Edited readme

README.md

@@ -1,2610 +1,15 @@
 ---
 tags:
-- mteb
-- sentence-similarity
-- sentence-transformers
-- Sentence Transformers
-model-index:
-- name: gte-small 
-  results:
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/amazon_counterfactual
-      name: MTEB AmazonCounterfactualClassification (en)
-      config: en
-      split: test
-      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
-    metrics:
-    - type: accuracy
-      value: 73.22388059701493
-    - type: ap
-      value: 36.09895941426988
-    - type: f1
-      value: 67.3205651539195
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/amazon_polarity
-      name: MTEB AmazonPolarityClassification
-      config: default
-      split: test
-      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
-    metrics:
-    - type: accuracy
-      value: 91.81894999999999
-    - type: ap
-      value: 88.5240138417305
-    - type: f1
-      value: 91.80367382706962
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/amazon_reviews_multi
-      name: MTEB AmazonReviewsClassification (en)
-      config: en
-      split: test
-      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
-    metrics:
-    - type: accuracy
-      value: 48.032
-    - type: f1
-      value: 47.4490665674719
-  - task:
-      type: Retrieval
-    dataset:
-      type: arguana
-      name: MTEB ArguAna
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 30.725
-    - type: map_at_10
-      value: 46.604
-    - type: map_at_100
-      value: 47.535
-    - type: map_at_1000
-      value: 47.538000000000004
-    - type: map_at_3
-      value: 41.833
-    - type: map_at_5
-      value: 44.61
-    - type: mrr_at_1
-      value: 31.223
-    - type: mrr_at_10
-      value: 46.794000000000004
-    - type: mrr_at_100
-      value: 47.725
-    - type: mrr_at_1000
-      value: 47.727000000000004
-    - type: mrr_at_3
-      value: 42.07
-    - type: mrr_at_5
-      value: 44.812000000000005
-    - type: ndcg_at_1
-      value: 30.725
-    - type: ndcg_at_10
-      value: 55.440999999999995
-    - type: ndcg_at_100
-      value: 59.134
-    - type: ndcg_at_1000
-      value: 59.199
-    - type: ndcg_at_3
-      value: 45.599000000000004
-    - type: ndcg_at_5
-      value: 50.637
-    - type: precision_at_1
-      value: 30.725
-    - type: precision_at_10
-      value: 8.364
-    - type: precision_at_100
-      value: 0.991
-    - type: precision_at_1000
-      value: 0.1
-    - type: precision_at_3
-      value: 18.848000000000003
-    - type: precision_at_5
-      value: 13.77
-    - type: recall_at_1
-      value: 30.725
-    - type: recall_at_10
-      value: 83.64200000000001
-    - type: recall_at_100
-      value: 99.14699999999999
-    - type: recall_at_1000
-      value: 99.644
-    - type: recall_at_3
-      value: 56.543
-    - type: recall_at_5
-      value: 68.848
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/arxiv-clustering-p2p
-      name: MTEB ArxivClusteringP2P
-      config: default
-      split: test
-      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
-    metrics:
-    - type: v_measure
-      value: 47.90178078197678
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/arxiv-clustering-s2s
-      name: MTEB ArxivClusteringS2S
-      config: default
-      split: test
-      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
-    metrics:
-    - type: v_measure
-      value: 40.25728393431922
-  - task:
-      type: Reranking
-    dataset:
-      type: mteb/askubuntudupquestions-reranking
-      name: MTEB AskUbuntuDupQuestions
-      config: default
-      split: test
-      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
-    metrics:
-    - type: map
-      value: 61.720297062897764
-    - type: mrr
-      value: 75.24139295607439
-  - task:
-      type: STS
-    dataset:
-      type: mteb/biosses-sts
-      name: MTEB BIOSSES
-      config: default
-      split: test
-      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
-    metrics:
-    - type: cos_sim_pearson
-      value: 89.43527309184616
-    - type: cos_sim_spearman
-      value: 88.17128615100206
-    - type: euclidean_pearson
-      value: 87.89922623089282
-    - type: euclidean_spearman
-      value: 87.96104039655451
-    - type: manhattan_pearson
-      value: 87.9818290932077
-    - type: manhattan_spearman
-      value: 88.00923426576885
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/banking77
-      name: MTEB Banking77Classification
-      config: default
-      split: test
-      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
-    metrics:
-    - type: accuracy
-      value: 84.0844155844156
-    - type: f1
-      value: 84.01485017302213
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/biorxiv-clustering-p2p
-      name: MTEB BiorxivClusteringP2P
-      config: default
-      split: test
-      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
-    metrics:
-    - type: v_measure
-      value: 38.36574769259432
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/biorxiv-clustering-s2s
-      name: MTEB BiorxivClusteringS2S
-      config: default
-      split: test
-      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
-    metrics:
-    - type: v_measure
-      value: 35.4857033165287
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackAndroidRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 30.261
-    - type: map_at_10
-      value: 42.419000000000004
-    - type: map_at_100
-      value: 43.927
-    - type: map_at_1000
-      value: 44.055
-    - type: map_at_3
-      value: 38.597
-    - type: map_at_5
-      value: 40.701
-    - type: mrr_at_1
-      value: 36.91
-    - type: mrr_at_10
-      value: 48.02
-    - type: mrr_at_100
-      value: 48.658
-    - type: mrr_at_1000
-      value: 48.708
-    - type: mrr_at_3
-      value: 44.945
-    - type: mrr_at_5
-      value: 46.705000000000005
-    - type: ndcg_at_1
-      value: 36.91
-    - type: ndcg_at_10
-      value: 49.353
-    - type: ndcg_at_100
-      value: 54.456
-    - type: ndcg_at_1000
-      value: 56.363
-    - type: ndcg_at_3
-      value: 43.483
-    - type: ndcg_at_5
-      value: 46.150999999999996
-    - type: precision_at_1
-      value: 36.91
-    - type: precision_at_10
-      value: 9.700000000000001
-    - type: precision_at_100
-      value: 1.557
-    - type: precision_at_1000
-      value: 0.202
-    - type: precision_at_3
-      value: 21.078
-    - type: precision_at_5
-      value: 15.421999999999999
-    - type: recall_at_1
-      value: 30.261
-    - type: recall_at_10
-      value: 63.242
-    - type: recall_at_100
-      value: 84.09100000000001
-    - type: recall_at_1000
-      value: 96.143
-    - type: recall_at_3
-      value: 46.478
-    - type: recall_at_5
-      value: 53.708
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackEnglishRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 31.145
-    - type: map_at_10
-      value: 40.996
-    - type: map_at_100
-      value: 42.266999999999996
-    - type: map_at_1000
-      value: 42.397
-    - type: map_at_3
-      value: 38.005
-    - type: map_at_5
-      value: 39.628
-    - type: mrr_at_1
-      value: 38.344
-    - type: mrr_at_10
-      value: 46.827000000000005
-    - type: mrr_at_100
-      value: 47.446
-    - type: mrr_at_1000
-      value: 47.489
-    - type: mrr_at_3
-      value: 44.448
-    - type: mrr_at_5
-      value: 45.747
-    - type: ndcg_at_1
-      value: 38.344
-    - type: ndcg_at_10
-      value: 46.733000000000004
-    - type: ndcg_at_100
-      value: 51.103
-    - type: ndcg_at_1000
-      value: 53.075
-    - type: ndcg_at_3
-      value: 42.366
-    - type: ndcg_at_5
-      value: 44.242
-    - type: precision_at_1
-      value: 38.344
-    - type: precision_at_10
-      value: 8.822000000000001
-    - type: precision_at_100
-      value: 1.417
-    - type: precision_at_1000
-      value: 0.187
-    - type: precision_at_3
-      value: 20.403
-    - type: precision_at_5
-      value: 14.306
-    - type: recall_at_1
-      value: 31.145
-    - type: recall_at_10
-      value: 56.909
-    - type: recall_at_100
-      value: 75.274
-    - type: recall_at_1000
-      value: 87.629
-    - type: recall_at_3
-      value: 43.784
-    - type: recall_at_5
-      value: 49.338
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackGamingRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 38.83
-    - type: map_at_10
-      value: 51.553000000000004
-    - type: map_at_100
-      value: 52.581
-    - type: map_at_1000
-      value: 52.638
-    - type: map_at_3
-      value: 48.112
-    - type: map_at_5
-      value: 50.095
-    - type: mrr_at_1
-      value: 44.513999999999996
-    - type: mrr_at_10
-      value: 54.998000000000005
-    - type: mrr_at_100
-      value: 55.650999999999996
-    - type: mrr_at_1000
-      value: 55.679
-    - type: mrr_at_3
-      value: 52.602000000000004
-    - type: mrr_at_5
-      value: 53.931
-    - type: ndcg_at_1
-      value: 44.513999999999996
-    - type: ndcg_at_10
-      value: 57.67400000000001
-    - type: ndcg_at_100
-      value: 61.663999999999994
-    - type: ndcg_at_1000
-      value: 62.743
-    - type: ndcg_at_3
-      value: 51.964
-    - type: ndcg_at_5
-      value: 54.773
-    - type: precision_at_1
-      value: 44.513999999999996
-    - type: precision_at_10
-      value: 9.423
-    - type: precision_at_100
-      value: 1.2309999999999999
-    - type: precision_at_1000
-      value: 0.13699999999999998
-    - type: precision_at_3
-      value: 23.323
-    - type: precision_at_5
-      value: 16.163
-    - type: recall_at_1
-      value: 38.83
-    - type: recall_at_10
-      value: 72.327
-    - type: recall_at_100
-      value: 89.519
-    - type: recall_at_1000
-      value: 97.041
-    - type: recall_at_3
-      value: 57.206
-    - type: recall_at_5
-      value: 63.88399999999999
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackGisRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 25.484
-    - type: map_at_10
-      value: 34.527
-    - type: map_at_100
-      value: 35.661
-    - type: map_at_1000
-      value: 35.739
-    - type: map_at_3
-      value: 32.199
-    - type: map_at_5
-      value: 33.632
-    - type: mrr_at_1
-      value: 27.458
-    - type: mrr_at_10
-      value: 36.543
-    - type: mrr_at_100
-      value: 37.482
-    - type: mrr_at_1000
-      value: 37.543
-    - type: mrr_at_3
-      value: 34.256
-    - type: mrr_at_5
-      value: 35.618
-    - type: ndcg_at_1
-      value: 27.458
-    - type: ndcg_at_10
-      value: 39.396
-    - type: ndcg_at_100
-      value: 44.742
-    - type: ndcg_at_1000
-      value: 46.708
-    - type: ndcg_at_3
-      value: 34.817
-    - type: ndcg_at_5
-      value: 37.247
-    - type: precision_at_1
-      value: 27.458
-    - type: precision_at_10
-      value: 5.976999999999999
-    - type: precision_at_100
-      value: 0.907
-    - type: precision_at_1000
-      value: 0.11100000000000002
-    - type: precision_at_3
-      value: 14.878
-    - type: precision_at_5
-      value: 10.35
-    - type: recall_at_1
-      value: 25.484
-    - type: recall_at_10
-      value: 52.317
-    - type: recall_at_100
-      value: 76.701
-    - type: recall_at_1000
-      value: 91.408
-    - type: recall_at_3
-      value: 40.043
-    - type: recall_at_5
-      value: 45.879
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackMathematicaRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 16.719
-    - type: map_at_10
-      value: 25.269000000000002
-    - type: map_at_100
-      value: 26.442
-    - type: map_at_1000
-      value: 26.557
-    - type: map_at_3
-      value: 22.56
-    - type: map_at_5
-      value: 24.082
-    - type: mrr_at_1
-      value: 20.896
-    - type: mrr_at_10
-      value: 29.982999999999997
-    - type: mrr_at_100
-      value: 30.895
-    - type: mrr_at_1000
-      value: 30.961
-    - type: mrr_at_3
-      value: 27.239
-    - type: mrr_at_5
-      value: 28.787000000000003
-    - type: ndcg_at_1
-      value: 20.896
-    - type: ndcg_at_10
-      value: 30.814000000000004
-    - type: ndcg_at_100
-      value: 36.418
-    - type: ndcg_at_1000
-      value: 39.182
-    - type: ndcg_at_3
-      value: 25.807999999999996
-    - type: ndcg_at_5
-      value: 28.143
-    - type: precision_at_1
-      value: 20.896
-    - type: precision_at_10
-      value: 5.821
-    - type: precision_at_100
-      value: 0.991
-    - type: precision_at_1000
-      value: 0.136
-    - type: precision_at_3
-      value: 12.562000000000001
-    - type: precision_at_5
-      value: 9.254
-    - type: recall_at_1
-      value: 16.719
-    - type: recall_at_10
-      value: 43.155
-    - type: recall_at_100
-      value: 67.831
-    - type: recall_at_1000
-      value: 87.617
-    - type: recall_at_3
-      value: 29.259
-    - type: recall_at_5
-      value: 35.260999999999996
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackPhysicsRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 29.398999999999997
-    - type: map_at_10
-      value: 39.876
-    - type: map_at_100
-      value: 41.205999999999996
-    - type: map_at_1000
-      value: 41.321999999999996
-    - type: map_at_3
-      value: 36.588
-    - type: map_at_5
-      value: 38.538
-    - type: mrr_at_1
-      value: 35.9
-    - type: mrr_at_10
-      value: 45.528
-    - type: mrr_at_100
-      value: 46.343
-    - type: mrr_at_1000
-      value: 46.388
-    - type: mrr_at_3
-      value: 42.862
-    - type: mrr_at_5
-      value: 44.440000000000005
-    - type: ndcg_at_1
-      value: 35.9
-    - type: ndcg_at_10
-      value: 45.987
-    - type: ndcg_at_100
-      value: 51.370000000000005
-    - type: ndcg_at_1000
-      value: 53.400000000000006
-    - type: ndcg_at_3
-      value: 40.841
-    - type: ndcg_at_5
-      value: 43.447
-    - type: precision_at_1
-      value: 35.9
-    - type: precision_at_10
-      value: 8.393
-    - type: precision_at_100
-      value: 1.283
-    - type: precision_at_1000
-      value: 0.166
-    - type: precision_at_3
-      value: 19.538
-    - type: precision_at_5
-      value: 13.975000000000001
-    - type: recall_at_1
-      value: 29.398999999999997
-    - type: recall_at_10
-      value: 58.361
-    - type: recall_at_100
-      value: 81.081
-    - type: recall_at_1000
-      value: 94.004
-    - type: recall_at_3
-      value: 43.657000000000004
-    - type: recall_at_5
-      value: 50.519999999999996
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackProgrammersRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 21.589
-    - type: map_at_10
-      value: 31.608999999999998
-    - type: map_at_100
-      value: 33.128
-    - type: map_at_1000
-      value: 33.247
-    - type: map_at_3
-      value: 28.671999999999997
-    - type: map_at_5
-      value: 30.233999999999998
-    - type: mrr_at_1
-      value: 26.712000000000003
-    - type: mrr_at_10
-      value: 36.713
-    - type: mrr_at_100
-      value: 37.713
-    - type: mrr_at_1000
-      value: 37.771
-    - type: mrr_at_3
-      value: 34.075
-    - type: mrr_at_5
-      value: 35.451
-    - type: ndcg_at_1
-      value: 26.712000000000003
-    - type: ndcg_at_10
-      value: 37.519999999999996
-    - type: ndcg_at_100
-      value: 43.946000000000005
-    - type: ndcg_at_1000
-      value: 46.297
-    - type: ndcg_at_3
-      value: 32.551
-    - type: ndcg_at_5
-      value: 34.660999999999994
-    - type: precision_at_1
-      value: 26.712000000000003
-    - type: precision_at_10
-      value: 7.066
-    - type: precision_at_100
-      value: 1.216
-    - type: precision_at_1000
-      value: 0.157
-    - type: precision_at_3
-      value: 15.906
-    - type: precision_at_5
-      value: 11.437999999999999
-    - type: recall_at_1
-      value: 21.589
-    - type: recall_at_10
-      value: 50.090999999999994
-    - type: recall_at_100
-      value: 77.43900000000001
-    - type: recall_at_1000
-      value: 93.35900000000001
-    - type: recall_at_3
-      value: 36.028999999999996
-    - type: recall_at_5
-      value: 41.698
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 25.121666666666663
-    - type: map_at_10
-      value: 34.46258333333334
-    - type: map_at_100
-      value: 35.710499999999996
-    - type: map_at_1000
-      value: 35.82691666666666
-    - type: map_at_3
-      value: 31.563249999999996
-    - type: map_at_5
-      value: 33.189750000000004
-    - type: mrr_at_1
-      value: 29.66441666666667
-    - type: mrr_at_10
-      value: 38.5455
-    - type: mrr_at_100
-      value: 39.39566666666667
-    - type: mrr_at_1000
-      value: 39.45325
-    - type: mrr_at_3
-      value: 36.003333333333345
-    - type: mrr_at_5
-      value: 37.440916666666666
-    - type: ndcg_at_1
-      value: 29.66441666666667
-    - type: ndcg_at_10
-      value: 39.978416666666675
-    - type: ndcg_at_100
-      value: 45.278666666666666
-    - type: ndcg_at_1000
-      value: 47.52275
-    - type: ndcg_at_3
-      value: 35.00058333333334
-    - type: ndcg_at_5
-      value: 37.34908333333333
-    - type: precision_at_1
-      value: 29.66441666666667
-    - type: precision_at_10
-      value: 7.094500000000001
-    - type: precision_at_100
-      value: 1.1523333333333332
-    - type: precision_at_1000
-      value: 0.15358333333333332
-    - type: precision_at_3
-      value: 16.184166666666663
-    - type: precision_at_5
-      value: 11.6005
-    - type: recall_at_1
-      value: 25.121666666666663
-    - type: recall_at_10
-      value: 52.23975000000001
-    - type: recall_at_100
-      value: 75.48408333333333
-    - type: recall_at_1000
-      value: 90.95316666666668
-    - type: recall_at_3
-      value: 38.38458333333333
-    - type: recall_at_5
-      value: 44.39933333333333
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackStatsRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 23.569000000000003
-    - type: map_at_10
-      value: 30.389
-    - type: map_at_100
-      value: 31.396
-    - type: map_at_1000
-      value: 31.493
-    - type: map_at_3
-      value: 28.276
-    - type: map_at_5
-      value: 29.459000000000003
-    - type: mrr_at_1
-      value: 26.534000000000002
-    - type: mrr_at_10
-      value: 33.217999999999996
-    - type: mrr_at_100
-      value: 34.054
-    - type: mrr_at_1000
-      value: 34.12
-    - type: mrr_at_3
-      value: 31.058000000000003
-    - type: mrr_at_5
-      value: 32.330999999999996
-    - type: ndcg_at_1
-      value: 26.534000000000002
-    - type: ndcg_at_10
-      value: 34.608
-    - type: ndcg_at_100
-      value: 39.391999999999996
-    - type: ndcg_at_1000
-      value: 41.837999999999994
-    - type: ndcg_at_3
-      value: 30.564999999999998
-    - type: ndcg_at_5
-      value: 32.509
-    - type: precision_at_1
-      value: 26.534000000000002
-    - type: precision_at_10
-      value: 5.414
-    - type: precision_at_100
-      value: 0.847
-    - type: precision_at_1000
-      value: 0.11399999999999999
-    - type: precision_at_3
-      value: 12.986
-    - type: precision_at_5
-      value: 9.202
-    - type: recall_at_1
-      value: 23.569000000000003
-    - type: recall_at_10
-      value: 44.896
-    - type: recall_at_100
-      value: 66.476
-    - type: recall_at_1000
-      value: 84.548
-    - type: recall_at_3
-      value: 33.79
-    - type: recall_at_5
-      value: 38.512
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackTexRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 16.36
-    - type: map_at_10
-      value: 23.57
-    - type: map_at_100
-      value: 24.698999999999998
-    - type: map_at_1000
-      value: 24.834999999999997
-    - type: map_at_3
-      value: 21.093
-    - type: map_at_5
-      value: 22.418
-    - type: mrr_at_1
-      value: 19.718
-    - type: mrr_at_10
-      value: 27.139999999999997
-    - type: mrr_at_100
-      value: 28.097
-    - type: mrr_at_1000
-      value: 28.177999999999997
-    - type: mrr_at_3
-      value: 24.805
-    - type: mrr_at_5
-      value: 26.121
-    - type: ndcg_at_1
-      value: 19.718
-    - type: ndcg_at_10
-      value: 28.238999999999997
-    - type: ndcg_at_100
-      value: 33.663
-    - type: ndcg_at_1000
-      value: 36.763
-    - type: ndcg_at_3
-      value: 23.747
-    - type: ndcg_at_5
-      value: 25.796000000000003
-    - type: precision_at_1
-      value: 19.718
-    - type: precision_at_10
-      value: 5.282
-    - type: precision_at_100
-      value: 0.9390000000000001
-    - type: precision_at_1000
-      value: 0.13899999999999998
-    - type: precision_at_3
-      value: 11.264000000000001
-    - type: precision_at_5
-      value: 8.341
-    - type: recall_at_1
-      value: 16.36
-    - type: recall_at_10
-      value: 38.669
-    - type: recall_at_100
-      value: 63.184
-    - type: recall_at_1000
-      value: 85.33800000000001
-    - type: recall_at_3
-      value: 26.214
-    - type: recall_at_5
-      value: 31.423000000000002
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackUnixRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 25.618999999999996
-    - type: map_at_10
-      value: 34.361999999999995
-    - type: map_at_100
-      value: 35.534
-    - type: map_at_1000
-      value: 35.634
-    - type: map_at_3
-      value: 31.402
-    - type: map_at_5
-      value: 32.815
-    - type: mrr_at_1
-      value: 30.037000000000003
-    - type: mrr_at_10
-      value: 38.284
-    - type: mrr_at_100
-      value: 39.141999999999996
-    - type: mrr_at_1000
-      value: 39.2
-    - type: mrr_at_3
-      value: 35.603
-    - type: mrr_at_5
-      value: 36.867
-    - type: ndcg_at_1
-      value: 30.037000000000003
-    - type: ndcg_at_10
-      value: 39.87
-    - type: ndcg_at_100
-      value: 45.243
-    - type: ndcg_at_1000
-      value: 47.507
-    - type: ndcg_at_3
-      value: 34.371
-    - type: ndcg_at_5
-      value: 36.521
-    - type: precision_at_1
-      value: 30.037000000000003
-    - type: precision_at_10
-      value: 6.819
-    - type: precision_at_100
-      value: 1.0699999999999998
-    - type: precision_at_1000
-      value: 0.13699999999999998
-    - type: precision_at_3
-      value: 15.392
-    - type: precision_at_5
-      value: 10.821
-    - type: recall_at_1
-      value: 25.618999999999996
-    - type: recall_at_10
-      value: 52.869
-    - type: recall_at_100
-      value: 76.395
-    - type: recall_at_1000
-      value: 92.19500000000001
-    - type: recall_at_3
-      value: 37.943
-    - type: recall_at_5
-      value: 43.342999999999996
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackWebmastersRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 23.283
-    - type: map_at_10
-      value: 32.155
-    - type: map_at_100
-      value: 33.724
-    - type: map_at_1000
-      value: 33.939
-    - type: map_at_3
-      value: 29.018
-    - type: map_at_5
-      value: 30.864000000000004
-    - type: mrr_at_1
-      value: 28.063
-    - type: mrr_at_10
-      value: 36.632
-    - type: mrr_at_100
-      value: 37.606
-    - type: mrr_at_1000
-      value: 37.671
-    - type: mrr_at_3
-      value: 33.992
-    - type: mrr_at_5
-      value: 35.613
-    - type: ndcg_at_1
-      value: 28.063
-    - type: ndcg_at_10
-      value: 38.024
-    - type: ndcg_at_100
-      value: 44.292
-    - type: ndcg_at_1000
-      value: 46.818
-    - type: ndcg_at_3
-      value: 32.965
-    - type: ndcg_at_5
-      value: 35.562
-    - type: precision_at_1
-      value: 28.063
-    - type: precision_at_10
-      value: 7.352
-    - type: precision_at_100
-      value: 1.514
-    - type: precision_at_1000
-      value: 0.23800000000000002
-    - type: precision_at_3
-      value: 15.481
-    - type: precision_at_5
-      value: 11.542
-    - type: recall_at_1
-      value: 23.283
-    - type: recall_at_10
-      value: 49.756
-    - type: recall_at_100
-      value: 78.05
-    - type: recall_at_1000
-      value: 93.854
-    - type: recall_at_3
-      value: 35.408
-    - type: recall_at_5
-      value: 42.187000000000005
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackWordpressRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 19.201999999999998
-    - type: map_at_10
-      value: 26.826
-    - type: map_at_100
-      value: 27.961000000000002
-    - type: map_at_1000
-      value: 28.066999999999997
-    - type: map_at_3
-      value: 24.237000000000002
-    - type: map_at_5
-      value: 25.811
-    - type: mrr_at_1
-      value: 20.887
-    - type: mrr_at_10
-      value: 28.660000000000004
-    - type: mrr_at_100
-      value: 29.660999999999998
-    - type: mrr_at_1000
-      value: 29.731
-    - type: mrr_at_3
-      value: 26.155
-    - type: mrr_at_5
-      value: 27.68
-    - type: ndcg_at_1
-      value: 20.887
-    - type: ndcg_at_10
-      value: 31.523
-    - type: ndcg_at_100
-      value: 37.055
-    - type: ndcg_at_1000
-      value: 39.579
-    - type: ndcg_at_3
-      value: 26.529000000000003
-    - type: ndcg_at_5
-      value: 29.137
-    - type: precision_at_1
-      value: 20.887
-    - type: precision_at_10
-      value: 5.065
-    - type: precision_at_100
-      value: 0.856
-    - type: precision_at_1000
-      value: 0.11900000000000001
-    - type: precision_at_3
-      value: 11.399
-    - type: precision_at_5
-      value: 8.392
-    - type: recall_at_1
-      value: 19.201999999999998
-    - type: recall_at_10
-      value: 44.285000000000004
-    - type: recall_at_100
-      value: 69.768
-    - type: recall_at_1000
-      value: 88.302
-    - type: recall_at_3
-      value: 30.804
-    - type: recall_at_5
-      value: 37.039
-  - task:
-      type: Retrieval
-    dataset:
-      type: climate-fever
-      name: MTEB ClimateFEVER
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 11.244
-    - type: map_at_10
-      value: 18.956
-    - type: map_at_100
-      value: 20.674
-    - type: map_at_1000
-      value: 20.863
-    - type: map_at_3
-      value: 15.923000000000002
-    - type: map_at_5
-      value: 17.518
-    - type: mrr_at_1
-      value: 25.080999999999996
-    - type: mrr_at_10
-      value: 35.94
-    - type: mrr_at_100
-      value: 36.969
-    - type: mrr_at_1000
-      value: 37.013
-    - type: mrr_at_3
-      value: 32.617000000000004
-    - type: mrr_at_5
-      value: 34.682
-    - type: ndcg_at_1
-      value: 25.080999999999996
-    - type: ndcg_at_10
-      value: 26.539
-    - type: ndcg_at_100
-      value: 33.601
-    - type: ndcg_at_1000
-      value: 37.203
-    - type: ndcg_at_3
-      value: 21.695999999999998
-    - type: ndcg_at_5
-      value: 23.567
-    - type: precision_at_1
-      value: 25.080999999999996
-    - type: precision_at_10
-      value: 8.143
-    - type: precision_at_100
-      value: 1.5650000000000002
-    - type: precision_at_1000
-      value: 0.22300000000000003
-    - type: precision_at_3
-      value: 15.983
-    - type: precision_at_5
-      value: 12.417
-    - type: recall_at_1
-      value: 11.244
-    - type: recall_at_10
-      value: 31.457
-    - type: recall_at_100
-      value: 55.92
-    - type: recall_at_1000
-      value: 76.372
-    - type: recall_at_3
-      value: 19.784
-    - type: recall_at_5
-      value: 24.857000000000003
-  - task:
-      type: Retrieval
-    dataset:
-      type: dbpedia-entity
-      name: MTEB DBPedia
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 8.595
-    - type: map_at_10
-      value: 18.75
-    - type: map_at_100
-      value: 26.354
-    - type: map_at_1000
-      value: 27.912
-    - type: map_at_3
-      value: 13.794
-    - type: map_at_5
-      value: 16.021
-    - type: mrr_at_1
-      value: 65.75
-    - type: mrr_at_10
-      value: 73.837
-    - type: mrr_at_100
-      value: 74.22800000000001
-    - type: mrr_at_1000
-      value: 74.234
-    - type: mrr_at_3
-      value: 72.5
-    - type: mrr_at_5
-      value: 73.387
-    - type: ndcg_at_1
-      value: 52.625
-    - type: ndcg_at_10
-      value: 39.101
-    - type: ndcg_at_100
-      value: 43.836000000000006
-    - type: ndcg_at_1000
-      value: 51.086
-    - type: ndcg_at_3
-      value: 44.229
-    - type: ndcg_at_5
-      value: 41.555
-    - type: precision_at_1
-      value: 65.75
-    - type: precision_at_10
-      value: 30.45
-    - type: precision_at_100
-      value: 9.81
-    - type: precision_at_1000
-      value: 2.045
-    - type: precision_at_3
-      value: 48.667
-    - type: precision_at_5
-      value: 40.8
-    - type: recall_at_1
-      value: 8.595
-    - type: recall_at_10
-      value: 24.201
-    - type: recall_at_100
-      value: 50.096
-    - type: recall_at_1000
-      value: 72.677
-    - type: recall_at_3
-      value: 15.212
-    - type: recall_at_5
-      value: 18.745
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/emotion
-      name: MTEB EmotionClassification
-      config: default
-      split: test
-      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
-    metrics:
-    - type: accuracy
-      value: 46.565
-    - type: f1
-      value: 41.49914329345582
-  - task:
-      type: Retrieval
-    dataset:
-      type: fever
-      name: MTEB FEVER
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 66.60000000000001
-    - type: map_at_10
-      value: 76.838
-    - type: map_at_100
-      value: 77.076
-    - type: map_at_1000
-      value: 77.09
-    - type: map_at_3
-      value: 75.545
-    - type: map_at_5
-      value: 76.39
-    - type: mrr_at_1
-      value: 71.707
-    - type: mrr_at_10
-      value: 81.514
-    - type: mrr_at_100
-      value: 81.64099999999999
-    - type: mrr_at_1000
-      value: 81.645
-    - type: mrr_at_3
-      value: 80.428
-    - type: mrr_at_5
-      value: 81.159
-    - type: ndcg_at_1
-      value: 71.707
-    - type: ndcg_at_10
-      value: 81.545
-    - type: ndcg_at_100
-      value: 82.477
-    - type: ndcg_at_1000
-      value: 82.73899999999999
-    - type: ndcg_at_3
-      value: 79.292
-    - type: ndcg_at_5
-      value: 80.599
-    - type: precision_at_1
-      value: 71.707
-    - type: precision_at_10
-      value: 10.035
-    - type: precision_at_100
-      value: 1.068
-    - type: precision_at_1000
-      value: 0.11100000000000002
-    - type: precision_at_3
-      value: 30.918
-    - type: precision_at_5
-      value: 19.328
-    - type: recall_at_1
-      value: 66.60000000000001
-    - type: recall_at_10
-      value: 91.353
-    - type: recall_at_100
-      value: 95.21
-    - type: recall_at_1000
-      value: 96.89999999999999
-    - type: recall_at_3
-      value: 85.188
-    - type: recall_at_5
-      value: 88.52
-  - task:
-      type: Retrieval
-    dataset:
-      type: fiqa
-      name: MTEB FiQA2018
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 19.338
-    - type: map_at_10
-      value: 31.752000000000002
-    - type: map_at_100
-      value: 33.516
-    - type: map_at_1000
-      value: 33.694
-    - type: map_at_3
-      value: 27.716
-    - type: map_at_5
-      value: 29.67
-    - type: mrr_at_1
-      value: 38.117000000000004
-    - type: mrr_at_10
-      value: 47.323
-    - type: mrr_at_100
-      value: 48.13
-    - type: mrr_at_1000
-      value: 48.161
-    - type: mrr_at_3
-      value: 45.062000000000005
-    - type: mrr_at_5
-      value: 46.358
-    - type: ndcg_at_1
-      value: 38.117000000000004
-    - type: ndcg_at_10
-      value: 39.353
-    - type: ndcg_at_100
-      value: 46.044000000000004
-    - type: ndcg_at_1000
-      value: 49.083
-    - type: ndcg_at_3
-      value: 35.891
-    - type: ndcg_at_5
-      value: 36.661
-    - type: precision_at_1
-      value: 38.117000000000004
-    - type: precision_at_10
-      value: 11.187999999999999
-    - type: precision_at_100
-      value: 1.802
-    - type: precision_at_1000
-      value: 0.234
-    - type: precision_at_3
-      value: 24.126
-    - type: precision_at_5
-      value: 17.562
-    - type: recall_at_1
-      value: 19.338
-    - type: recall_at_10
-      value: 45.735
-    - type: recall_at_100
-      value: 71.281
-    - type: recall_at_1000
-      value: 89.537
-    - type: recall_at_3
-      value: 32.525
-    - type: recall_at_5
-      value: 37.671
-  - task:
-      type: Retrieval
-    dataset:
-      type: hotpotqa
-      name: MTEB HotpotQA
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 36.995
-    - type: map_at_10
-      value: 55.032000000000004
-    - type: map_at_100
-      value: 55.86
-    - type: map_at_1000
-      value: 55.932
-    - type: map_at_3
-      value: 52.125
-    - type: map_at_5
-      value: 53.884
-    - type: mrr_at_1
-      value: 73.991
-    - type: mrr_at_10
-      value: 80.096
-    - type: mrr_at_100
-      value: 80.32000000000001
-    - type: mrr_at_1000
-      value: 80.331
-    - type: mrr_at_3
-      value: 79.037
-    - type: mrr_at_5
-      value: 79.719
-    - type: ndcg_at_1
-      value: 73.991
-    - type: ndcg_at_10
-      value: 63.786
-    - type: ndcg_at_100
-      value: 66.78
-    - type: ndcg_at_1000
-      value: 68.255
-    - type: ndcg_at_3
-      value: 59.501000000000005
-    - type: ndcg_at_5
-      value: 61.82299999999999
-    - type: precision_at_1
-      value: 73.991
-    - type: precision_at_10
-      value: 13.157
-    - type: precision_at_100
-      value: 1.552
-    - type: precision_at_1000
-      value: 0.17500000000000002
-    - type: precision_at_3
-      value: 37.519999999999996
-    - type: precision_at_5
-      value: 24.351
-    - type: recall_at_1
-      value: 36.995
-    - type: recall_at_10
-      value: 65.78699999999999
-    - type: recall_at_100
-      value: 77.583
-    - type: recall_at_1000
-      value: 87.421
-    - type: recall_at_3
-      value: 56.279999999999994
-    - type: recall_at_5
-      value: 60.878
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/imdb
-      name: MTEB ImdbClassification
-      config: default
-      split: test
-      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
-    metrics:
-    - type: accuracy
-      value: 86.80239999999999
-    - type: ap
-      value: 81.97305141128378
-    - type: f1
-      value: 86.76976305549273
-  - task:
-      type: Retrieval
-    dataset:
-      type: msmarco
-      name: MTEB MSMARCO
-      config: default
-      split: dev
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 21.166
-    - type: map_at_10
-      value: 33.396
-    - type: map_at_100
-      value: 34.588
-    - type: map_at_1000
-      value: 34.637
-    - type: map_at_3
-      value: 29.509999999999998
-    - type: map_at_5
-      value: 31.719
-    - type: mrr_at_1
-      value: 21.762
-    - type: mrr_at_10
-      value: 33.969
-    - type: mrr_at_100
-      value: 35.099000000000004
-    - type: mrr_at_1000
-      value: 35.141
-    - type: mrr_at_3
-      value: 30.148000000000003
-    - type: mrr_at_5
-      value: 32.324000000000005
-    - type: ndcg_at_1
-      value: 21.776999999999997
-    - type: ndcg_at_10
-      value: 40.306999999999995
-    - type: ndcg_at_100
-      value: 46.068
-    - type: ndcg_at_1000
-      value: 47.3
-    - type: ndcg_at_3
-      value: 32.416
-    - type: ndcg_at_5
-      value: 36.345
-    - type: precision_at_1
-      value: 21.776999999999997
-    - type: precision_at_10
-      value: 6.433
-    - type: precision_at_100
-      value: 0.932
-    - type: precision_at_1000
-      value: 0.104
-    - type: precision_at_3
-      value: 13.897
-    - type: precision_at_5
-      value: 10.324
-    - type: recall_at_1
-      value: 21.166
-    - type: recall_at_10
-      value: 61.587
-    - type: recall_at_100
-      value: 88.251
-    - type: recall_at_1000
-      value: 97.727
-    - type: recall_at_3
-      value: 40.196
-    - type: recall_at_5
-      value: 49.611
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/mtop_domain
-      name: MTEB MTOPDomainClassification (en)
-      config: en
-      split: test
-      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
-    metrics:
-    - type: accuracy
-      value: 93.04605563155496
-    - type: f1
-      value: 92.78007303978372
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/mtop_intent
-      name: MTEB MTOPIntentClassification (en)
-      config: en
-      split: test
-      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
-    metrics:
-    - type: accuracy
-      value: 69.65116279069767
-    - type: f1
-      value: 52.75775172527262
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/amazon_massive_intent
-      name: MTEB MassiveIntentClassification (en)
-      config: en
-      split: test
-      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
-    metrics:
-    - type: accuracy
-      value: 70.34633490248822
-    - type: f1
-      value: 68.15345065392562
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/amazon_massive_scenario
-      name: MTEB MassiveScenarioClassification (en)
-      config: en
-      split: test
-      revision: 7d571f92784cd94a019292a1f45445077d0ef634
-    metrics:
-    - type: accuracy
-      value: 75.63887020847343
-    - type: f1
-      value: 76.08074680233685
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/medrxiv-clustering-p2p
-      name: MTEB MedrxivClusteringP2P
-      config: default
-      split: test
-      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
-    metrics:
-    - type: v_measure
-      value: 33.77933406071333
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/medrxiv-clustering-s2s
-      name: MTEB MedrxivClusteringS2S
-      config: default
-      split: test
-      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
-    metrics:
-    - type: v_measure
-      value: 32.06504927238196
-  - task:
-      type: Reranking
-    dataset:
-      type: mteb/mind_small
-      name: MTEB MindSmallReranking
-      config: default
-      split: test
-      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
-    metrics:
-    - type: map
-      value: 32.20682480490871
-    - type: mrr
-      value: 33.41462721527003
-  - task:
-      type: Retrieval
-    dataset:
-      type: nfcorpus
-      name: MTEB NFCorpus
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 5.548
-    - type: map_at_10
-      value: 13.086999999999998
-    - type: map_at_100
-      value: 16.698
-    - type: map_at_1000
-      value: 18.151999999999997
-    - type: map_at_3
-      value: 9.576
-    - type: map_at_5
-      value: 11.175
-    - type: mrr_at_1
-      value: 44.272
-    - type: mrr_at_10
-      value: 53.635999999999996
-    - type: mrr_at_100
-      value: 54.228
-    - type: mrr_at_1000
-      value: 54.26499999999999
-    - type: mrr_at_3
-      value: 51.754
-    - type: mrr_at_5
-      value: 53.086
-    - type: ndcg_at_1
-      value: 42.724000000000004
-    - type: ndcg_at_10
-      value: 34.769
-    - type: ndcg_at_100
-      value: 32.283
-    - type: ndcg_at_1000
-      value: 40.843
-    - type: ndcg_at_3
-      value: 39.852
-    - type: ndcg_at_5
-      value: 37.858999999999995
-    - type: precision_at_1
-      value: 44.272
-    - type: precision_at_10
-      value: 26.068
-    - type: precision_at_100
-      value: 8.328000000000001
-    - type: precision_at_1000
-      value: 2.1
-    - type: precision_at_3
-      value: 37.874
-    - type: precision_at_5
-      value: 33.065
-    - type: recall_at_1
-      value: 5.548
-    - type: recall_at_10
-      value: 16.936999999999998
-    - type: recall_at_100
-      value: 33.72
-    - type: recall_at_1000
-      value: 64.348
-    - type: recall_at_3
-      value: 10.764999999999999
-    - type: recall_at_5
-      value: 13.361
-  - task:
-      type: Retrieval
-    dataset:
-      type: nq
-      name: MTEB NQ
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 28.008
-    - type: map_at_10
-      value: 42.675000000000004
-    - type: map_at_100
-      value: 43.85
-    - type: map_at_1000
-      value: 43.884
-    - type: map_at_3
-      value: 38.286
-    - type: map_at_5
-      value: 40.78
-    - type: mrr_at_1
-      value: 31.518
-    - type: mrr_at_10
-      value: 45.015
-    - type: mrr_at_100
-      value: 45.924
-    - type: mrr_at_1000
-      value: 45.946999999999996
-    - type: mrr_at_3
-      value: 41.348
-    - type: mrr_at_5
-      value: 43.428
-    - type: ndcg_at_1
-      value: 31.489
-    - type: ndcg_at_10
-      value: 50.285999999999994
-    - type: ndcg_at_100
-      value: 55.291999999999994
-    - type: ndcg_at_1000
-      value: 56.05
-    - type: ndcg_at_3
-      value: 41.976
-    - type: ndcg_at_5
-      value: 46.103
-    - type: precision_at_1
-      value: 31.489
-    - type: precision_at_10
-      value: 8.456
-    - type: precision_at_100
-      value: 1.125
-    - type: precision_at_1000
-      value: 0.12
-    - type: precision_at_3
-      value: 19.09
-    - type: precision_at_5
-      value: 13.841000000000001
-    - type: recall_at_1
-      value: 28.008
-    - type: recall_at_10
-      value: 71.21499999999999
-    - type: recall_at_100
-      value: 92.99
-    - type: recall_at_1000
-      value: 98.578
-    - type: recall_at_3
-      value: 49.604
-    - type: recall_at_5
-      value: 59.094
-  - task:
-      type: Retrieval
-    dataset:
-      type: quora
-      name: MTEB QuoraRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 70.351
-    - type: map_at_10
-      value: 84.163
-    - type: map_at_100
-      value: 84.785
-    - type: map_at_1000
-      value: 84.801
-    - type: map_at_3
-      value: 81.16
-    - type: map_at_5
-      value: 83.031
-    - type: mrr_at_1
-      value: 80.96
-    - type: mrr_at_10
-      value: 87.241
-    - type: mrr_at_100
-      value: 87.346
-    - type: mrr_at_1000
-      value: 87.347
-    - type: mrr_at_3
-      value: 86.25699999999999
-    - type: mrr_at_5
-      value: 86.907
-    - type: ndcg_at_1
-      value: 80.97
-    - type: ndcg_at_10
-      value: 88.017
-    - type: ndcg_at_100
-      value: 89.241
-    - type: ndcg_at_1000
-      value: 89.34299999999999
-    - type: ndcg_at_3
-      value: 85.053
-    - type: ndcg_at_5
-      value: 86.663
-    - type: precision_at_1
-      value: 80.97
-    - type: precision_at_10
-      value: 13.358
-    - type: precision_at_100
-      value: 1.525
-    - type: precision_at_1000
-      value: 0.157
-    - type: precision_at_3
-      value: 37.143
-    - type: precision_at_5
-      value: 24.451999999999998
-    - type: recall_at_1
-      value: 70.351
-    - type: recall_at_10
-      value: 95.39800000000001
-    - type: recall_at_100
-      value: 99.55199999999999
-    - type: recall_at_1000
-      value: 99.978
-    - type: recall_at_3
-      value: 86.913
-    - type: recall_at_5
-      value: 91.448
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/reddit-clustering
-      name: MTEB RedditClustering
-      config: default
-      split: test
-      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
-    metrics:
-    - type: v_measure
-      value: 55.62406719814139
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/reddit-clustering-p2p
-      name: MTEB RedditClusteringP2P
-      config: default
-      split: test
-      revision: 282350215ef01743dc01b456c7f5241fa8937f16
-    metrics:
-    - type: v_measure
-      value: 61.386700035141736
-  - task:
-      type: Retrieval
-    dataset:
-      type: scidocs
-      name: MTEB SCIDOCS
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 4.618
-    - type: map_at_10
-      value: 12.920000000000002
-    - type: map_at_100
-      value: 15.304
-    - type: map_at_1000
-      value: 15.656999999999998
-    - type: map_at_3
-      value: 9.187
-    - type: map_at_5
-      value: 10.937
-    - type: mrr_at_1
-      value: 22.8
-    - type: mrr_at_10
-      value: 35.13
-    - type: mrr_at_100
-      value: 36.239
-    - type: mrr_at_1000
-      value: 36.291000000000004
-    - type: mrr_at_3
-      value: 31.917
-    - type: mrr_at_5
-      value: 33.787
-    - type: ndcg_at_1
-      value: 22.8
-    - type: ndcg_at_10
-      value: 21.382
-    - type: ndcg_at_100
-      value: 30.257
-    - type: ndcg_at_1000
-      value: 36.001
-    - type: ndcg_at_3
-      value: 20.43
-    - type: ndcg_at_5
-      value: 17.622
-    - type: precision_at_1
-      value: 22.8
-    - type: precision_at_10
-      value: 11.26
-    - type: precision_at_100
-      value: 2.405
-    - type: precision_at_1000
-      value: 0.377
-    - type: precision_at_3
-      value: 19.633
-    - type: precision_at_5
-      value: 15.68
-    - type: recall_at_1
-      value: 4.618
-    - type: recall_at_10
-      value: 22.811999999999998
-    - type: recall_at_100
-      value: 48.787000000000006
-    - type: recall_at_1000
-      value: 76.63799999999999
-    - type: recall_at_3
-      value: 11.952
-    - type: recall_at_5
-      value: 15.892000000000001
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sickr-sts
-      name: MTEB SICK-R
-      config: default
-      split: test
-      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
-    metrics:
-    - type: cos_sim_pearson
-      value: 84.01529458252244
-    - type: cos_sim_spearman
-      value: 77.92985224770254
-    - type: euclidean_pearson
-      value: 81.04251429422487
-    - type: euclidean_spearman
-      value: 77.92838490549133
-    - type: manhattan_pearson
-      value: 80.95892251458979
-    - type: manhattan_spearman
-      value: 77.81028089705941
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts12-sts
-      name: MTEB STS12
-      config: default
-      split: test
-      revision: a0d554a64d88156834ff5ae9920b964011b16384
-    metrics:
-    - type: cos_sim_pearson
-      value: 83.97885282534388
-    - type: cos_sim_spearman
-      value: 75.1221970851712
-    - type: euclidean_pearson
-      value: 80.34455956720097
-    - type: euclidean_spearman
-      value: 74.5894274239938
-    - type: manhattan_pearson
-      value: 80.38999766325465
-    - type: manhattan_spearman
-      value: 74.68524557166975
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts13-sts
-      name: MTEB STS13
-      config: default
-      split: test
-      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
-    metrics:
-    - type: cos_sim_pearson
-      value: 82.95746064915672
-    - type: cos_sim_spearman
-      value: 85.08683458043946
-    - type: euclidean_pearson
-      value: 84.56699492836385
-    - type: euclidean_spearman
-      value: 85.66089116133713
-    - type: manhattan_pearson
-      value: 84.47553323458541
-    - type: manhattan_spearman
-      value: 85.56142206781472
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts14-sts
-      name: MTEB STS14
-      config: default
-      split: test
-      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
-    metrics:
-    - type: cos_sim_pearson
-      value: 82.71377893595067
-    - type: cos_sim_spearman
-      value: 81.03453291428589
-    - type: euclidean_pearson
-      value: 82.57136298308613
-    - type: euclidean_spearman
-      value: 81.15839961890875
-    - type: manhattan_pearson
-      value: 82.55157879373837
-    - type: manhattan_spearman
-      value: 81.1540163767054
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts15-sts
-      name: MTEB STS15
-      config: default
-      split: test
-      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
-    metrics:
-    - type: cos_sim_pearson
-      value: 86.64197832372373
-    - type: cos_sim_spearman
-      value: 88.31966852492485
-    - type: euclidean_pearson
-      value: 87.98692129976983
-    - type: euclidean_spearman
-      value: 88.6247340837856
-    - type: manhattan_pearson
-      value: 87.90437827826412
-    - type: manhattan_spearman
-      value: 88.56278787131457
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts16-sts
-      name: MTEB STS16
-      config: default
-      split: test
-      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
-    metrics:
-    - type: cos_sim_pearson
-      value: 81.84159950146693
-    - type: cos_sim_spearman
-      value: 83.90678384140168
-    - type: euclidean_pearson
-      value: 83.19005018860221
-    - type: euclidean_spearman
-      value: 84.16260415876295
-    - type: manhattan_pearson
-      value: 83.05030612994494
-    - type: manhattan_spearman
-      value: 83.99605629718336
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts17-crosslingual-sts
-      name: MTEB STS17 (en-en)
-      config: en-en
-      split: test
-      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
-    metrics:
-    - type: cos_sim_pearson
-      value: 87.49935350176666
-    - type: cos_sim_spearman
-      value: 87.59086606735383
-    - type: euclidean_pearson
-      value: 88.06537181129983
-    - type: euclidean_spearman
-      value: 87.6687448086014
-    - type: manhattan_pearson
-      value: 87.96599131972935
-    - type: manhattan_spearman
-      value: 87.63295748969642
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts22-crosslingual-sts
-      name: MTEB STS22 (en)
-      config: en
-      split: test
-      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
-    metrics:
-    - type: cos_sim_pearson
-      value: 67.68232799482763
-    - type: cos_sim_spearman
-      value: 67.99930378085793
-    - type: euclidean_pearson
-      value: 68.50275360001696
-    - type: euclidean_spearman
-      value: 67.81588179309259
-    - type: manhattan_pearson
-      value: 68.5892154749763
-    - type: manhattan_spearman
-      value: 67.84357259640682
-  - task:
-      type: STS
-    dataset:
-      type: mteb/stsbenchmark-sts
-      name: MTEB STSBenchmark
-      config: default
-      split: test
-      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
-    metrics:
-    - type: cos_sim_pearson
-      value: 84.37049618406554
-    - type: cos_sim_spearman
-      value: 85.57014313159492
-    - type: euclidean_pearson
-      value: 85.57469513908282
-    - type: euclidean_spearman
-      value: 85.661948135258
-    - type: manhattan_pearson
-      value: 85.36866831229028
-    - type: manhattan_spearman
-      value: 85.5043455368843
-  - task:
-      type: Reranking
-    dataset:
-      type: mteb/scidocs-reranking
-      name: MTEB SciDocsRR
-      config: default
-      split: test
-      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
-    metrics:
-    - type: map
-      value: 84.83259065376154
-    - type: mrr
-      value: 95.58455433455433
-  - task:
-      type: Retrieval
-    dataset:
-      type: scifact
-      name: MTEB SciFact
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 58.817
-    - type: map_at_10
-      value: 68.459
-    - type: map_at_100
-      value: 68.951
-    - type: map_at_1000
-      value: 68.979
-    - type: map_at_3
-      value: 65.791
-    - type: map_at_5
-      value: 67.583
-    - type: mrr_at_1
-      value: 61.667
-    - type: mrr_at_10
-      value: 69.368
-    - type: mrr_at_100
-      value: 69.721
-    - type: mrr_at_1000
-      value: 69.744
-    - type: mrr_at_3
-      value: 67.278
-    - type: mrr_at_5
-      value: 68.611
-    - type: ndcg_at_1
-      value: 61.667
-    - type: ndcg_at_10
-      value: 72.70100000000001
-    - type: ndcg_at_100
-      value: 74.928
-    - type: ndcg_at_1000
-      value: 75.553
-    - type: ndcg_at_3
-      value: 68.203
-    - type: ndcg_at_5
-      value: 70.804
-    - type: precision_at_1
-      value: 61.667
-    - type: precision_at_10
-      value: 9.533
-    - type: precision_at_100
-      value: 1.077
-    - type: precision_at_1000
-      value: 0.11299999999999999
-    - type: precision_at_3
-      value: 26.444000000000003
-    - type: precision_at_5
-      value: 17.599999999999998
-    - type: recall_at_1
-      value: 58.817
-    - type: recall_at_10
-      value: 84.789
-    - type: recall_at_100
-      value: 95.0
-    - type: recall_at_1000
-      value: 99.667
-    - type: recall_at_3
-      value: 72.8
-    - type: recall_at_5
-      value: 79.294
-  - task:
-      type: PairClassification
-    dataset:
-      type: mteb/sprintduplicatequestions-pairclassification
-      name: MTEB SprintDuplicateQuestions
-      config: default
-      split: test
-      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
-    metrics:
-    - type: cos_sim_accuracy
-      value: 99.8108910891089
-    - type: cos_sim_ap
-      value: 95.5743678558349
-    - type: cos_sim_f1
-      value: 90.43133366385722
-    - type: cos_sim_precision
-      value: 89.67551622418878
-    - type: cos_sim_recall
-      value: 91.2
-    - type: dot_accuracy
-      value: 99.75841584158415
-    - type: dot_ap
-      value: 94.00786363627253
-    - type: dot_f1
-      value: 87.51910341314316
-    - type: dot_precision
-      value: 89.20041536863967
-    - type: dot_recall
-      value: 85.9
-    - type: euclidean_accuracy
-      value: 99.81485148514851
-    - type: euclidean_ap
-      value: 95.4752113136905
-    - type: euclidean_f1
-      value: 90.44334975369456
-    - type: euclidean_precision
-      value: 89.126213592233
-    - type: euclidean_recall
-      value: 91.8
-    - type: manhattan_accuracy
-      value: 99.81584158415842
-    - type: manhattan_ap
-      value: 95.5163172682464
-    - type: manhattan_f1
-      value: 90.51987767584097
-    - type: manhattan_precision
-      value: 92.3076923076923
-    - type: manhattan_recall
-      value: 88.8
-    - type: max_accuracy
-      value: 99.81584158415842
-    - type: max_ap
-      value: 95.5743678558349
-    - type: max_f1
-      value: 90.51987767584097
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/stackexchange-clustering
-      name: MTEB StackExchangeClustering
-      config: default
-      split: test
-      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
-    metrics:
-    - type: v_measure
-      value: 62.63235986949449
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/stackexchange-clustering-p2p
-      name: MTEB StackExchangeClusteringP2P
-      config: default
-      split: test
-      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
-    metrics:
-    - type: v_measure
-      value: 36.334795589585575
-  - task:
-      type: Reranking
-    dataset:
-      type: mteb/stackoverflowdupquestions-reranking
-      name: MTEB StackOverflowDupQuestions
-      config: default
-      split: test
-      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
-    metrics:
-    - type: map
-      value: 52.02955214518782
-    - type: mrr
-      value: 52.8004838298956
-  - task:
-      type: Summarization
-    dataset:
-      type: mteb/summeval
-      name: MTEB SummEval
-      config: default
-      split: test
-      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
-    metrics:
-    - type: cos_sim_pearson
-      value: 30.63769566275453
-    - type: cos_sim_spearman
-      value: 30.422379185989335
-    - type: dot_pearson
-      value: 26.88493071882256
-    - type: dot_spearman
-      value: 26.505249740971305
-  - task:
-      type: Retrieval
-    dataset:
-      type: trec-covid
-      name: MTEB TRECCOVID
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 0.21
-    - type: map_at_10
-      value: 1.654
-    - type: map_at_100
-      value: 10.095
-    - type: map_at_1000
-      value: 25.808999999999997
-    - type: map_at_3
-      value: 0.594
-    - type: map_at_5
-      value: 0.9289999999999999
-    - type: mrr_at_1
-      value: 78.0
-    - type: mrr_at_10
-      value: 87.019
-    - type: mrr_at_100
-      value: 87.019
-    - type: mrr_at_1000
-      value: 87.019
-    - type: mrr_at_3
-      value: 86.333
-    - type: mrr_at_5
-      value: 86.733
-    - type: ndcg_at_1
-      value: 73.0
-    - type: ndcg_at_10
-      value: 66.52900000000001
-    - type: ndcg_at_100
-      value: 53.433
-    - type: ndcg_at_1000
-      value: 51.324000000000005
-    - type: ndcg_at_3
-      value: 72.02199999999999
-    - type: ndcg_at_5
-      value: 69.696
-    - type: precision_at_1
-      value: 78.0
-    - type: precision_at_10
-      value: 70.39999999999999
-    - type: precision_at_100
-      value: 55.46
-    - type: precision_at_1000
-      value: 22.758
-    - type: precision_at_3
-      value: 76.667
-    - type: precision_at_5
-      value: 74.0
-    - type: recall_at_1
-      value: 0.21
-    - type: recall_at_10
-      value: 1.8849999999999998
-    - type: recall_at_100
-      value: 13.801
-    - type: recall_at_1000
-      value: 49.649
-    - type: recall_at_3
-      value: 0.632
-    - type: recall_at_5
-      value: 1.009
-  - task:
-      type: Retrieval
-    dataset:
-      type: webis-touche2020
-      name: MTEB Touche2020
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 1.797
-    - type: map_at_10
-      value: 9.01
-    - type: map_at_100
-      value: 14.682
-    - type: map_at_1000
-      value: 16.336000000000002
-    - type: map_at_3
-      value: 4.546
-    - type: map_at_5
-      value: 5.9270000000000005
-    - type: mrr_at_1
-      value: 24.490000000000002
-    - type: mrr_at_10
-      value: 41.156
-    - type: mrr_at_100
-      value: 42.392
-    - type: mrr_at_1000
-      value: 42.408
-    - type: mrr_at_3
-      value: 38.775999999999996
-    - type: mrr_at_5
-      value: 40.102
-    - type: ndcg_at_1
-      value: 21.429000000000002
-    - type: ndcg_at_10
-      value: 22.222
-    - type: ndcg_at_100
-      value: 34.405
-    - type: ndcg_at_1000
-      value: 46.599000000000004
-    - type: ndcg_at_3
-      value: 25.261
-    - type: ndcg_at_5
-      value: 22.695999999999998
-    - type: precision_at_1
-      value: 24.490000000000002
-    - type: precision_at_10
-      value: 19.796
-    - type: precision_at_100
-      value: 7.306
-    - type: precision_at_1000
-      value: 1.5350000000000001
-    - type: precision_at_3
-      value: 27.211000000000002
-    - type: precision_at_5
-      value: 22.857
-    - type: recall_at_1
-      value: 1.797
-    - type: recall_at_10
-      value: 15.706000000000001
-    - type: recall_at_100
-      value: 46.412
-    - type: recall_at_1000
-      value: 83.159
-    - type: recall_at_3
-      value: 6.1370000000000005
-    - type: recall_at_5
-      value: 8.599
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/toxic_conversations_50k
-      name: MTEB ToxicConversationsClassification
-      config: default
-      split: test
-      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
-    metrics:
-    - type: accuracy
-      value: 70.3302
-    - type: ap
-      value: 14.169121204575601
-    - type: f1
-      value: 54.229345975274235
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/tweet_sentiment_extraction
-      name: MTEB TweetSentimentExtractionClassification
-      config: default
-      split: test
-      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
-    metrics:
-    - type: accuracy
-      value: 58.22297679683077
-    - type: f1
-      value: 58.62984908377875
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/twentynewsgroups-clustering
-      name: MTEB TwentyNewsgroupsClustering
-      config: default
-      split: test
-      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
-    metrics:
-    - type: v_measure
-      value: 49.952922428464255
-  - task:
-      type: PairClassification
-    dataset:
-      type: mteb/twittersemeval2015-pairclassification
-      name: MTEB TwitterSemEval2015
-      config: default
-      split: test
-      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
-    metrics:
-    - type: cos_sim_accuracy
-      value: 84.68140907194373
-    - type: cos_sim_ap
-      value: 70.12180123666836
-    - type: cos_sim_f1
-      value: 65.77501791258658
-    - type: cos_sim_precision
-      value: 60.07853403141361
-    - type: cos_sim_recall
-      value: 72.66490765171504
-    - type: dot_accuracy
-      value: 81.92167848840674
-    - type: dot_ap
-      value: 60.49837581423469
-    - type: dot_f1
-      value: 58.44186046511628
-    - type: dot_precision
-      value: 52.24532224532224
-    - type: dot_recall
-      value: 66.3060686015831
-    - type: euclidean_accuracy
-      value: 84.73505394289802
-    - type: euclidean_ap
-      value: 70.3278904593286
-    - type: euclidean_f1
-      value: 65.98851124940161
-    - type: euclidean_precision
-      value: 60.38107752956636
-    - type: euclidean_recall
-      value: 72.74406332453826
-    - type: manhattan_accuracy
-      value: 84.73505394289802
-    - type: manhattan_ap
-      value: 70.00737738537337
-    - type: manhattan_f1
-      value: 65.80150784822642
-    - type: manhattan_precision
-      value: 61.892583120204606
-    - type: manhattan_recall
-      value: 70.23746701846966
-    - type: max_accuracy
-      value: 84.73505394289802
-    - type: max_ap
-      value: 70.3278904593286
-    - type: max_f1
-      value: 65.98851124940161
-  - task:
-      type: PairClassification
-    dataset:
-      type: mteb/twitterurlcorpus-pairclassification
-      name: MTEB TwitterURLCorpus
-      config: default
-      split: test
-      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
-    metrics:
-    - type: cos_sim_accuracy
-      value: 88.44258159661582
-    - type: cos_sim_ap
-      value: 84.91926704880888
-    - type: cos_sim_f1
-      value: 77.07651086632926
-    - type: cos_sim_precision
-      value: 74.5894554883319
-    - type: cos_sim_recall
-      value: 79.73514012935017
-    - type: dot_accuracy
-      value: 85.88116583226608
-    - type: dot_ap
-      value: 78.9753854779923
-    - type: dot_f1
-      value: 72.17757637979255
-    - type: dot_precision
-      value: 66.80647486729143
-    - type: dot_recall
-      value: 78.48783492454572
-    - type: euclidean_accuracy
-      value: 88.5299025885823
-    - type: euclidean_ap
-      value: 85.08006075642194
-    - type: euclidean_f1
-      value: 77.29637336504163
-    - type: euclidean_precision
-      value: 74.69836253950014
-    - type: euclidean_recall
-      value: 80.08161379735141
-    - type: manhattan_accuracy
-      value: 88.55124771995187
-    - type: manhattan_ap
-      value: 85.00941529932851
-    - type: manhattan_f1
-      value: 77.33100233100232
-    - type: manhattan_precision
-      value: 73.37572573956317
-    - type: manhattan_recall
-      value: 81.73698798891284
-    - type: max_accuracy
-      value: 88.55124771995187
-    - type: max_ap
-      value: 85.08006075642194
-    - type: max_f1
-      value: 77.33100233100232
+  - Transformers.js
+  - feature extraction
+pipeline_tag: feature-extraction
+library_name: "transformers.js"
 language:
-- en
+  - en
 license: mit
 ---
 
-Forked from [thenlper/gte-small](huggingface.co/thenlper/gte-small) with ONNX to work with Transformers.js.
+Fork of [thenlper/gte-small](huggingface.co/thenlper/gte-small) with ONNX to work with Transformers.js.
 
 # gte-small
 
@@ -2638,50 +43,68 @@ We compared the performance of the GTE models with other popular text embedding
 
 ## Usage
 
-Code example
+### Node JS
 
-```python
-import torch.nn.functional as F
-from torch import Tensor
-from transformers import AutoTokenizer, AutoModel
+first install transformers.js using `npm i @xenova/transformers`.
 
-def average_pool(last_hidden_states: Tensor,
-                 attention_mask: Tensor) -> Tensor:
-    last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
-    return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
+```javascript
+import { env, pipeline } from '@xenova/transformers'
 
-input_texts = [
-    "what is the capital of China?",
-    "how to implement quick sort in python?",
-    "Beijing",
-    "sorting algorithms"
-]
+(async () => {
+  // Give it any input you want
+  const input = "Hello AI";
 
-tokenizer = AutoTokenizer.from_pretrained("thenlper/gte-small")
-model = AutoModel.from_pretrained("thenlper/gte-small")
+  // Create the pipeline
+  const pipe = await pipeline(
+    "feature-extraction",
+    "koxy-ai/gte-small"
+  );
 
-# Tokenize the input texts
-batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')
+  // Generate the embedding
+  const output = await pipe(input, {
+    pooling: "mean",
+    normalize: true
+  });
 
-outputs = model(**batch_dict)
-embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
+  // Extract the embedding from the output
+  const embedding = Array.from(output.data);
 
-# (Optionally) normalize embeddings
-embeddings = F.normalize(embeddings, p=2, dim=1)
-scores = (embeddings[:1] @ embeddings[1:].T) * 100
-print(scores.tolist())
+  // Do anything with the embedding
+  console.log(embedding);
+});
 ```
 
-Use with sentence-transformers:
-```python
-from sentence_transformers import SentenceTransformer
-from sentence_transformers.util import cos_sim
+### Deno
 
-sentences = ['That is a happy person', 'That is a very happy person']
+No need to install anything.
 
-model = SentenceTransformer('thenlper/gte-large')
-embeddings = model.encode(sentences)
-print(cos_sim(embeddings[0], embeddings[1]))
+```javascript
+import { env, pipeline } from 'https://cdn.jsdelivr.net/npm/@xenova/[email protected]'
+
+// Some config for Deno
+env.useBrowserCache = false;
+env.allowLocalModels = false;
+
+// Give it any input you want
+const input = "Hello AI";
+
+// Create the pipeline
+const pipe = await pipeline(
+  "feature-extraction",
+  "koxy-ai/gte-small"
+);
+
+// Generate the embedding
+const output = await pipe(input, {
+  pooling: "mean",
+  normalize: true
+});
+
+// Extract the embedding from the output
+const embedding = Array.from(output.data);
+
+// Do anything with the embedding
+console.log(embedding);
 ```
 
 ### Limitation
@@ -2690,8 +113,6 @@ This model exclusively caters to English texts, and any lengthy texts will be tr
 
 ### Citation
 
-If you find our paper or models helpful, please consider citing them as follows:
-
 ```
 @misc{li2023general,
       title={Towards General Text Embeddings with Multi-stage Contrastive Learning}, 
@@ -2701,4 +122,4 @@ If you find our paper or models helpful, please consider citing them as follows:
       archivePrefix={arXiv},
       primaryClass={cs.CL}
 }
-```
+```