
      value: 28.552
    - type: mrr_at_100
      value: 29.42
    - type: mrr_at_1000
      value: 29.497
    - type: mrr_at_3
      value: 26.14
    - type: mrr_at_5
      value: 27.502
    - type: ndcg_at_1
      value: 20.274
    - type: ndcg_at_10
      value: 29.088
    - type: ndcg_at_100
      value: 34.293
    - type: ndcg_at_1000
      value: 37.271
    - type: ndcg_at_3
      value: 24.708
    - type: ndcg_at_5
      value: 26.809
    - type: precision_at_1
      value: 20.274
    - type: precision_at_10
      value: 5.361
    - type: precision_at_100
      value: 0.915
    - type: precision_at_1000
      value: 0.13
    - type: precision_at_3
      value: 11.733
    - type: precision_at_5
      value: 8.556999999999999
    - type: recall_at_1
      value: 16.715
    - type: recall_at_10
      value: 39.587
    - type: recall_at_100
      value: 62.336000000000006
    - type: recall_at_1000
      value: 83.453
    - type: recall_at_3
      value: 27.839999999999996
    - type: recall_at_5
      value: 32.952999999999996
    - type: map_at_1
      value: 28.793000000000003
    - type: map_at_10
      value: 38.582
    - type: map_at_100
      value: 39.881
    - type: map_at_1000
      value: 39.987
    - type: map_at_3
      value: 35.851
    - type: map_at_5
      value: 37.289
    - type: mrr_at_1
      value: 34.455999999999996
    - type: mrr_at_10
      value: 43.909
    - type: mrr_at_100
      value: 44.74
    - type: mrr_at_1000
      value: 44.786
    - type: mrr_at_3
      value: 41.659
    - type: mrr_at_5
      value: 43.010999999999996
    - type: ndcg_at_1
      value: 34.455999999999996
    - type: ndcg_at_10
      value: 44.266
    - type: ndcg_at_100
      value: 49.639
    - type: ndcg_at_1000
      value: 51.644
    - type: ndcg_at_3
      value: 39.865
    - type: ndcg_at_5
      value: 41.887
    - type: precision_at_1
      value: 34.455999999999996
    - type: precision_at_10
      value: 7.843999999999999
    - type: precision_at_100
      value: 1.243
    - type: precision_at_1000
      value: 0.158
    - type: precision_at_3
      value: 18.831999999999997
    - type: precision_at_5
      value: 13.147
    - type: recall_at_1
      value: 28.793000000000003
    - type: recall_at_10
      value: 55.68300000000001
    - type: recall_at_100
      value: 77.99000000000001
    - type: recall_at_1000
      value: 91.183
    - type: recall_at_3
      value: 43.293
    - type: recall_at_5
      value: 48.618
    - type: map_at_1
      value: 25.907000000000004
    - type: map_at_10
      value: 35.519
    - type: map_at_100
      value: 36.806
    - type: map_at_1000
      value: 36.912
    - type: map_at_3
      value: 32.748
    - type: map_at_5
      value: 34.232
    - type: mrr_at_1
      value: 31.621
    - type: mrr_at_10
      value: 40.687
    - type: mrr_at_100
      value: 41.583
    - type: mrr_at_1000
      value: 41.638999999999996
    - type: mrr_at_3
      value: 38.527
    - type: mrr_at_5
      value: 39.612
    - type: ndcg_at_1
      value: 31.621
    - type: ndcg_at_10
      value: 41.003
    - type: ndcg_at_100
      value: 46.617999999999995
    - type: ndcg_at_1000
      value: 48.82
    - type: ndcg_at_3
      value: 36.542
    - type: ndcg_at_5
      value: 38.368
    - type: precision_at_1
      value: 31.621
    - type: precision_at_10
      value: 7.396999999999999
    - type: precision_at_100
      value: 1.191
    - type: precision_at_1000
      value: 0.153
    - type: precision_at_3
      value: 17.39
    - type: precision_at_5
      value: 12.1
    - type: recall_at_1
      value: 25.907000000000004
    - type: recall_at_10
      value: 52.115
    - type: recall_at_100
      value: 76.238
    - type: recall_at_1000
      value: 91.218
    - type: recall_at_3
      value: 39.417
    - type: recall_at_5
      value: 44.435
    - type: map_at_1
      value: 25.732166666666668
    - type: map_at_10
      value: 34.51616666666667
    - type: map_at_100
      value: 35.67241666666666
    - type: map_at_1000
      value: 35.78675
    - type: map_at_3
      value: 31.953416666666662
    - type: map_at_5
      value: 33.333
    - type: mrr_at_1
      value: 30.300166666666673
    - type: mrr_at_10
      value: 38.6255
    - type: mrr_at_100
      value: 39.46183333333334
    - type: mrr_at_1000
      value: 39.519999999999996
    - type: mrr_at_3
      value: 36.41299999999999
    - type: mrr_at_5
      value: 37.6365
    - type: ndcg_at_1
      value: 30.300166666666673
    - type: ndcg_at_10
      value: 39.61466666666667
    - type: ndcg_at_100
      value: 44.60808333333334
    - type: ndcg_at_1000
      value: 46.91708333333334
    - type: ndcg_at_3
      value: 35.26558333333333
    - type: ndcg_at_5
      value: 37.220000000000006
    - type: precision_at_1
      value: 30.300166666666673
    - type: precision_at_10
      value: 6.837416666666667
    - type: precision_at_100
      value: 1.10425
    - type: precision_at_1000
      value: 0.14875
    - type: precision_at_3
      value: 16.13716666666667
    - type: precision_at_5
      value: 11.2815
    - type: recall_at_1
      value: 25.732166666666668
    - type: recall_at_10
      value: 50.578916666666665
    - type: recall_at_100
      value: 72.42183333333334
    - type: recall_at_1000
      value: 88.48766666666667
    - type: recall_at_3
      value: 38.41325
    - type: recall_at_5
      value: 43.515750000000004
    - type: map_at_1
      value: 23.951
    - type: map_at_10
      value: 30.974
    - type: map_at_100
      value: 31.804
    - type: map_at_1000
      value: 31.900000000000002
    - type: map_at_3
      value: 28.762
    - type: map_at_5
      value: 29.94
    - type: mrr_at_1
      value: 26.534000000000002
    - type: mrr_at_10
      value: 33.553
    - type: mrr_at_100
      value: 34.297
    - type: mrr_at_1000
      value: 34.36
    - type: mrr_at_3
      value: 31.391000000000002
    - type: mrr_at_5
      value: 32.525999999999996
    - type: ndcg_at_1
      value: 26.534000000000002
    - type: ndcg_at_10
      value: 35.112
    - type: ndcg_at_100
      value: 39.28
    - type: ndcg_at_1000
      value: 41.723
    - type: ndcg_at_3
      value: 30.902
    - type: ndcg_at_5
      value: 32.759
    - type: precision_at_1
      value: 26.534000000000002
    - type: precision_at_10
      value: 5.445
    - type: precision_at_100
      value: 0.819
    - type: precision_at_1000
      value: 0.11
    - type: precision_at_3
      value: 12.986
    - type: precision_at_5
      value: 9.049
    - type: recall_at_1
      value: 23.951
    - type: recall_at_10
      value: 45.24
    - type: recall_at_100
      value: 64.12299999999999
    - type: recall_at_1000
      value: 82.28999999999999
    - type: recall_at_3
      value: 33.806000000000004
    - type: recall_at_5
      value: 38.277
    - type: map_at_1
      value: 16.829
    - type: map_at_10
      value: 23.684
    - type: map_at_100
      value: 24.683
    - type: map_at_1000
      value: 24.81
    - type: map_at_3
      value: 21.554000000000002
    - type: map_at_5
      value: 22.768
    - type: mrr_at_1
      value: 20.096
    - type: mrr_at_10
      value: 27.230999999999998
    - type: mrr_at_100
      value: 28.083999999999996
    - type: mrr_at_1000
      value: 28.166000000000004
    - type: mrr_at_3
      value: 25.212
    - type: mrr_at_5
      value: 26.32
    - type: ndcg_at_1
      value: 20.096
    - type: ndcg_at_10
      value: 27.989000000000004
    - type: ndcg_at_100
      value: 32.847
    - type: ndcg_at_1000
      value: 35.896
    - type: ndcg_at_3
      value: 24.116
    - type: ndcg_at_5
      value: 25.964
    - type: precision_at_1
      value: 20.096
    - type: precision_at_10
      value: 5
    - type: precision_at_100
      value: 0.8750000000000001
    - type: precision_at_1000
      value: 0.131
    - type: precision_at_3
      value: 11.207
    - type: precision_at_5
      value: 8.08
    - type: recall_at_1
      value: 16.829
    - type: recall_at_10
      value: 37.407000000000004
    - type: recall_at_100
      value: 59.101000000000006
    - type: recall_at_1000
      value: 81.024
    - type: recall_at_3
      value: 26.739
    - type: recall_at_5
      value: 31.524
    - type: map_at_1
      value: 24.138
    - type: map_at_10
      value: 32.275999999999996
    - type: map_at_100
      value: 33.416000000000004
    - type: map_at_1000
      value: 33.527
    - type: map_at_3
      value: 29.854000000000003
    - type: map_at_5
      value: 31.096
    - type: mrr_at_1
      value: 28.450999999999997
    - type: mrr_at_10
      value: 36.214
    - type: mrr_at_100
      value: 37.134
    - type: mrr_at_1000
      value: 37.198
    - type: mrr_at_3
      value: 34.001999999999995
    - type: mrr_at_5
      value: 35.187000000000005
    - type: ndcg_at_1
      value: 28.450999999999997
    - type: ndcg_at_10
      value: 37.166
    - type: ndcg_at_100
      value: 42.454
    - type: ndcg_at_1000
      value: 44.976
    - type: ndcg_at_3
      value: 32.796
    - type: ndcg_at_5
      value: 34.631
    - type: precision_at_1
      value: 28.450999999999997
    - type: precision_at_10
      value: 6.241
    - type: precision_at_100
      value: 0.9950000000000001
    - type: precision_at_1000
      value: 0.133
    - type: precision_at_3
      value: 14.801
    - type: precision_at_5
      value: 10.280000000000001
    - type: recall_at_1
      value: 24.138
    - type: recall_at_10
      value: 48.111
    - type: recall_at_100
      value: 71.245
    - type: recall_at_1000
      value: 88.986
    - type: recall_at_3
      value: 36.119
    - type: recall_at_5
      value: 40.846
    - type: map_at_1
      value: 23.244
    - type: map_at_10
      value: 31.227
    - type: map_at_100
      value: 33.007
    - type: map_at_1000
      value: 33.223
    - type: map_at_3
      value: 28.924
    - type: map_at_5
      value: 30.017
    - type: mrr_at_1
      value: 27.668
    - type: mrr_at_10
      value: 35.524
    - type: mrr_at_100
      value: 36.699
    - type: mrr_at_1000
      value: 36.759
    - type: mrr_at_3
      value: 33.366
    - type: mrr_at_5
      value: 34.552
    - type: ndcg_at_1
      value: 27.668
    - type: ndcg_at_10
      value: 36.381
    - type: ndcg_at_100
      value: 43.062
    - type: ndcg_at_1000
      value: 45.656
    - type: ndcg_at_3
      value: 32.501999999999995
    - type: ndcg_at_5
      value: 34.105999999999995
    - type: precision_at_1
      value: 27.668
    - type: precision_at_10
      value: 6.798
    - type: precision_at_100
      value: 1.492
    - type: precision_at_1000
      value: 0.234
    - type: precision_at_3
      value: 15.152
    - type: precision_at_5
      value: 10.791
    - type: recall_at_1
      value: 23.244
    - type: recall_at_10
      value: 45.979
    - type: recall_at_100
      value: 74.822
    - type: recall_at_1000
      value: 91.078
    - type: recall_at_3
      value: 34.925
    - type: recall_at_5
      value: 39.126
    - type: map_at_1
      value: 19.945
    - type: map_at_10
      value: 27.517999999999997
    - type: map_at_100
      value: 28.588
    - type: map_at_1000
      value: 28.682000000000002
    - type: map_at_3
      value: 25.345000000000002
    - type: map_at_5
      value: 26.555
    - type: mrr_at_1
      value: 21.996
    - type: mrr_at_10
      value: 29.845
    - type: mrr_at_100
      value: 30.775999999999996
    - type: mrr_at_1000
      value: 30.845
    - type: mrr_at_3
      value: 27.726
    - type: mrr_at_5
      value: 28.882
    - type: ndcg_at_1
      value: 21.996
    - type: ndcg_at_10
      value: 32.034
    - type: ndcg_at_100
      value: 37.185
    - type: ndcg_at_1000
      value: 39.645
    - type: ndcg_at_3
      value: 27.750999999999998
    - type: ndcg_at_5
      value: 29.805999999999997
    - type: precision_at_1
      value: 21.996
    - type: precision_at_10
      value: 5.065
    - type: precision_at_100
      value: 0.819
    - type: precision_at_1000
      value: 0.11399999999999999
    - type: precision_at_3
      value: 12.076
    - type: precision_at_5
      value: 8.392
    - type: recall_at_1
      value: 19.945
    - type: recall_at_10
      value: 43.62
    - type: recall_at_100
      value: 67.194
    - type: recall_at_1000
      value: 85.7
    - type: recall_at_3
      value: 32.15
    - type: recall_at_5
      value: 37.208999999999996
  - task:
      type: Retrieval
    dataset:
      name: MTEB ClimateFEVER
      type: climate-fever
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 18.279
    - type: map_at_10
      value: 31.052999999999997
    - type: map_at_100
      value: 33.125
    - type: map_at_1000
      value: 33.306000000000004
    - type: map_at_3
      value: 26.208
    - type: map_at_5
      value: 28.857
    - type: mrr_at_1
      value: 42.671
    - type: mrr_at_10
      value: 54.557
    - type: mrr_at_100
      value: 55.142
    - type: mrr_at_1000
      value: 55.169000000000004
    - type: mrr_at_3
      value: 51.488
    - type: mrr_at_5
      value: 53.439
    - type: ndcg_at_1
      value: 42.671
    - type: ndcg_at_10
      value: 41.276
    - type: ndcg_at_100
      value: 48.376000000000005
    - type: ndcg_at_1000
      value: 51.318
    - type: ndcg_at_3
      value: 35.068
    - type: ndcg_at_5
      value: 37.242
    - type: precision_at_1
      value: 42.671
    - type: precision_at_10
      value: 12.638
    - type: precision_at_100
      value: 2.045
    - type: precision_at_1000
      value: 0.26
    - type: precision_at_3
      value: 26.08
    - type: precision_at_5
      value: 19.805
    - type: recall_at_1
      value: 18.279
    - type: recall_at_10
      value: 46.946
    - type: recall_at_100
      value: 70.97200000000001
    - type: recall_at_1000
      value: 87.107
    - type: recall_at_3
      value: 31.147999999999996
    - type: recall_at_5
      value: 38.099
  - task:
      type: Retrieval
    dataset:
      name: MTEB DBPedia
      type: dbpedia-entity
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 8.573
    - type: map_at_10
      value: 19.747
    - type: map_at_100
      value: 28.205000000000002
    - type: map_at_1000
      value: 29.831000000000003
    - type: map_at_3
      value: 14.109
    - type: map_at_5
      value: 16.448999999999998
    - type: mrr_at_1
      value: 71
    - type: mrr_at_10
      value: 77.68599999999999
    - type: mrr_at_100
      value: 77.995
    - type: mrr_at_1000
      value: 78.00200000000001
    - type: mrr_at_3
      value: 76.292
    - type: mrr_at_5
      value: 77.029
    - type: ndcg_at_1
      value: 59.12500000000001
    - type: ndcg_at_10
      value: 43.9
    - type: ndcg_at_100
      value: 47.863
    - type: ndcg_at_1000
      value: 54.848
    - type: ndcg_at_3
      value: 49.803999999999995
    - type: ndcg_at_5
      value: 46.317
    - type: precision_at_1
      value: 71
    - type: precision_at_10
      value: 34.4
    - type: precision_at_100
      value: 11.063
    - type: precision_at_1000
      value: 1.989
    - type: precision_at_3
      value: 52.333
    - type: precision_at_5
      value: 43.7
    - type: recall_at_1
      value: 8.573
    - type: recall_at_10
      value: 25.615
    - type: recall_at_100
      value: 53.385000000000005
    - type: recall_at_1000
      value: 75.46000000000001
    - type: recall_at_3
      value: 15.429
    - type: recall_at_5
      value: 19.357
  - task:
      type: Classification
    dataset:
      name: MTEB EmotionClassification
      type: mteb/emotion
      config: default
      split: test
      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
    metrics:
    - type: accuracy
      value: 47.989999999999995
    - type: f1
      value: 42.776314451497555
  - task:
      type: Retrieval
    dataset:
      name: MTEB FEVER
      type: fever
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 74.13499999999999
    - type: map_at_10
      value: 82.825
    - type: map_at_100
      value: 83.096
    - type: map_at_1000
      value: 83.111
    - type: map_at_3
      value: 81.748
    - type: map_at_5
      value: 82.446
    - type: mrr_at_1
      value: 79.553
    - type: mrr_at_10
      value: 86.654
    - type: mrr_at_100
      value: 86.774
    - type: mrr_at_1000
      value: 86.778
    - type: mrr_at_3
      value: 85.981
    - type: mrr_at_5
      value: 86.462
    - type: ndcg_at_1
      value: 79.553
    - type: ndcg_at_10
      value: 86.345
    - type: ndcg_at_100
      value: 87.32
    - type: ndcg_at_1000
      value: 87.58200000000001
    - type: ndcg_at_3
      value: 84.719
    - type: ndcg_at_5
      value: 85.677
    - type: precision_at_1
      value: 79.553
    - type: precision_at_10
      value: 10.402000000000001
    - type: precision_at_100
      value: 1.1119999999999999
    - type: precision_at_1000
      value: 0.11499999999999999
    - type: precision_at_3
      value: 32.413
    - type: precision_at_5
      value: 20.138
    - type: recall_at_1
      value: 74.13499999999999
    - type: recall_at_10
      value: 93.215
    - type: recall_at_100
      value: 97.083
    - type: recall_at_1000
      value: 98.732
    - type: recall_at_3
      value: 88.79
    - type: recall_at_5
      value: 91.259
  - task:
      type: Retrieval
    dataset:
      name: MTEB FiQA2018
      type: fiqa
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 18.298000000000002
    - type: map_at_10
      value: 29.901
    - type: map_at_100
      value: 31.528
    - type: map_at_1000
      value: 31.713
    - type: map_at_3
      value: 25.740000000000002
    - type: map_at_5
      value: 28.227999999999998
    - type: mrr_at_1
      value: 36.728
    - type: mrr_at_10
      value: 45.401
    - type: mrr_at_100
      value: 46.27
    - type: mrr_at_1000
      value: 46.315
    - type: mrr_at_3
      value: 42.978
    - type: mrr_at_5
      value: 44.29
    - type: ndcg_at_1
      value: 36.728
    - type: ndcg_at_10
      value: 37.456
    - type: ndcg_at_100
      value: 43.832
    - type: ndcg_at_1000
      value: 47
    - type: ndcg_at_3
      value: 33.694
    - type: ndcg_at_5
      value: 35.085
    - type: precision_at_1
      value: 36.728
    - type: precision_at_10
      value: 10.386
    - type: precision_at_100
      value: 1.701
    - type: precision_at_1000
      value: 0.22599999999999998
    - type: precision_at_3
      value: 22.479
    - type: precision_at_5
      value: 16.605
    - type: recall_at_1
      value: 18.298000000000002
    - type: recall_at_10
      value: 44.369
    - type: recall_at_100
      value: 68.098
    - type: recall_at_1000
      value: 87.21900000000001
    - type: recall_at_3
      value: 30.215999999999998
    - type: recall_at_5
      value: 36.861
  - task:
      type: Retrieval
    dataset:
      name: MTEB HotpotQA
      type: hotpotqa
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 39.568
    - type: map_at_10
      value: 65.061
    - type: map_at_100
      value: 65.896
    - type: map_at_1000
      value: 65.95100000000001
    - type: map_at_3
      value: 61.831
    - type: map_at_5
      value: 63.849000000000004
    - type: mrr_at_1
      value: 79.136
    - type: mrr_at_10
      value: 84.58200000000001
    - type: mrr_at_100
      value: 84.765
    - type: mrr_at_1000
      value: 84.772
    - type: mrr_at_3
      value: 83.684
    - type: mrr_at_5
      value: 84.223
    - type: ndcg_at_1
      value: 79.136
    - type: ndcg_at_10
      value: 72.622
    - type: ndcg_at_100
      value: 75.539
    - type: ndcg_at_1000
      value: 76.613
    - type: ndcg_at_3
      value: 68.065
    - type: ndcg_at_5
      value: 70.58
    - type: precision_at_1
      value: 79.136
    - type: precision_at_10
      value: 15.215
    - type: precision_at_100
      value: 1.7500000000000002
    - type: precision_at_1000
      value: 0.189
    - type: precision_at_3
      value: 44.011
    - type: precision_at_5
      value: 28.388999999999996
    - type: recall_at_1
      value: 39.568
    - type: recall_at_10
      value: 76.077
    - type: recall_at_100
      value: 87.481
    - type: recall_at_1000
      value: 94.56400000000001
    - type: recall_at_3
      value: 66.01599999999999
    - type: recall_at_5
      value: 70.97200000000001
  - task:
      type: Classification
    dataset:
      name: MTEB ImdbClassification
      type: mteb/imdb
      config: default
      split: test
      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
    metrics:
    - type: accuracy
      value: 85.312
    - type: ap
      value: 80.36296867333715
    - type: f1
      value: 85.26613311552218
  - task:
      type: Retrieval
    dataset:
      name: MTEB MSMARCO
      type: msmarco
      config: default
      split: dev
      revision: None
    metrics:
    - type: map_at_1
      value: 23.363999999999997
    - type: map_at_10
      value: 35.711999999999996
    - type: map_at_100
      value: 36.876999999999995
    - type: map_at_1000
      value: 36.923
    - type: map_at_3
      value: 32.034
    - type: map_at_5
      value: 34.159
    - type: mrr_at_1
      value: 24.04
    - type: mrr_at_10
      value: 36.345
    - type: mrr_at_100
      value: 37.441
    - type: mrr_at_1000
      value: 37.480000000000004
    - type: mrr_at_3
      value: 32.713
    - type: mrr_at_5
      value: 34.824
    - type: ndcg_at_1
      value: 24.026
    - type: ndcg_at_10
      value: 42.531
    - type: ndcg_at_100
      value: 48.081
    - type: ndcg_at_1000
      value: 49.213
    - type: ndcg_at_3
      value: 35.044
    - type: ndcg_at_5
      value: 38.834
    - type: precision_at_1
      value: 24.026
    - type: precision_at_10
      value: 6.622999999999999
    - type: precision_at_100
      value: 0.941
    - type: precision_at_1000
      value: 0.104
    - type: precision_at_3
      value: 14.909
    - type: precision_at_5
      value: 10.871
    - type: recall_at_1
      value: 23.363999999999997
    - type: recall_at_10
      value: 63.426
    - type: recall_at_100
      value: 88.96300000000001
    - type: recall_at_1000
      value: 97.637
    - type: recall_at_3
      value: 43.095
    - type: recall_at_5
      value: 52.178000000000004
  - task:
      type: Classification
    dataset:
      name: MTEB MTOPDomainClassification (en)
      type: mteb/mtop_domain
      config: en
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
    - type: accuracy
      value: 93.0095759233926
    - type: f1
      value: 92.78387794667408
  - task:
      type: Classification
    dataset:
      name: MTEB MTOPIntentClassification (en)
      type: mteb/mtop_intent
      config: en
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
    - type: accuracy
      value: 75.0296397628819
    - type: f1
      value: 58.45699589820874
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (en)
      type: mteb/amazon_massive_intent
      config: en
      split: test
      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
    metrics:
    - type: accuracy
      value: 73.45662407531944
    - type: f1
      value: 71.42364781421813
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveScenarioClassification (en)
      type: mteb/amazon_massive_scenario
      config: en
      split: test
      revision: 7d571f92784cd94a019292a1f45445077d0ef634
    metrics:
    - type: accuracy
      value: 77.07800941492937
    - type: f1
      value: 77.22799045640845
  - task:
      type: Clustering
    dataset:
      name: MTEB MedrxivClusteringP2P
      type: mteb/medrxiv-clustering-p2p
      config: default
      split: test
      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
    metrics:
    - type: v_measure
      value: 34.531234379250606
  - task:
      type: Clustering
    dataset:
      name: MTEB MedrxivClusteringS2S
      type: mteb/medrxiv-clustering-s2s
      config: default
      split: test
      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
    metrics:
    - type: v_measure
      value: 30.941490381193802
  - task:
      type: Reranking
    dataset:
      name: MTEB MindSmallReranking
      type: mteb/mind_small
      config: default
      split: test
      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
    metrics:
    - type: map
      value: 30.3115090856725
    - type: mrr
      value: 31.290667638675757
  - task:
      type: Retrieval
    dataset:
      name: MTEB NFCorpus
      type: nfcorpus
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 5.465
    - type: map_at_10
      value: 13.03
    - type: map_at_100
      value: 16.057
    - type: map_at_1000
      value: 17.49
    - type: map_at_3
      value: 9.553
    - type: map_at_5
      value: 11.204
    - type: mrr_at_1
      value: 43.653
    - type: mrr_at_10
      value: 53.269
    - type: mrr_at_100
      value: 53.72
    - type: mrr_at_1000
      value: 53.761
    - type: mrr_at_3
      value: 50.929
    - type: mrr_at_5
      value: 52.461
    - type: ndcg_at_1
      value: 42.26
    - type: ndcg_at_10
      value: 34.673
    - type: ndcg_at_100
      value: 30.759999999999998
    - type: ndcg_at_1000
      value: 39.728
    - type: ndcg_at_3
      value: 40.349000000000004
    - type: ndcg_at_5
      value: 37.915
    - type: precision_at_1
      value: 43.653
    - type: precision_at_10
      value: 25.789
    - type: precision_at_100
      value: 7.754999999999999
    - type: precision_at_1000
      value: 2.07
    - type: precision_at_3
      value: 38.596000000000004
    - type: precision_at_5
      value: 33.251
    - type: recall_at_1
      value: 5.465
    - type: recall_at_10
      value: 17.148
    - type: recall_at_100
      value: 29.768
    - type: recall_at_1000
      value: 62.239
    - type: recall_at_3
      value: 10.577
    - type: recall_at_5
      value: 13.315
  - task:
      type: Retrieval
    dataset:
      name: MTEB NQ
      type: nq
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 37.008
    - type: map_at_10
      value: 52.467
    - type: map_at_100
      value: 53.342999999999996
    - type: map_at_1000
      value: 53.366
    - type: map_at_3
      value: 48.412
    - type: map_at_5
      value: 50.875
    - type: mrr_at_1
      value: 41.541
    - type: mrr_at_10
      value: 54.967
    - type: mrr_at_100
      value: 55.611
    - type: mrr_at_1000
      value: 55.627
    - type: mrr_at_3
      value: 51.824999999999996
    - type: mrr_at_5
      value: 53.763000000000005
    - type: ndcg_at_1
      value: 41.541
    - type: ndcg_at_10
      value: 59.724999999999994
    - type: ndcg_at_100
      value: 63.38700000000001
    - type: ndcg_at_1000
      value: 63.883
    - type: ndcg_at_3
      value: 52.331
    - type: ndcg_at_5
      value: 56.327000000000005
    - type: precision_at_1
      value: 41.541
    - type: precision_at_10
      value: 9.447
    - type: precision_at_100
      value: 1.1520000000000001
    - type: precision_at_1000
      value: 0.12
    - type: precision_at_3
      value: 23.262
    - type: precision_at_5
      value: 16.314999999999998
    - type: recall_at_1
      value: 37.008
    - type: recall_at_10
      value: 79.145
    - type: recall_at_100
      value: 94.986
    - type: recall_at_1000
      value: 98.607
    - type: recall_at_3
      value: 60.277
    - type: recall_at_5
      value: 69.407
  - task:
      type: Retrieval
    dataset:
      name: MTEB QuoraRetrieval
      type: quora
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 70.402
    - type: map_at_10
      value: 84.181
    - type: map_at_100
      value: 84.796
    - type: map_at_1000
      value: 84.81400000000001
    - type: map_at_3
      value: 81.209
    - type: map_at_5
      value: 83.085
    - type: mrr_at_1
      value: 81.02000000000001
    - type: mrr_at_10
      value: 87.263
    - type: mrr_at_100
      value: 87.36
    - type: mrr_at_1000
      value: 87.36
    - type: mrr_at_3
      value: 86.235
    - type: mrr_at_5
      value: 86.945
    - type: ndcg_at_1
      value: 81.01
    - type: ndcg_at_10
      value: 87.99900000000001
    - type: ndcg_at_100
      value: 89.217
    - type: ndcg_at_1000
      value: 89.33
    - type: ndcg_at_3
      value: 85.053
    - type: ndcg_at_5
      value: 86.703
    - type: precision_at_1
      value: 81.01
    - type: precision_at_10
      value: 13.336
    - type: precision_at_100
      value: 1.52
    - type: precision_at_1000
      value: 0.156
    - type: precision_at_3
      value: 37.14
    - type: precision_at_5
      value: 24.44
    - type: recall_at_1
      value: 70.402
    - type: recall_at_10
      value: 95.214
    - type: recall_at_100
      value: 99.438
    - type: recall_at_1000
      value: 99.928
    - type: recall_at_3
      value: 86.75699999999999
    - type: recall_at_5
      value: 91.44099999999999
  - task:
      type: Clustering
    dataset:
      name: MTEB RedditClustering
      type: mteb/reddit-clustering
      config: default
      split: test
      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
    metrics:
    - type: v_measure
      value: 56.51721502758904
  - task:
      type: Clustering
    dataset:
      name: MTEB RedditClusteringP2P
      type: mteb/reddit-clustering-p2p
      config: default
      split: test
      revision: 282350215ef01743dc01b456c7f5241fa8937f16
    metrics:
    - type: v_measure
      value: 61.054808572333016
  - task:
      type: Retrieval
    dataset:
      name: MTEB SCIDOCS
      type: scidocs
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 4.578
    - type: map_at_10
      value: 11.036999999999999
    - type: map_at_100
      value: 12.879999999999999
    - type: map_at_1000
      value: 13.150999999999998
    - type: map_at_3
      value: 8.133
    - type: map_at_5
      value: 9.559
    - type: mrr_at_1
      value: 22.6
    - type: mrr_at_10
      value: 32.68
    - type: mrr_at_100
      value: 33.789
    - type: mrr_at_1000
      value: 33.854
    - type: mrr_at_3
      value: 29.7
    - type: mrr_at_5
      value: 31.480000000000004
    - type: ndcg_at_1
      value: 22.6
    - type: ndcg_at_10
      value: 18.616
    - type: ndcg_at_100
      value: 25.883
    - type: ndcg_at_1000
      value: 30.944
    - type: ndcg_at_3
      value: 18.136
    - type: ndcg_at_5
      value: 15.625
    - type: precision_at_1
      value: 22.6
    - type: precision_at_10
      value: 9.48
    - type: precision_at_100
      value: 1.991
    - type: precision_at_1000
      value: 0.321
    - type: precision_at_3
      value: 16.8
    - type: precision_at_5
      value: 13.54
    - type: recall_at_1
      value: 4.578
    - type: recall_at_10
      value: 19.213
    - type: recall_at_100
      value: 40.397
    - type: recall_at_1000
      value: 65.2
    - type: recall_at_3
      value: 10.208
    - type: recall_at_5
      value: 13.718
  - task:
      type: STS
    dataset:
      name: MTEB SICK-R
      type: mteb/sickr-sts
      config: default
      split: test
      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
    metrics:
    - type: cos_sim_pearson
      value: 83.44288351714071
    - type: cos_sim_spearman
      value: 79.37995604564952
    - type: euclidean_pearson
      value: 81.1078874670718
    - type: euclidean_spearman
      value: 79.37995905980499
    - type: manhattan_pearson
      value: 81.03697527288986
    - type: manhattan_spearman
      value: 79.33490235296236
  - task:
      type: STS
    dataset:
      name: MTEB STS12
      type: mteb/sts12-sts
      config: default
      split: test
      revision: a0d554a64d88156834ff5ae9920b964011b16384
    metrics:
    - type: cos_sim_pearson
      value: 84.95557650436523
    - type: cos_sim_spearman
      value: 78.5190672399868
    - type: euclidean_pearson
      value: 81.58064025904707
    - type: euclidean_spearman
      value: 78.5190672399868
    - type: manhattan_pearson
      value: 81.52857930619889
    - type: manhattan_spearman
      value: 78.50421361308034
  - task:
      type: STS
    dataset:
      name: MTEB STS13
      type: mteb/sts13-sts
      config: default
      split: test
      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
    metrics:
    - type: cos_sim_pearson
      value: 84.79128416228737
    - type: cos_sim_spearman
      value: 86.05402451477147
    - type: euclidean_pearson
      value: 85.46280267054289
    - type: euclidean_spearman
      value: 86.05402451477147
    - type: manhattan_pearson
      value: 85.46278563858236
    - type: manhattan_spearman
      value: 86.08079590861004
  - task:
      type: STS
    dataset:
      name: MTEB STS14
      type: mteb/sts14-sts
      config: default
      split: test
      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
    metrics:
    - type: cos_sim_pearson
      value: 83.20623089568763
    - type: cos_sim_spearman
      value: 81.53786907061009
    - type: euclidean_pearson
      value: 82.82272250091494
    - type: euclidean_spearman
      value: 81.53786907061009
    - type: manhattan_pearson
      value: 82.78850494027013
    - type: manhattan_spearman
      value: 81.5135618083407
  - task:
      type: STS
    dataset:
      name: MTEB STS15
      type: mteb/sts15-sts
      config: default
      split: test
      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
    metrics:
    - type: cos_sim_pearson
      value: 85.46366618397936
    - type: cos_sim_spearman
      value: 86.96566013336908
    - type: euclidean_pearson
      value: 86.62651697548931
    - type: euclidean_spearman
      value: 86.96565526364454
    - type: manhattan_pearson
      value: 86.58812160258009
    - type: manhattan_spearman
      value: 86.9336484321288
  - task:
      type: STS
    dataset:
      name: MTEB STS16
      type: mteb/sts16-sts
      config: default
      split: test
      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
    metrics:
    - type: cos_sim_pearson
      value: 82.51858358641559
    - type: cos_sim_spearman
      value: 84.7652527954999
    - type: euclidean_pearson
      value: 84.23914783766861
    - type: euclidean_spearman
      value: 84.7652527954999
    - type: manhattan_pearson
      value: 84.22749648503171
    - type: manhattan_spearman
      value: 84.74527996746386
  - 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: 87.28026563313065
    - type: cos_sim_spearman
      value: 87.46928143824915
    - type: euclidean_pearson
      value: 88.30558762000372
    - type: euclidean_spearman
      value: 87.46928143824915
    - type: manhattan_pearson
      value: 88.10513330809331
    - type: manhattan_spearman
      value: 87.21069787834173
  - 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: 62.376497134587375
    - type: cos_sim_spearman
      value: 65.0159550112516
    - type: euclidean_pearson
      value: 65.64572120879598
    - type: euclidean_spearman
      value: 65.0159550112516
    - type: manhattan_pearson
      value: 65.88143604989976
    - type: manhattan_spearman
      value: 65.17547297222434
  - task:
      type: STS
    dataset:
      name: MTEB STSBenchmark
      type: mteb/stsbenchmark-sts
      config: default
      split: test
      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
    metrics:
    - type: cos_sim_pearson
      value: 84.22876368947644
    - type: cos_sim_spearman
      value: 85.46935577445318
    - type: euclidean_pearson
      value: 85.32830231392005
    - type: euclidean_spearman
      value: 85.46935577445318
    - type: manhattan_pearson
      value: 85.30353211758495
    - type: manhattan_spearman
      value: 85.42821085956945
  - task:
      type: Reranking
    dataset:
      name: MTEB SciDocsRR
      type: mteb/scidocs-reranking
      config: default
      split: test
      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
    metrics:
    - type: map
      value: 80.60986667767133
    - type: mrr
      value: 94.29432314236236
  - task:
      type: Retrieval
    dataset:
      name: MTEB SciFact
      type: scifact
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 54.528
    - type: map_at_10
      value: 65.187
    - type: map_at_100
      value: 65.62599999999999
    - type: map_at_1000
      value: 65.657
    - type: map_at_3
      value: 62.352
    - type: map_at_5
      value: 64.025
    - type: mrr_at_1
      value: 57.333
    - type: mrr_at_10
      value: 66.577
    - type: mrr_at_100
      value: 66.88
    - type: mrr_at_1000
      value: 66.908
    - type: mrr_at_3
      value: 64.556
    - type: mrr_at_5
      value: 65.739
    - type: ndcg_at_1
      value: 57.333
    - type: ndcg_at_10
      value: 70.275
    - type: ndcg_at_100
      value: 72.136
    - type: ndcg_at_1000
      value: 72.963
    - type: ndcg_at_3
      value: 65.414
    - type: ndcg_at_5
      value: 67.831
    - type: precision_at_1
      value: 57.333
    - type: precision_at_10
      value: 9.5
    - type: precision_at_100
      value: 1.057
    - type: precision_at_1000
      value: 0.11199999999999999
    - type: precision_at_3
      value: 25.778000000000002
    - type: precision_at_5
      value: 17.2
    - type: recall_at_1
      value: 54.528
    - type: recall_at_10
      value: 84.356
    - type: recall_at_100
      value: 92.833
    - type: recall_at_1000
      value: 99.333
    - type: recall_at_3
      value: 71.283
    - type: recall_at_5
      value: 77.14999999999999
  - task:
      type: PairClassification
    dataset:
      name: MTEB SprintDuplicateQuestions
      type: mteb/sprintduplicatequestions-pairclassification
      config: default
      split: test
      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
    metrics:
    - type: cos_sim_accuracy
      value: 99.74158415841585
    - type: cos_sim_ap
      value: 92.90048959850317
    - type: cos_sim_f1
      value: 86.35650810245687
    - type: cos_sim_precision
      value: 90.4709748083242
    - type: cos_sim_recall
      value: 82.6
    - type: dot_accuracy
      value: 99.74158415841585
    - type: dot_ap
      value: 92.90048959850317
    - type: dot_f1
      value: 86.35650810245687
    - type: dot_precision
      value: 90.4709748083242
    - type: dot_recall
      value: 82.6
    - type: euclidean_accuracy
      value: 99.74158415841585
    - type: euclidean_ap
      value: 92.90048959850317
    - type: euclidean_f1
      value: 86.35650810245687
    - type: euclidean_precision
      value: 90.4709748083242
    - type: euclidean_recall
      value: 82.6
    - type: manhattan_accuracy
      value: 99.74158415841585
    - type: manhattan_ap
      value: 92.87344692947894
    - type: manhattan_f1
      value: 86.38497652582159
    - type: manhattan_precision
      value: 90.29443838604145
    - type: manhattan_recall
      value: 82.8
    - type: max_accuracy
      value: 99.74158415841585
    - type: max_ap
      value: 92.90048959850317
    - type: max_f1
      value: 86.38497652582159
  - task:
      type: Clustering
    dataset:
      name: MTEB StackExchangeClustering
      type: mteb/stackexchange-clustering
      config: default
      split: test
      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
    metrics:
    - type: v_measure
      value: 63.191648770424216
  - task:
      type: Clustering
    dataset:
      name: MTEB StackExchangeClusteringP2P
      type: mteb/stackexchange-clustering-p2p
      config: default
      split: test
      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
    metrics:
    - type: v_measure
      value: 34.02944668730218
  - task:
      type: Reranking
    dataset:
      name: MTEB StackOverflowDupQuestions
      type: mteb/stackoverflowdupquestions-reranking
      config: default
      split: test
      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
    metrics:
    - type: map
      value: 50.466386167525265
    - type: mrr
      value: 51.19071492233257
  - task:
      type: Summarization
    dataset:
      name: MTEB SummEval
      type: mteb/summeval
      config: default
      split: test
      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
    metrics:
    - type: cos_sim_pearson
      value: 30.198022505886435
    - type: cos_sim_spearman
      value: 30.40170257939193
    - type: dot_pearson
      value: 30.198015316402614
    - type: dot_spearman
      value: 30.40170257939193
  - task:
      type: Retrieval
    dataset:
      name: MTEB TRECCOVID
      type: trec-covid
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 0.242
    - type: map_at_10
      value: 2.17
    - type: map_at_100
      value: 12.221
    - type: map_at_1000
      value: 28.63
    - type: map_at_3
      value: 0.728
    - type: map_at_5
      value: 1.185
    - type: mrr_at_1
      value: 94
    - type: mrr_at_10
      value: 97
    - type: mrr_at_100
      value: 97
    - type: mrr_at_1000
      value: 97
    - type: mrr_at_3
      value: 97
    - type: mrr_at_5
      value: 97
    - type: ndcg_at_1
      value: 89
    - type: ndcg_at_10
      value: 82.30499999999999
    - type: ndcg_at_100
      value: 61.839999999999996
    - type: ndcg_at_1000
      value: 53.381
    - type: ndcg_at_3
      value: 88.877
    - type: ndcg_at_5
      value: 86.05199999999999
    - type: precision_at_1
      value: 94
    - type: precision_at_10
      value: 87
    - type: precision_at_100
      value: 63.38
    - type: precision_at_1000
      value: 23.498
    - type: precision_at_3
      value: 94
    - type: precision_at_5
      value: 92
    - type: recall_at_1
      value: 0.242
    - type: recall_at_10
      value: 2.302
    - type: recall_at_100
      value: 14.979000000000001
    - type: recall_at_1000
      value: 49.638
    - type: recall_at_3
      value: 0.753
    - type: recall_at_5
      value: 1.226
  - task:
      type: Retrieval
    dataset:
      name: MTEB Touche2020
      type: webis-touche2020
      config: default
      split: test
      revision: None
    metrics:
    - type: map_at_1
      value: 3.006
    - type: map_at_10
      value: 11.805
    - type: map_at_100
      value: 18.146
    - type: map_at_1000
      value: 19.788
    - type: map_at_3
      value: 5.914
    - type: map_at_5
      value: 8.801
    - type: mrr_at_1
      value: 40.816
    - type: mrr_at_10
      value: 56.36600000000001
    - type: mrr_at_100
      value: 56.721999999999994
    - type: mrr_at_1000
      value: 56.721999999999994
    - type: mrr_at_3
      value: 52.041000000000004
    - type: mrr_at_5
      value: 54.796
    - type: ndcg_at_1
      value: 37.755
    - type: ndcg_at_10
      value: 29.863
    - type: ndcg_at_100
      value: 39.571
    - type: ndcg_at_1000
      value: 51.385999999999996
    - type: ndcg_at_3
      value: 32.578
    - type: ndcg_at_5
      value: 32.351
    - type: precision_at_1
      value: 40.816
    - type: precision_at_10
      value: 26.531
    - type: precision_at_100
      value: 7.796
    - type: precision_at_1000
      value: 1.555
    - type: precision_at_3
      value: 32.653
    - type: precision_at_5
      value: 33.061
    - type: recall_at_1
      value: 3.006
    - type: recall_at_10
      value: 18.738
    - type: recall_at_100
      value: 48.058
    - type: recall_at_1000
      value: 83.41300000000001
    - type: recall_at_3
      value: 7.166
    - type: recall_at_5
      value: 12.102
  - task:
      type: Classification
    dataset:
      name: MTEB ToxicConversationsClassification
      type: mteb/toxic_conversations_50k
      config: default
      split: test
      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
    metrics:
    - type: accuracy
      value: 71.4178
    - type: ap
      value: 14.648781342150446
    - type: f1
      value: 55.07299194946378
  - task:
      type: Classification
    dataset:
      name: MTEB TweetSentimentExtractionClassification
      type: mteb/tweet_sentiment_extraction
      config: default
      split: test
      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
    metrics:
    - type: accuracy
      value: 60.919637804187886
    - type: f1
      value: 61.24122013967399
  - task:
      type: Clustering
    dataset:
      name: MTEB TwentyNewsgroupsClustering
      type: mteb/twentynewsgroups-clustering
      config: default
      split: test
      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
    metrics:
    - type: v_measure
      value: 49.207896583685695
  - task:
      type: PairClassification
    dataset:
      name: MTEB TwitterSemEval2015
      type: mteb/twittersemeval2015-pairclassification
      config: default
      split: test
      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
    metrics:
    - type: cos_sim_accuracy
      value: 86.23114978840078
    - type: cos_sim_ap
      value: 74.26624727825818
    - type: cos_sim_f1
      value: 68.72377190817083
    - type: cos_sim_precision
      value: 64.56400742115028
    - type: cos_sim_recall
      value: 73.45646437994723
    - type: dot_accuracy
      value: 86.23114978840078
    - type: dot_ap
      value: 74.26624032659652
    - type: dot_f1
      value: 68.72377190817083
    - type: dot_precision
      value: 64.56400742115028
    - type: dot_recall
      value: 73.45646437994723
    - type: euclidean_accuracy
      value: 86.23114978840078
    - type: euclidean_ap
      value: 74.26624714480556
    - type: euclidean_f1
      value: 68.72377190817083
    - type: euclidean_precision
      value: 64.56400742115028
    - type: euclidean_recall
      value: 73.45646437994723
    - type: manhattan_accuracy
      value: 86.16558383501221
    - type: manhattan_ap
      value: 74.2091943976357
    - type: manhattan_f1
      value: 68.64221520524654
    - type: manhattan_precision
      value: 63.59135913591359
    - type: manhattan_recall
      value: 74.5646437994723
    - type: max_accuracy
      value: 86.23114978840078
    - type: max_ap
      value: 74.26624727825818
    - type: max_f1
      value: 68.72377190817083
  - task:
      type: PairClassification
    dataset:
      name: MTEB TwitterURLCorpus
      type: mteb/twitterurlcorpus-pairclassification
      config: default
      split: test
      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
    metrics:
    - type: cos_sim_accuracy
      value: 89.3681841114604
    - type: cos_sim_ap
      value: 86.65166387498546
    - type: cos_sim_f1
      value: 79.02581944698774
    - type: cos_sim_precision
      value: 75.35796605434099
    - type: cos_sim_recall
      value: 83.06898675700647
    - type: dot_accuracy
      value: 89.3681841114604
    - type: dot_ap
      value: 86.65166019802056
    - type: dot_f1
      value: 79.02581944698774
    - type: dot_precision
      value: 75.35796605434099
    - type: dot_recall
      value: 83.06898675700647
    - type: euclidean_accuracy
      value: 89.3681841114604
    - type: euclidean_ap
      value: 86.65166462876266
    - type: euclidean_f1
      value: 79.02581944698774
    - type: euclidean_precision
      value: 75.35796605434099
    - type: euclidean_recall
      value: 83.06898675700647
    - type: manhattan_accuracy
      value: 89.36624364497226
    - type: manhattan_ap
      value: 86.65076471274106
    - type: manhattan_f1
      value: 79.07408783532733
    - type: manhattan_precision
      value: 76.41102972856527
    - type: manhattan_recall
      value: 81.92947336002464
    - type: max_accuracy
      value: 89.3681841114604
    - type: max_ap
      value: 86.65166462876266
    - type: max_f1
      value: 79.07408783532733
---

# YorkieOH10/nomic-embed-text-v1.5-Q8_0-GGUF
This model was converted to GGUF format from [`nomic-ai/nomic-embed-text-v1.5`](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5) 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/nomic-ai/nomic-embed-text-v1.5) 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 YorkieOH10/nomic-embed-text-v1.5-Q8_0-GGUF --hf-file nomic-embed-text-v1.5-q8_0.gguf -p "The meaning to life and the universe is"
```

### Server:
```bash
llama-server --hf-repo YorkieOH10/nomic-embed-text-v1.5-Q8_0-GGUF --hf-file nomic-embed-text-v1.5-q8_0.gguf -c 2048
```

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

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

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

Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo YorkieOH10/nomic-embed-text-v1.5-Q8_0-GGUF --hf-file nomic-embed-text-v1.5-q8_0.gguf -p "The meaning to life and the universe is"
```
or 
```
./llama-server --hf-repo YorkieOH10/nomic-embed-text-v1.5-Q8_0-GGUF --hf-file nomic-embed-text-v1.5-q8_0.gguf -c 2048
```
