Update README.md

README.md

@@ -2799,4 +2799,3020 @@ model-index:
     metrics:
     - type: v_measure
       value: 79.58576208710117
----
+---
+---
+tags:
+- mteb
+- arctic
+- arctic-embed
+model-index:
+- name: base
+  results:
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_counterfactual
+      name: MTEB AmazonCounterfactualClassification (en)
+      config: en
+      split: test
+      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
+    metrics:
+    - type: accuracy
+      value: 76.80597014925374
+    - type: ap
+      value: 39.31198155789558
+    - type: f1
+      value: 70.48198448222148
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_polarity
+      name: MTEB AmazonPolarityClassification
+      config: default
+      split: test
+      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
+    metrics:
+    - type: accuracy
+      value: 82.831525
+    - type: ap
+      value: 77.4474050181638
+    - type: f1
+      value: 82.77204845110204
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_reviews_multi
+      name: MTEB AmazonReviewsClassification (en)
+      config: en
+      split: test
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+    metrics:
+    - type: accuracy
+      value: 38.93000000000001
+    - type: f1
+      value: 37.98013371053459
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/arguana
+      name: MTEB ArguAna
+      config: default
+      split: test
+      revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
+    metrics:
+    - type: map_at_1
+      value: 31.223
+    - type: map_at_10
+      value: 47.43
+    - type: map_at_100
+      value: 48.208
+    - type: map_at_1000
+      value: 48.211
+    - type: map_at_3
+      value: 42.579
+    - type: map_at_5
+      value: 45.263999999999996
+    - type: mrr_at_1
+      value: 31.65
+    - type: mrr_at_10
+      value: 47.573
+    - type: mrr_at_100
+      value: 48.359
+    - type: mrr_at_1000
+      value: 48.362
+    - type: mrr_at_3
+      value: 42.734
+    - type: mrr_at_5
+      value: 45.415
+    - type: ndcg_at_1
+      value: 31.223
+    - type: ndcg_at_10
+      value: 56.436
+    - type: ndcg_at_100
+      value: 59.657000000000004
+    - type: ndcg_at_1000
+      value: 59.731
+    - type: ndcg_at_3
+      value: 46.327
+    - type: ndcg_at_5
+      value: 51.178000000000004
+    - type: precision_at_1
+      value: 31.223
+    - type: precision_at_10
+      value: 8.527999999999999
+    - type: precision_at_100
+      value: 0.991
+    - type: precision_at_1000
+      value: 0.1
+    - type: precision_at_3
+      value: 19.061
+    - type: precision_at_5
+      value: 13.797999999999998
+    - type: recall_at_1
+      value: 31.223
+    - type: recall_at_10
+      value: 85.277
+    - type: recall_at_100
+      value: 99.075
+    - type: recall_at_1000
+      value: 99.644
+    - type: recall_at_3
+      value: 57.18299999999999
+    - type: recall_at_5
+      value: 68.99
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-p2p
+      name: MTEB ArxivClusteringP2P
+      config: default
+      split: test
+      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
+    metrics:
+    - type: v_measure
+      value: 47.23625429411296
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-s2s
+      name: MTEB ArxivClusteringS2S
+      config: default
+      split: test
+      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
+    metrics:
+    - type: v_measure
+      value: 37.433880471403654
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/askubuntudupquestions-reranking
+      name: MTEB AskUbuntuDupQuestions
+      config: default
+      split: test
+      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
+    metrics:
+    - type: map
+      value: 60.53175025582013
+    - type: mrr
+      value: 74.51160796728664
+  - task:
+      type: STS
+    dataset:
+      type: mteb/biosses-sts
+      name: MTEB BIOSSES
+      config: default
+      split: test
+      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
+    metrics:
+    - type: cos_sim_pearson
+      value: 88.93746103286769
+    - type: cos_sim_spearman
+      value: 86.62245567912619
+    - type: euclidean_pearson
+      value: 87.154173907501
+    - type: euclidean_spearman
+      value: 86.62245567912619
+    - type: manhattan_pearson
+      value: 87.17682026633462
+    - type: manhattan_spearman
+      value: 86.74775973908348
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/banking77
+      name: MTEB Banking77Classification
+      config: default
+      split: test
+      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
+    metrics:
+    - type: accuracy
+      value: 80.33766233766232
+    - type: f1
+      value: 79.64931422442245
+  - task:
+      type: Clustering
+    dataset:
+      type: jinaai/big-patent-clustering
+      name: MTEB BigPatentClustering
+      config: default
+      split: test
+      revision: 62d5330920bca426ce9d3c76ea914f15fc83e891
+    metrics:
+    - type: v_measure
+      value: 19.116028913890613
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-p2p
+      name: MTEB BiorxivClusteringP2P
+      config: default
+      split: test
+      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
+    metrics:
+    - type: v_measure
+      value: 36.966921852810174
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-s2s
+      name: MTEB BiorxivClusteringS2S
+      config: default
+      split: test
+      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
+    metrics:
+    - type: v_measure
+      value: 31.98019698537654
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/cqadupstack-android
+      name: MTEB CQADupstackAndroidRetrieval
+      config: default
+      split: test
+      revision: f46a197baaae43b4f621051089b82a364682dfeb
+    metrics:
+    - type: map_at_1
+      value: 34.079
+    - type: map_at_10
+      value: 46.35
+    - type: map_at_100
+      value: 47.785
+    - type: map_at_1000
+      value: 47.903
+    - type: map_at_3
+      value: 42.620999999999995
+    - type: map_at_5
+      value: 44.765
+    - type: mrr_at_1
+      value: 41.345
+    - type: mrr_at_10
+      value: 52.032000000000004
+    - type: mrr_at_100
+      value: 52.690000000000005
+    - type: mrr_at_1000
+      value: 52.727999999999994
+    - type: mrr_at_3
+      value: 49.428
+    - type: mrr_at_5
+      value: 51.093999999999994
+    - type: ndcg_at_1
+      value: 41.345
+    - type: ndcg_at_10
+      value: 53.027
+    - type: ndcg_at_100
+      value: 57.962
+    - type: ndcg_at_1000
+      value: 59.611999999999995
+    - type: ndcg_at_3
+      value: 47.687000000000005
+    - type: ndcg_at_5
+      value: 50.367
+    - type: precision_at_1
+      value: 41.345
+    - type: precision_at_10
+      value: 10.157
+    - type: precision_at_100
+      value: 1.567
+    - type: precision_at_1000
+      value: 0.199
+    - type: precision_at_3
+      value: 23.081
+    - type: precision_at_5
+      value: 16.738
+    - type: recall_at_1
+      value: 34.079
+    - type: recall_at_10
+      value: 65.93900000000001
+    - type: recall_at_100
+      value: 86.42699999999999
+    - type: recall_at_1000
+      value: 96.61
+    - type: recall_at_3
+      value: 50.56699999999999
+    - type: recall_at_5
+      value: 57.82000000000001
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/cqadupstack-english
+      name: MTEB CQADupstackEnglishRetrieval
+      config: default
+      split: test
+      revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
+    metrics:
+    - type: map_at_1
+      value: 33.289
+    - type: map_at_10
+      value: 43.681
+    - type: map_at_100
+      value: 45.056000000000004
+    - type: map_at_1000
+      value: 45.171
+    - type: map_at_3
+      value: 40.702
+    - type: map_at_5
+      value: 42.292
+    - type: mrr_at_1
+      value: 41.146
+    - type: mrr_at_10
+      value: 49.604
+    - type: mrr_at_100
+      value: 50.28399999999999
+    - type: mrr_at_1000
+      value: 50.322
+    - type: mrr_at_3
+      value: 47.611
+    - type: mrr_at_5
+      value: 48.717
+    - type: ndcg_at_1
+      value: 41.146
+    - type: ndcg_at_10
+      value: 49.43
+    - type: ndcg_at_100
+      value: 54.01899999999999
+    - type: ndcg_at_1000
+      value: 55.803000000000004
+    - type: ndcg_at_3
+      value: 45.503
+    - type: ndcg_at_5
+      value: 47.198
+    - type: precision_at_1
+      value: 41.146
+    - type: precision_at_10
+      value: 9.268
+    - type: precision_at_100
+      value: 1.4749999999999999
+    - type: precision_at_1000
+      value: 0.19
+    - type: precision_at_3
+      value: 21.932
+    - type: precision_at_5
+      value: 15.389
+    - type: recall_at_1
+      value: 33.289
+    - type: recall_at_10
+      value: 59.209999999999994
+    - type: recall_at_100
+      value: 78.676
+    - type: recall_at_1000
+      value: 89.84100000000001
+    - type: recall_at_3
+      value: 47.351
+    - type: recall_at_5
+      value: 52.178999999999995
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/cqadupstack-gaming
+      name: MTEB CQADupstackGamingRetrieval
+      config: default
+      split: test
+      revision: 4885aa143210c98657558c04aaf3dc47cfb54340
+    metrics:
+    - type: map_at_1
+      value: 44.483
+    - type: map_at_10
+      value: 56.862
+    - type: map_at_100
+      value: 57.901
+    - type: map_at_1000
+      value: 57.948
+    - type: map_at_3
+      value: 53.737
+    - type: map_at_5
+      value: 55.64
+    - type: mrr_at_1
+      value: 50.658
+    - type: mrr_at_10
+      value: 60.281
+    - type: mrr_at_100
+      value: 60.946
+    - type: mrr_at_1000
+      value: 60.967000000000006
+    - type: mrr_at_3
+      value: 58.192
+    - type: mrr_at_5
+      value: 59.531
+    - type: ndcg_at_1
+      value: 50.658
+    - type: ndcg_at_10
+      value: 62.339
+    - type: ndcg_at_100
+      value: 66.28399999999999
+    - type: ndcg_at_1000
+      value: 67.166
+    - type: ndcg_at_3
+      value: 57.458
+    - type: ndcg_at_5
+      value: 60.112
+    - type: precision_at_1
+      value: 50.658
+    - type: precision_at_10
+      value: 9.762
+    - type: precision_at_100
+      value: 1.26
+    - type: precision_at_1000
+      value: 0.13799999999999998
+    - type: precision_at_3
+      value: 25.329
+    - type: precision_at_5
+      value: 17.254
+    - type: recall_at_1
+      value: 44.483
+    - type: recall_at_10
+      value: 74.819
+    - type: recall_at_100
+      value: 91.702
+    - type: recall_at_1000
+      value: 97.84
+    - type: recall_at_3
+      value: 62.13999999999999
+    - type: recall_at_5
+      value: 68.569
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/cqadupstack-gis
+      name: MTEB CQADupstackGisRetrieval
+      config: default
+      split: test
+      revision: 5003b3064772da1887988e05400cf3806fe491f2
+    metrics:
+    - type: map_at_1
+      value: 26.489
+    - type: map_at_10
+      value: 37.004999999999995
+    - type: map_at_100
+      value: 38.001000000000005
+    - type: map_at_1000
+      value: 38.085
+    - type: map_at_3
+      value: 34.239999999999995
+    - type: map_at_5
+      value: 35.934
+    - type: mrr_at_1
+      value: 28.362
+    - type: mrr_at_10
+      value: 38.807
+    - type: mrr_at_100
+      value: 39.671
+    - type: mrr_at_1000
+      value: 39.736
+    - type: mrr_at_3
+      value: 36.29
+    - type: mrr_at_5
+      value: 37.906
+    - type: ndcg_at_1
+      value: 28.362
+    - type: ndcg_at_10
+      value: 42.510999999999996
+    - type: ndcg_at_100
+      value: 47.226
+    - type: ndcg_at_1000
+      value: 49.226
+    - type: ndcg_at_3
+      value: 37.295
+    - type: ndcg_at_5
+      value: 40.165
+    - type: precision_at_1
+      value: 28.362
+    - type: precision_at_10
+      value: 6.633
+    - type: precision_at_100
+      value: 0.9490000000000001
+    - type: precision_at_1000
+      value: 0.11499999999999999
+    - type: precision_at_3
+      value: 16.234
+    - type: precision_at_5
+      value: 11.434999999999999
+    - type: recall_at_1
+      value: 26.489
+    - type: recall_at_10
+      value: 57.457
+    - type: recall_at_100
+      value: 78.712
+    - type: recall_at_1000
+      value: 93.565
+    - type: recall_at_3
+      value: 43.748
+    - type: recall_at_5
+      value: 50.589
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/cqadupstack-mathematica
+      name: MTEB CQADupstackMathematicaRetrieval
+      config: default
+      split: test
+      revision: 90fceea13679c63fe563ded68f3b6f06e50061de
+    metrics:
+    - type: map_at_1
+      value: 12.418999999999999
+    - type: map_at_10
+      value: 22.866
+    - type: map_at_100
+      value: 24.365000000000002
+    - type: map_at_1000
+      value: 24.479
+    - type: map_at_3
+      value: 19.965
+    - type: map_at_5
+      value: 21.684
+    - type: mrr_at_1
+      value: 14.677000000000001
+    - type: mrr_at_10
+      value: 26.316
+    - type: mrr_at_100
+      value: 27.514
+    - type: mrr_at_1000
+      value: 27.57
+    - type: mrr_at_3
+      value: 23.3
+    - type: mrr_at_5
+      value: 25.191000000000003
+    - type: ndcg_at_1
+      value: 14.677000000000001
+    - type: ndcg_at_10
+      value: 28.875
+    - type: ndcg_at_100
+      value: 35.607
+    - type: ndcg_at_1000
+      value: 38.237
+    - type: ndcg_at_3
+      value: 23.284
+    - type: ndcg_at_5
+      value: 26.226
+    - type: precision_at_1
+      value: 14.677000000000001
+    - type: precision_at_10
+      value: 5.771
+    - type: precision_at_100
+      value: 1.058
+    - type: precision_at_1000
+      value: 0.14200000000000002
+    - type: precision_at_3
+      value: 11.940000000000001
+    - type: precision_at_5
+      value: 9.229
+    - type: recall_at_1
+      value: 12.418999999999999
+    - type: recall_at_10
+      value: 43.333
+    - type: recall_at_100
+      value: 71.942
+    - type: recall_at_1000
+      value: 90.67399999999999
+    - type: recall_at_3
+      value: 28.787000000000003
+    - type: recall_at_5
+      value: 35.638
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/cqadupstack-physics
+      name: MTEB CQADupstackPhysicsRetrieval
+      config: default
+      split: test
+      revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
+    metrics:
+    - type: map_at_1
+      value: 31.686999999999998
+    - type: map_at_10
+      value: 42.331
+    - type: map_at_100
+      value: 43.655
+    - type: map_at_1000
+      value: 43.771
+    - type: map_at_3
+      value: 38.944
+    - type: map_at_5
+      value: 40.991
+    - type: mrr_at_1
+      value: 37.921
+    - type: mrr_at_10
+      value: 47.534
+    - type: mrr_at_100
+      value: 48.362
+    - type: mrr_at_1000
+      value: 48.405
+    - type: mrr_at_3
+      value: 44.995000000000005
+    - type: mrr_at_5
+      value: 46.617
+    - type: ndcg_at_1
+      value: 37.921
+    - type: ndcg_at_10
+      value: 48.236000000000004
+    - type: ndcg_at_100
+      value: 53.705000000000005
+    - type: ndcg_at_1000
+      value: 55.596000000000004
+    - type: ndcg_at_3
+      value: 43.11
+    - type: ndcg_at_5
+      value: 45.862
+    - type: precision_at_1
+      value: 37.921
+    - type: precision_at_10
+      value: 8.643
+    - type: precision_at_100
+      value: 1.336
+    - type: precision_at_1000
+      value: 0.166
+    - type: precision_at_3
+      value: 20.308
+    - type: precision_at_5
+      value: 14.514
+    - type: recall_at_1
+      value: 31.686999999999998
+    - type: recall_at_10
+      value: 60.126999999999995
+    - type: recall_at_100
+      value: 83.10600000000001
+    - type: recall_at_1000
+      value: 95.15
+    - type: recall_at_3
+      value: 46.098
+    - type: recall_at_5
+      value: 53.179
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/cqadupstack-programmers
+      name: MTEB CQADupstackProgrammersRetrieval
+      config: default
+      split: test
+      revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
+    metrics:
+    - type: map_at_1
+      value: 28.686
+    - type: map_at_10
+      value: 39.146
+    - type: map_at_100
+      value: 40.543
+    - type: map_at_1000
+      value: 40.644999999999996
+    - type: map_at_3
+      value: 36.195
+    - type: map_at_5
+      value: 37.919000000000004
+    - type: mrr_at_1
+      value: 35.160000000000004
+    - type: mrr_at_10
+      value: 44.711
+    - type: mrr_at_100
+      value: 45.609
+    - type: mrr_at_1000
+      value: 45.655
+    - type: mrr_at_3
+      value: 42.409
+    - type: mrr_at_5
+      value: 43.779
+    - type: ndcg_at_1
+      value: 35.160000000000004
+    - type: ndcg_at_10
+      value: 44.977000000000004
+    - type: ndcg_at_100
+      value: 50.663000000000004
+    - type: ndcg_at_1000
+      value: 52.794
+    - type: ndcg_at_3
+      value: 40.532000000000004
+    - type: ndcg_at_5
+      value: 42.641
+    - type: precision_at_1
+      value: 35.160000000000004
+    - type: precision_at_10
+      value: 8.014000000000001
+    - type: precision_at_100
+      value: 1.269
+    - type: precision_at_1000
+      value: 0.163
+    - type: precision_at_3
+      value: 19.444
+    - type: precision_at_5
+      value: 13.653
+    - type: recall_at_1
+      value: 28.686
+    - type: recall_at_10
+      value: 56.801
+    - type: recall_at_100
+      value: 80.559
+    - type: recall_at_1000
+      value: 95.052
+    - type: recall_at_3
+      value: 43.675999999999995
+    - type: recall_at_5
+      value: 49.703
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/cqadupstack
+      name: MTEB CQADupstackRetrieval
+      config: default
+      split: test
+      revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
+    metrics:
+    - type: map_at_1
+      value: 28.173833333333338
+    - type: map_at_10
+      value: 38.202083333333334
+    - type: map_at_100
+      value: 39.47475
+    - type: map_at_1000
+      value: 39.586499999999994
+    - type: map_at_3
+      value: 35.17308333333334
+    - type: map_at_5
+      value: 36.914
+    - type: mrr_at_1
+      value: 32.92958333333333
+    - type: mrr_at_10
+      value: 42.16758333333333
+    - type: mrr_at_100
+      value: 43.04108333333333
+    - type: mrr_at_1000
+      value: 43.092499999999994
+    - type: mrr_at_3
+      value: 39.69166666666666
+    - type: mrr_at_5
+      value: 41.19458333333333
+    - type: ndcg_at_1
+      value: 32.92958333333333
+    - type: ndcg_at_10
+      value: 43.80583333333333
+    - type: ndcg_at_100
+      value: 49.060916666666664
+    - type: ndcg_at_1000
+      value: 51.127250000000004
+    - type: ndcg_at_3
+      value: 38.80383333333333
+    - type: ndcg_at_5
+      value: 41.29658333333333
+    - type: precision_at_1
+      value: 32.92958333333333
+    - type: precision_at_10
+      value: 7.655666666666666
+    - type: precision_at_100
+      value: 1.2094166666666668
+    - type: precision_at_1000
+      value: 0.15750000000000003
+    - type: precision_at_3
+      value: 17.87975
+    - type: precision_at_5
+      value: 12.741833333333332
+    - type: recall_at_1
+      value: 28.173833333333338
+    - type: recall_at_10
+      value: 56.219249999999995
+    - type: recall_at_100
+      value: 79.01416666666665
+    - type: recall_at_1000
+      value: 93.13425000000001
+    - type: recall_at_3
+      value: 42.39241666666667
+    - type: recall_at_5
+      value: 48.764833333333335
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/cqadupstack-stats
+      name: MTEB CQADupstackStatsRetrieval
+      config: default
+      split: test
+      revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
+    metrics:
+    - type: map_at_1
+      value: 25.625999999999998
+    - type: map_at_10
+      value: 32.808
+    - type: map_at_100
+      value: 33.951
+    - type: map_at_1000
+      value: 34.052
+    - type: map_at_3
+      value: 30.536
+    - type: map_at_5
+      value: 31.77
+    - type: mrr_at_1
+      value: 28.374
+    - type: mrr_at_10
+      value: 35.527
+    - type: mrr_at_100
+      value: 36.451
+    - type: mrr_at_1000
+      value: 36.522
+    - type: mrr_at_3
+      value: 33.410000000000004
+    - type: mrr_at_5
+      value: 34.537
+    - type: ndcg_at_1
+      value: 28.374
+    - type: ndcg_at_10
+      value: 37.172
+    - type: ndcg_at_100
+      value: 42.474000000000004
+    - type: ndcg_at_1000
+      value: 44.853
+    - type: ndcg_at_3
+      value: 32.931
+    - type: ndcg_at_5
+      value: 34.882999999999996
+    - type: precision_at_1
+      value: 28.374
+    - type: precision_at_10
+      value: 5.813
+    - type: precision_at_100
+      value: 0.928
+    - type: precision_at_1000
+      value: 0.121
+    - type: precision_at_3
+      value: 14.008000000000001
+    - type: precision_at_5
+      value: 9.754999999999999
+    - type: recall_at_1
+      value: 25.625999999999998
+    - type: recall_at_10
+      value: 47.812
+    - type: recall_at_100
+      value: 71.61800000000001
+    - type: recall_at_1000
+      value: 88.881
+    - type: recall_at_3
+      value: 35.876999999999995
+    - type: recall_at_5
+      value: 40.839
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/cqadupstack-tex
+      name: MTEB CQADupstackTexRetrieval
+      config: default
+      split: test
+      revision: 46989137a86843e03a6195de44b09deda022eec7
+    metrics:
+    - type: map_at_1
+      value: 18.233
+    - type: map_at_10
+      value: 26.375999999999998
+    - type: map_at_100
+      value: 27.575
+    - type: map_at_1000
+      value: 27.706999999999997
+    - type: map_at_3
+      value: 23.619
+    - type: map_at_5
+      value: 25.217
+    - type: mrr_at_1
+      value: 22.023
+    - type: mrr_at_10
+      value: 30.122
+    - type: mrr_at_100
+      value: 31.083
+    - type: mrr_at_1000
+      value: 31.163999999999998
+    - type: mrr_at_3
+      value: 27.541
+    - type: mrr_at_5
+      value: 29.061999999999998
+    - type: ndcg_at_1
+      value: 22.023
+    - type: ndcg_at_10
+      value: 31.476
+    - type: ndcg_at_100
+      value: 37.114000000000004
+    - type: ndcg_at_1000
+      value: 39.981
+    - type: ndcg_at_3
+      value: 26.538
+    - type: ndcg_at_5
+      value: 29.016
+    - type: precision_at_1
+      value: 22.023
+    - type: precision_at_10
+      value: 5.819
+    - type: precision_at_100
+      value: 1.018
+    - type: precision_at_1000
+      value: 0.14300000000000002
+    - type: precision_at_3
+      value: 12.583
+    - type: precision_at_5
+      value: 9.36
+    - type: recall_at_1
+      value: 18.233
+    - type: recall_at_10
+      value: 43.029
+    - type: recall_at_100
+      value: 68.253
+    - type: recall_at_1000
+      value: 88.319
+    - type: recall_at_3
+      value: 29.541
+    - type: recall_at_5
+      value: 35.783
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/cqadupstack-unix
+      name: MTEB CQADupstackUnixRetrieval
+      config: default
+      split: test
+      revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
+    metrics:
+    - type: map_at_1
+      value: 28.923
+    - type: map_at_10
+      value: 39.231
+    - type: map_at_100
+      value: 40.483000000000004
+    - type: map_at_1000
+      value: 40.575
+    - type: map_at_3
+      value: 35.94
+    - type: map_at_5
+      value: 37.683
+    - type: mrr_at_1
+      value: 33.955
+    - type: mrr_at_10
+      value: 43.163000000000004
+    - type: mrr_at_100
+      value: 44.054
+    - type: mrr_at_1000
+      value: 44.099
+    - type: mrr_at_3
+      value: 40.361000000000004
+    - type: mrr_at_5
+      value: 41.905
+    - type: ndcg_at_1
+      value: 33.955
+    - type: ndcg_at_10
+      value: 45.068000000000005
+    - type: ndcg_at_100
+      value: 50.470000000000006
+    - type: ndcg_at_1000
+      value: 52.349000000000004
+    - type: ndcg_at_3
+      value: 39.298
+    - type: ndcg_at_5
+      value: 41.821999999999996
+    - type: precision_at_1
+      value: 33.955
+    - type: precision_at_10
+      value: 7.649
+    - type: precision_at_100
+      value: 1.173
+    - type: precision_at_1000
+      value: 0.14200000000000002
+    - type: precision_at_3
+      value: 17.817
+    - type: precision_at_5
+      value: 12.537
+    - type: recall_at_1
+      value: 28.923
+    - type: recall_at_10
+      value: 58.934
+    - type: recall_at_100
+      value: 81.809
+    - type: recall_at_1000
+      value: 94.71300000000001
+    - type: recall_at_3
+      value: 42.975
+    - type: recall_at_5
+      value: 49.501
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/cqadupstack-webmasters
+      name: MTEB CQADupstackWebmastersRetrieval
+      config: default
+      split: test
+      revision: 160c094312a0e1facb97e55eeddb698c0abe3571
+    metrics:
+    - type: map_at_1
+      value: 28.596
+    - type: map_at_10
+      value: 38.735
+    - type: map_at_100
+      value: 40.264
+    - type: map_at_1000
+      value: 40.48
+    - type: map_at_3
+      value: 35.394999999999996
+    - type: map_at_5
+      value: 37.099
+    - type: mrr_at_1
+      value: 33.992
+    - type: mrr_at_10
+      value: 43.076
+    - type: mrr_at_100
+      value: 44.005
+    - type: mrr_at_1000
+      value: 44.043
+    - type: mrr_at_3
+      value: 40.415
+    - type: mrr_at_5
+      value: 41.957
+    - type: ndcg_at_1
+      value: 33.992
+    - type: ndcg_at_10
+      value: 44.896
+    - type: ndcg_at_100
+      value: 50.44499999999999
+    - type: ndcg_at_1000
+      value: 52.675000000000004
+    - type: ndcg_at_3
+      value: 39.783
+    - type: ndcg_at_5
+      value: 41.997
+    - type: precision_at_1
+      value: 33.992
+    - type: precision_at_10
+      value: 8.498
+    - type: precision_at_100
+      value: 1.585
+    - type: precision_at_1000
+      value: 0.248
+    - type: precision_at_3
+      value: 18.511
+    - type: precision_at_5
+      value: 13.241
+    - type: recall_at_1
+      value: 28.596
+    - type: recall_at_10
+      value: 56.885
+    - type: recall_at_100
+      value: 82.306
+    - type: recall_at_1000
+      value: 95.813
+    - type: recall_at_3
+      value: 42.168
+    - type: recall_at_5
+      value: 48.32
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/cqadupstack-wordpress
+      name: MTEB CQADupstackWordpressRetrieval
+      config: default
+      split: test
+      revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
+    metrics:
+    - type: map_at_1
+      value: 25.576
+    - type: map_at_10
+      value: 33.034
+    - type: map_at_100
+      value: 34.117999999999995
+    - type: map_at_1000
+      value: 34.222
+    - type: map_at_3
+      value: 30.183
+    - type: map_at_5
+      value: 31.974000000000004
+    - type: mrr_at_1
+      value: 27.542
+    - type: mrr_at_10
+      value: 34.838
+    - type: mrr_at_100
+      value: 35.824
+    - type: mrr_at_1000
+      value: 35.899
+    - type: mrr_at_3
+      value: 32.348
+    - type: mrr_at_5
+      value: 34.039
+    - type: ndcg_at_1
+      value: 27.542
+    - type: ndcg_at_10
+      value: 37.663000000000004
+    - type: ndcg_at_100
+      value: 42.762
+    - type: ndcg_at_1000
+      value: 45.235
+    - type: ndcg_at_3
+      value: 32.227
+    - type: ndcg_at_5
+      value: 35.27
+    - type: precision_at_1
+      value: 27.542
+    - type: precision_at_10
+      value: 5.840999999999999
+    - type: precision_at_100
+      value: 0.895
+    - type: precision_at_1000
+      value: 0.123
+    - type: precision_at_3
+      value: 13.370000000000001
+    - type: precision_at_5
+      value: 9.797
+    - type: recall_at_1
+      value: 25.576
+    - type: recall_at_10
+      value: 50.285000000000004
+    - type: recall_at_100
+      value: 73.06
+    - type: recall_at_1000
+      value: 91.15299999999999
+    - type: recall_at_3
+      value: 35.781
+    - type: recall_at_5
+      value: 43.058
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/climate-fever
+      name: MTEB ClimateFEVER
+      config: default
+      split: test
+      revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
+    metrics:
+    - type: map_at_1
+      value: 17.061
+    - type: map_at_10
+      value: 29.464000000000002
+    - type: map_at_100
+      value: 31.552999999999997
+    - type: map_at_1000
+      value: 31.707
+    - type: map_at_3
+      value: 24.834999999999997
+    - type: map_at_5
+      value: 27.355
+    - type: mrr_at_1
+      value: 38.958
+    - type: mrr_at_10
+      value: 51.578
+    - type: mrr_at_100
+      value: 52.262
+    - type: mrr_at_1000
+      value: 52.283
+    - type: mrr_at_3
+      value: 48.599
+    - type: mrr_at_5
+      value: 50.404
+    - type: ndcg_at_1
+      value: 38.958
+    - type: ndcg_at_10
+      value: 39.367999999999995
+    - type: ndcg_at_100
+      value: 46.521
+    - type: ndcg_at_1000
+      value: 49.086999999999996
+    - type: ndcg_at_3
+      value: 33.442
+    - type: ndcg_at_5
+      value: 35.515
+    - type: precision_at_1
+      value: 38.958
+    - type: precision_at_10
+      value: 12.110999999999999
+    - type: precision_at_100
+      value: 1.982
+    - type: precision_at_1000
+      value: 0.247
+    - type: precision_at_3
+      value: 25.102999999999998
+    - type: precision_at_5
+      value: 18.971
+    - type: recall_at_1
+      value: 17.061
+    - type: recall_at_10
+      value: 45.198
+    - type: recall_at_100
+      value: 69.18900000000001
+    - type: recall_at_1000
+      value: 83.38499999999999
+    - type: recall_at_3
+      value: 30.241
+    - type: recall_at_5
+      value: 36.851
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/dbpedia
+      name: MTEB DBPedia
+      config: default
+      split: test
+      revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
+    metrics:
+    - type: map_at_1
+      value: 9.398
+    - type: map_at_10
+      value: 21.421
+    - type: map_at_100
+      value: 31.649
+    - type: map_at_1000
+      value: 33.469
+    - type: map_at_3
+      value: 15.310000000000002
+    - type: map_at_5
+      value: 17.946
+    - type: mrr_at_1
+      value: 71
+    - type: mrr_at_10
+      value: 78.92099999999999
+    - type: mrr_at_100
+      value: 79.225
+    - type: mrr_at_1000
+      value: 79.23
+    - type: mrr_at_3
+      value: 77.792
+    - type: mrr_at_5
+      value: 78.467
+    - type: ndcg_at_1
+      value: 57.99999999999999
+    - type: ndcg_at_10
+      value: 44.733000000000004
+    - type: ndcg_at_100
+      value: 50.646
+    - type: ndcg_at_1000
+      value: 57.903999999999996
+    - type: ndcg_at_3
+      value: 49.175999999999995
+    - type: ndcg_at_5
+      value: 46.800999999999995
+    - type: precision_at_1
+      value: 71
+    - type: precision_at_10
+      value: 36.25
+    - type: precision_at_100
+      value: 12.135
+    - type: precision_at_1000
+      value: 2.26
+    - type: precision_at_3
+      value: 52.75
+    - type: precision_at_5
+      value: 45.65
+    - type: recall_at_1
+      value: 9.398
+    - type: recall_at_10
+      value: 26.596999999999998
+    - type: recall_at_100
+      value: 57.943
+    - type: recall_at_1000
+      value: 81.147
+    - type: recall_at_3
+      value: 16.634
+    - type: recall_at_5
+      value: 20.7
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/emotion
+      name: MTEB EmotionClassification
+      config: default
+      split: test
+      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
+    metrics:
+    - type: accuracy
+      value: 46.535000000000004
+    - type: f1
+      value: 42.53702746452163
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/fever
+      name: MTEB FEVER
+      config: default
+      split: test
+      revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
+    metrics:
+    - type: map_at_1
+      value: 77.235
+    - type: map_at_10
+      value: 85.504
+    - type: map_at_100
+      value: 85.707
+    - type: map_at_1000
+      value: 85.718
+    - type: map_at_3
+      value: 84.425
+    - type: map_at_5
+      value: 85.13
+    - type: mrr_at_1
+      value: 83.363
+    - type: mrr_at_10
+      value: 89.916
+    - type: mrr_at_100
+      value: 89.955
+    - type: mrr_at_1000
+      value: 89.956
+    - type: mrr_at_3
+      value: 89.32600000000001
+    - type: mrr_at_5
+      value: 89.79
+    - type: ndcg_at_1
+      value: 83.363
+    - type: ndcg_at_10
+      value: 89.015
+    - type: ndcg_at_100
+      value: 89.649
+    - type: ndcg_at_1000
+      value: 89.825
+    - type: ndcg_at_3
+      value: 87.45100000000001
+    - type: ndcg_at_5
+      value: 88.39399999999999
+    - type: precision_at_1
+      value: 83.363
+    - type: precision_at_10
+      value: 10.659
+    - type: precision_at_100
+      value: 1.122
+    - type: precision_at_1000
+      value: 0.11499999999999999
+    - type: precision_at_3
+      value: 33.338
+    - type: precision_at_5
+      value: 20.671999999999997
+    - type: recall_at_1
+      value: 77.235
+    - type: recall_at_10
+      value: 95.389
+    - type: recall_at_100
+      value: 97.722
+    - type: recall_at_1000
+      value: 98.744
+    - type: recall_at_3
+      value: 91.19800000000001
+    - type: recall_at_5
+      value: 93.635
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/fiqa
+      name: MTEB FiQA2018
+      config: default
+      split: test
+      revision: 27a168819829fe9bcd655c2df245fb19452e8e06
+    metrics:
+    - type: map_at_1
+      value: 20.835
+    - type: map_at_10
+      value: 34.459
+    - type: map_at_100
+      value: 36.335
+    - type: map_at_1000
+      value: 36.518
+    - type: map_at_3
+      value: 30.581000000000003
+    - type: map_at_5
+      value: 32.859
+    - type: mrr_at_1
+      value: 40.894999999999996
+    - type: mrr_at_10
+      value: 50.491
+    - type: mrr_at_100
+      value: 51.243
+    - type: mrr_at_1000
+      value: 51.286
+    - type: mrr_at_3
+      value: 47.994
+    - type: mrr_at_5
+      value: 49.429
+    - type: ndcg_at_1
+      value: 40.894999999999996
+    - type: ndcg_at_10
+      value: 42.403
+    - type: ndcg_at_100
+      value: 48.954
+    - type: ndcg_at_1000
+      value: 51.961
+    - type: ndcg_at_3
+      value: 39.11
+    - type: ndcg_at_5
+      value: 40.152
+    - type: precision_at_1
+      value: 40.894999999999996
+    - type: precision_at_10
+      value: 11.466
+    - type: precision_at_100
+      value: 1.833
+    - type: precision_at_1000
+      value: 0.23700000000000002
+    - type: precision_at_3
+      value: 25.874000000000002
+    - type: precision_at_5
+      value: 19.012
+    - type: recall_at_1
+      value: 20.835
+    - type: recall_at_10
+      value: 49.535000000000004
+    - type: recall_at_100
+      value: 73.39099999999999
+    - type: recall_at_1000
+      value: 91.01599999999999
+    - type: recall_at_3
+      value: 36.379
+    - type: recall_at_5
+      value: 42.059999999999995
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/hotpotqa
+      name: MTEB HotpotQA
+      config: default
+      split: test
+      revision: ab518f4d6fcca38d87c25209f94beba119d02014
+    metrics:
+    - type: map_at_1
+      value: 40.945
+    - type: map_at_10
+      value: 65.376
+    - type: map_at_100
+      value: 66.278
+    - type: map_at_1000
+      value: 66.33
+    - type: map_at_3
+      value: 61.753
+    - type: map_at_5
+      value: 64.077
+    - type: mrr_at_1
+      value: 81.891
+    - type: mrr_at_10
+      value: 87.256
+    - type: mrr_at_100
+      value: 87.392
+    - type: mrr_at_1000
+      value: 87.395
+    - type: mrr_at_3
+      value: 86.442
+    - type: mrr_at_5
+      value: 86.991
+    - type: ndcg_at_1
+      value: 81.891
+    - type: ndcg_at_10
+      value: 73.654
+    - type: ndcg_at_100
+      value: 76.62299999999999
+    - type: ndcg_at_1000
+      value: 77.60000000000001
+    - type: ndcg_at_3
+      value: 68.71199999999999
+    - type: ndcg_at_5
+      value: 71.563
+    - type: precision_at_1
+      value: 81.891
+    - type: precision_at_10
+      value: 15.409
+    - type: precision_at_100
+      value: 1.77
+    - type: precision_at_1000
+      value: 0.19
+    - type: precision_at_3
+      value: 44.15
+    - type: precision_at_5
+      value: 28.732000000000003
+    - type: recall_at_1
+      value: 40.945
+    - type: recall_at_10
+      value: 77.04299999999999
+    - type: recall_at_100
+      value: 88.508
+    - type: recall_at_1000
+      value: 94.943
+    - type: recall_at_3
+      value: 66.226
+    - type: recall_at_5
+      value: 71.83
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/imdb
+      name: MTEB ImdbClassification
+      config: default
+      split: test
+      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
+    metrics:
+    - type: accuracy
+      value: 74.08200000000001
+    - type: ap
+      value: 68.10929101713998
+    - type: f1
+      value: 73.98447117652009
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/msmarco
+      name: MTEB MSMARCO
+      config: default
+      split: dev
+      revision: c5a29a104738b98a9e76336939199e264163d4a0
+    metrics:
+    - type: map_at_1
+      value: 21.729000000000003
+    - type: map_at_10
+      value: 34.602
+    - type: map_at_100
+      value: 35.756
+    - type: map_at_1000
+      value: 35.803000000000004
+    - type: map_at_3
+      value: 30.619000000000003
+    - type: map_at_5
+      value: 32.914
+    - type: mrr_at_1
+      value: 22.364
+    - type: mrr_at_10
+      value: 35.183
+    - type: mrr_at_100
+      value: 36.287000000000006
+    - type: mrr_at_1000
+      value: 36.327999999999996
+    - type: mrr_at_3
+      value: 31.258000000000003
+    - type: mrr_at_5
+      value: 33.542
+    - type: ndcg_at_1
+      value: 22.364
+    - type: ndcg_at_10
+      value: 41.765
+    - type: ndcg_at_100
+      value: 47.293
+    - type: ndcg_at_1000
+      value: 48.457
+    - type: ndcg_at_3
+      value: 33.676
+    - type: ndcg_at_5
+      value: 37.783
+    - type: precision_at_1
+      value: 22.364
+    - type: precision_at_10
+      value: 6.662
+    - type: precision_at_100
+      value: 0.943
+    - type: precision_at_1000
+      value: 0.104
+    - type: precision_at_3
+      value: 14.435999999999998
+    - type: precision_at_5
+      value: 10.764999999999999
+    - type: recall_at_1
+      value: 21.729000000000003
+    - type: recall_at_10
+      value: 63.815999999999995
+    - type: recall_at_100
+      value: 89.265
+    - type: recall_at_1000
+      value: 98.149
+    - type: recall_at_3
+      value: 41.898
+    - type: recall_at_5
+      value: 51.76500000000001
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_domain
+      name: MTEB MTOPDomainClassification (en)
+      config: en
+      split: test
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+    metrics:
+    - type: accuracy
+      value: 92.73141814865483
+    - type: f1
+      value: 92.17518476408004
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_intent
+      name: MTEB MTOPIntentClassification (en)
+      config: en
+      split: test
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+    metrics:
+    - type: accuracy
+      value: 65.18011855905152
+    - type: f1
+      value: 46.70999638311856
+  - task:
+      type: Classification
+    dataset:
+      type: masakhane/masakhanews
+      name: MTEB MasakhaNEWSClassification (eng)
+      config: eng
+      split: test
+      revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
+    metrics:
+    - type: accuracy
+      value: 75.24261603375525
+    - type: f1
+      value: 74.07895183913367
+  - task:
+      type: Clustering
+    dataset:
+      type: masakhane/masakhanews
+      name: MTEB MasakhaNEWSClusteringP2P (eng)
+      config: eng
+      split: test
+      revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
+    metrics:
+    - type: v_measure
+      value: 28.43855875387446
+  - task:
+      type: Clustering
+    dataset:
+      type: masakhane/masakhanews
+      name: MTEB MasakhaNEWSClusteringS2S (eng)
+      config: eng
+      split: test
+      revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
+    metrics:
+    - type: v_measure
+      value: 29.05331990256969
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (en)
+      config: en
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 66.92333557498318
+    - type: f1
+      value: 64.29789389602692
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (en)
+      config: en
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 72.74714189643578
+    - type: f1
+      value: 71.672585608315
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-p2p
+      name: MTEB MedrxivClusteringP2P
+      config: default
+      split: test
+      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
+    metrics:
+    - type: v_measure
+      value: 31.503564225501613
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-s2s
+      name: MTEB MedrxivClusteringS2S
+      config: default
+      split: test
+      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
+    metrics:
+    - type: v_measure
+      value: 28.410225127136457
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/mind_small
+      name: MTEB MindSmallReranking
+      config: default
+      split: test
+      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
+    metrics:
+    - type: map
+      value: 29.170019896091908
+    - type: mrr
+      value: 29.881276831500976
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/nfcorpus
+      name: MTEB NFCorpus
+      config: default
+      split: test
+      revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
+    metrics:
+    - type: map_at_1
+      value: 6.544
+    - type: map_at_10
+      value: 14.116999999999999
+    - type: map_at_100
+      value: 17.522
+    - type: map_at_1000
+      value: 19
+    - type: map_at_3
+      value: 10.369
+    - type: map_at_5
+      value: 12.189
+    - type: mrr_at_1
+      value: 47.988
+    - type: mrr_at_10
+      value: 56.84
+    - type: mrr_at_100
+      value: 57.367000000000004
+    - type: mrr_at_1000
+      value: 57.403000000000006
+    - type: mrr_at_3
+      value: 54.592
+    - type: mrr_at_5
+      value: 56.233
+    - type: ndcg_at_1
+      value: 45.82
+    - type: ndcg_at_10
+      value: 36.767
+    - type: ndcg_at_100
+      value: 33.356
+    - type: ndcg_at_1000
+      value: 42.062
+    - type: ndcg_at_3
+      value: 42.15
+    - type: ndcg_at_5
+      value: 40.355000000000004
+    - type: precision_at_1
+      value: 47.988
+    - type: precision_at_10
+      value: 27.121000000000002
+    - type: precision_at_100
+      value: 8.455
+    - type: precision_at_1000
+      value: 2.103
+    - type: precision_at_3
+      value: 39.628
+    - type: precision_at_5
+      value: 35.356
+    - type: recall_at_1
+      value: 6.544
+    - type: recall_at_10
+      value: 17.928
+    - type: recall_at_100
+      value: 32.843
+    - type: recall_at_1000
+      value: 65.752
+    - type: recall_at_3
+      value: 11.297
+    - type: recall_at_5
+      value: 14.357000000000001
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/nq
+      name: MTEB NQ
+      config: default
+      split: test
+      revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
+    metrics:
+    - type: map_at_1
+      value: 39.262
+    - type: map_at_10
+      value: 55.095000000000006
+    - type: map_at_100
+      value: 55.93900000000001
+    - type: map_at_1000
+      value: 55.955999999999996
+    - type: map_at_3
+      value: 50.93
+    - type: map_at_5
+      value: 53.491
+    - type: mrr_at_1
+      value: 43.598
+    - type: mrr_at_10
+      value: 57.379999999999995
+    - type: mrr_at_100
+      value: 57.940999999999995
+    - type: mrr_at_1000
+      value: 57.952000000000005
+    - type: mrr_at_3
+      value: 53.998000000000005
+    - type: mrr_at_5
+      value: 56.128
+    - type: ndcg_at_1
+      value: 43.598
+    - type: ndcg_at_10
+      value: 62.427
+    - type: ndcg_at_100
+      value: 65.759
+    - type: ndcg_at_1000
+      value: 66.133
+    - type: ndcg_at_3
+      value: 54.745999999999995
+    - type: ndcg_at_5
+      value: 58.975
+    - type: precision_at_1
+      value: 43.598
+    - type: precision_at_10
+      value: 9.789
+    - type: precision_at_100
+      value: 1.171
+    - type: precision_at_1000
+      value: 0.121
+    - type: precision_at_3
+      value: 24.295
+    - type: precision_at_5
+      value: 17.028
+    - type: recall_at_1
+      value: 39.262
+    - type: recall_at_10
+      value: 82.317
+    - type: recall_at_100
+      value: 96.391
+    - type: recall_at_1000
+      value: 99.116
+    - type: recall_at_3
+      value: 62.621
+    - type: recall_at_5
+      value: 72.357
+  - task:
+      type: Classification
+    dataset:
+      type: ag_news
+      name: MTEB NewsClassification
+      config: default
+      split: test
+      revision: eb185aade064a813bc0b7f42de02595523103ca4
+    metrics:
+    - type: accuracy
+      value: 78.17500000000001
+    - type: f1
+      value: 78.01940892857273
+  - task:
+      type: PairClassification
+    dataset:
+      type: GEM/opusparcus
+      name: MTEB OpusparcusPC (en)
+      config: en
+      split: test
+      revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
+    metrics:
+    - type: cos_sim_accuracy
+      value: 99.89816700610999
+    - type: cos_sim_ap
+      value: 100
+    - type: cos_sim_f1
+      value: 99.9490575649516
+    - type: cos_sim_precision
+      value: 100
+    - type: cos_sim_recall
+      value: 99.89816700610999
+    - type: dot_accuracy
+      value: 99.89816700610999
+    - type: dot_ap
+      value: 100
+    - type: dot_f1
+      value: 99.9490575649516
+    - type: dot_precision
+      value: 100
+    - type: dot_recall
+      value: 99.89816700610999
+    - type: euclidean_accuracy
+      value: 99.89816700610999
+    - type: euclidean_ap
+      value: 100
+    - type: euclidean_f1
+      value: 99.9490575649516
+    - type: euclidean_precision
+      value: 100
+    - type: euclidean_recall
+      value: 99.89816700610999
+    - type: manhattan_accuracy
+      value: 99.89816700610999
+    - type: manhattan_ap
+      value: 100
+    - type: manhattan_f1
+      value: 99.9490575649516
+    - type: manhattan_precision
+      value: 100
+    - type: manhattan_recall
+      value: 99.89816700610999
+    - type: max_accuracy
+      value: 99.89816700610999
+    - type: max_ap
+      value: 100
+    - type: max_f1
+      value: 99.9490575649516
+  - task:
+      type: PairClassification
+    dataset:
+      type: paws-x
+      name: MTEB PawsX (en)
+      config: en
+      split: test
+      revision: 8a04d940a42cd40658986fdd8e3da561533a3646
+    metrics:
+    - type: cos_sim_accuracy
+      value: 61
+    - type: cos_sim_ap
+      value: 59.630757252602464
+    - type: cos_sim_f1
+      value: 62.37521514629949
+    - type: cos_sim_precision
+      value: 45.34534534534534
+    - type: cos_sim_recall
+      value: 99.88974641675854
+    - type: dot_accuracy
+      value: 61
+    - type: dot_ap
+      value: 59.631527308059006
+    - type: dot_f1
+      value: 62.37521514629949
+    - type: dot_precision
+      value: 45.34534534534534
+    - type: dot_recall
+      value: 99.88974641675854
+    - type: euclidean_accuracy
+      value: 61
+    - type: euclidean_ap
+      value: 59.630757252602464
+    - type: euclidean_f1
+      value: 62.37521514629949
+    - type: euclidean_precision
+      value: 45.34534534534534
+    - type: euclidean_recall
+      value: 99.88974641675854
+    - type: manhattan_accuracy
+      value: 60.9
+    - type: manhattan_ap
+      value: 59.613947780462254
+    - type: manhattan_f1
+      value: 62.37521514629949
+    - type: manhattan_precision
+      value: 45.34534534534534
+    - type: manhattan_recall
+      value: 99.88974641675854
+    - type: max_accuracy
+      value: 61
+    - type: max_ap
+      value: 59.631527308059006
+    - type: max_f1
+      value: 62.37521514629949
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/quora
+      name: MTEB QuoraRetrieval
+      config: default
+      split: test
+      revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
+    metrics:
+    - type: map_at_1
+      value: 69.963
+    - type: map_at_10
+      value: 83.59400000000001
+    - type: map_at_100
+      value: 84.236
+    - type: map_at_1000
+      value: 84.255
+    - type: map_at_3
+      value: 80.69800000000001
+    - type: map_at_5
+      value: 82.568
+    - type: mrr_at_1
+      value: 80.58999999999999
+    - type: mrr_at_10
+      value: 86.78200000000001
+    - type: mrr_at_100
+      value: 86.89099999999999
+    - type: mrr_at_1000
+      value: 86.893
+    - type: mrr_at_3
+      value: 85.757
+    - type: mrr_at_5
+      value: 86.507
+    - type: ndcg_at_1
+      value: 80.60000000000001
+    - type: ndcg_at_10
+      value: 87.41799999999999
+    - type: ndcg_at_100
+      value: 88.723
+    - type: ndcg_at_1000
+      value: 88.875
+    - type: ndcg_at_3
+      value: 84.565
+    - type: ndcg_at_5
+      value: 86.236
+    - type: precision_at_1
+      value: 80.60000000000001
+    - type: precision_at_10
+      value: 13.239
+    - type: precision_at_100
+      value: 1.5150000000000001
+    - type: precision_at_1000
+      value: 0.156
+    - type: precision_at_3
+      value: 36.947
+    - type: precision_at_5
+      value: 24.354
+    - type: recall_at_1
+      value: 69.963
+    - type: recall_at_10
+      value: 94.553
+    - type: recall_at_100
+      value: 99.104
+    - type: recall_at_1000
+      value: 99.872
+    - type: recall_at_3
+      value: 86.317
+    - type: recall_at_5
+      value: 91.023
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering
+      name: MTEB RedditClustering
+      config: default
+      split: test
+      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
+    metrics:
+    - type: v_measure
+      value: 47.52890410998761
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering-p2p
+      name: MTEB RedditClusteringP2P
+      config: default
+      split: test
+      revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
+    metrics:
+    - type: v_measure
+      value: 62.760692287940486
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/scidocs
+      name: MTEB SCIDOCS
+      config: default
+      split: test
+      revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
+    metrics:
+    - type: map_at_1
+      value: 5.093
+    - type: map_at_10
+      value: 12.695
+    - type: map_at_100
+      value: 14.824000000000002
+    - type: map_at_1000
+      value: 15.123000000000001
+    - type: map_at_3
+      value: 8.968
+    - type: map_at_5
+      value: 10.828
+    - type: mrr_at_1
+      value: 25.1
+    - type: mrr_at_10
+      value: 35.894999999999996
+    - type: mrr_at_100
+      value: 36.966
+    - type: mrr_at_1000
+      value: 37.019999999999996
+    - type: mrr_at_3
+      value: 32.467
+    - type: mrr_at_5
+      value: 34.416999999999994
+    - type: ndcg_at_1
+      value: 25.1
+    - type: ndcg_at_10
+      value: 21.096999999999998
+    - type: ndcg_at_100
+      value: 29.202
+    - type: ndcg_at_1000
+      value: 34.541
+    - type: ndcg_at_3
+      value: 19.875
+    - type: ndcg_at_5
+      value: 17.497
+    - type: precision_at_1
+      value: 25.1
+    - type: precision_at_10
+      value: 10.9
+    - type: precision_at_100
+      value: 2.255
+    - type: precision_at_1000
+      value: 0.35400000000000004
+    - type: precision_at_3
+      value: 18.367
+    - type: precision_at_5
+      value: 15.299999999999999
+    - type: recall_at_1
+      value: 5.093
+    - type: recall_at_10
+      value: 22.092
+    - type: recall_at_100
+      value: 45.778
+    - type: recall_at_1000
+      value: 71.985
+    - type: recall_at_3
+      value: 11.167
+    - type: recall_at_5
+      value: 15.501999999999999
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sickr-sts
+      name: MTEB SICK-R
+      config: default
+      split: test
+      revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
+    metrics:
+    - type: cos_sim_pearson
+      value: 74.04386981759481
+    - type: cos_sim_spearman
+      value: 69.12484963763646
+    - type: euclidean_pearson
+      value: 71.49384353291062
+    - type: euclidean_spearman
+      value: 69.12484548317074
+    - type: manhattan_pearson
+      value: 71.49828173987272
+    - type: manhattan_spearman
+      value: 69.08350274367014
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts12-sts
+      name: MTEB STS12
+      config: default
+      split: test
+      revision: a0d554a64d88156834ff5ae9920b964011b16384
+    metrics:
+    - type: cos_sim_pearson
+      value: 66.95372527615659
+    - type: cos_sim_spearman
+      value: 66.96821894433991
+    - type: euclidean_pearson
+      value: 64.675348002074
+    - type: euclidean_spearman
+      value: 66.96821894433991
+    - type: manhattan_pearson
+      value: 64.5965887073831
+    - type: manhattan_spearman
+      value: 66.88569076794741
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts13-sts
+      name: MTEB STS13
+      config: default
+      split: test
+      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
+    metrics:
+    - type: cos_sim_pearson
+      value: 77.34698437961983
+    - type: cos_sim_spearman
+      value: 79.1153001117325
+    - type: euclidean_pearson
+      value: 78.53562874696966
+    - type: euclidean_spearman
+      value: 79.11530018205724
+    - type: manhattan_pearson
+      value: 78.46484988944093
+    - type: manhattan_spearman
+      value: 79.01416027493104
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts14-sts
+      name: MTEB STS14
+      config: default
+      split: test
+      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
+    metrics:
+    - type: cos_sim_pearson
+      value: 68.81220371935373
+    - type: cos_sim_spearman
+      value: 68.50538405089604
+    - type: euclidean_pearson
+      value: 68.69204272683749
+    - type: euclidean_spearman
+      value: 68.50534223912419
+    - type: manhattan_pearson
+      value: 68.67300120149523
+    - type: manhattan_spearman
+      value: 68.45404301623115
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts15-sts
+      name: MTEB STS15
+      config: default
+      split: test
+      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
+    metrics:
+    - type: cos_sim_pearson
+      value: 78.2464678879813
+    - type: cos_sim_spearman
+      value: 79.92003940566667
+    - type: euclidean_pearson
+      value: 79.8080778793964
+    - type: euclidean_spearman
+      value: 79.92003940566667
+    - type: manhattan_pearson
+      value: 79.80153621444681
+    - type: manhattan_spearman
+      value: 79.91293261418134
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts16-sts
+      name: MTEB STS16
+      config: default
+      split: test
+      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
+    metrics:
+    - type: cos_sim_pearson
+      value: 76.31179207708662
+    - type: cos_sim_spearman
+      value: 78.65597349856115
+    - type: euclidean_pearson
+      value: 78.76937027472678
+    - type: euclidean_spearman
+      value: 78.65597349856115
+    - type: manhattan_pearson
+      value: 78.77129513300605
+    - type: manhattan_spearman
+      value: 78.62640467680775
+  - 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: 79.43158429552561
+    - type: cos_sim_spearman
+      value: 81.46108646565362
+    - type: euclidean_pearson
+      value: 81.47071791452292
+    - type: euclidean_spearman
+      value: 81.46108646565362
+    - type: manhattan_pearson
+      value: 81.56920643846031
+    - type: manhattan_spearman
+      value: 81.42226241399516
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts22-crosslingual-sts
+      name: MTEB STS22 (en)
+      config: en
+      split: test
+      revision: eea2b4fe26a775864c896887d910b76a8098ad3f
+    metrics:
+    - type: cos_sim_pearson
+      value: 66.89546474141514
+    - type: cos_sim_spearman
+      value: 65.8393752170531
+    - type: euclidean_pearson
+      value: 67.2580522762307
+    - type: euclidean_spearman
+      value: 65.8393752170531
+    - type: manhattan_pearson
+      value: 67.45157729300522
+    - type: manhattan_spearman
+      value: 66.19470854403802
+  - task:
+      type: STS
+    dataset:
+      type: mteb/stsbenchmark-sts
+      name: MTEB STSBenchmark
+      config: default
+      split: test
+      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
+    metrics:
+    - type: cos_sim_pearson
+      value: 71.39566306334434
+    - type: cos_sim_spearman
+      value: 74.0981396086974
+    - type: euclidean_pearson
+      value: 73.7834496259745
+    - type: euclidean_spearman
+      value: 74.09803741302046
+    - type: manhattan_pearson
+      value: 73.79958138780945
+    - type: manhattan_spearman
+      value: 74.09894837555905
+  - task:
+      type: STS
+    dataset:
+      type: PhilipMay/stsb_multi_mt
+      name: MTEB STSBenchmarkMultilingualSTS (en)
+      config: en
+      split: test
+      revision: 93d57ef91790589e3ce9c365164337a8a78b7632
+    metrics:
+    - type: cos_sim_pearson
+      value: 71.39566311006806
+    - type: cos_sim_spearman
+      value: 74.0981396086974
+    - type: euclidean_pearson
+      value: 73.78344970897099
+    - type: euclidean_spearman
+      value: 74.09803741302046
+    - type: manhattan_pearson
+      value: 73.79958147136705
+    - type: manhattan_spearman
+      value: 74.09894837555905
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/scidocs-reranking
+      name: MTEB SciDocsRR
+      config: default
+      split: test
+      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
+    metrics:
+    - type: map
+      value: 80.81059564334683
+    - type: mrr
+      value: 94.62696617108381
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/scifact
+      name: MTEB SciFact
+      config: default
+      split: test
+      revision: 0228b52cf27578f30900b9e5271d331663a030d7
+    metrics:
+    - type: map_at_1
+      value: 57.760999999999996
+    - type: map_at_10
+      value: 68.614
+    - type: map_at_100
+      value: 69.109
+    - type: map_at_1000
+      value: 69.134
+    - type: map_at_3
+      value: 65.735
+    - type: map_at_5
+      value: 67.42099999999999
+    - type: mrr_at_1
+      value: 60.667
+    - type: mrr_at_10
+      value: 69.94200000000001
+    - type: mrr_at_100
+      value: 70.254
+    - type: mrr_at_1000
+      value: 70.28
+    - type: mrr_at_3
+      value: 67.72200000000001
+    - type: mrr_at_5
+      value: 69.18900000000001
+    - type: ndcg_at_1
+      value: 60.667
+    - type: ndcg_at_10
+      value: 73.548
+    - type: ndcg_at_100
+      value: 75.381
+    - type: ndcg_at_1000
+      value: 75.991
+    - type: ndcg_at_3
+      value: 68.685
+    - type: ndcg_at_5
+      value: 71.26
+    - type: precision_at_1
+      value: 60.667
+    - type: precision_at_10
+      value: 9.833
+    - type: precision_at_100
+      value: 1.08
+    - type: precision_at_1000
+      value: 0.11299999999999999
+    - type: precision_at_3
+      value: 26.889000000000003
+    - type: precision_at_5
+      value: 17.8
+    - type: recall_at_1
+      value: 57.760999999999996
+    - type: recall_at_10
+      value: 87.13300000000001
+    - type: recall_at_100
+      value: 95
+    - type: recall_at_1000
+      value: 99.667
+    - type: recall_at_3
+      value: 74.211
+    - type: recall_at_5
+      value: 80.63900000000001
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/sprintduplicatequestions-pairclassification
+      name: MTEB SprintDuplicateQuestions
+      config: default
+      split: test
+      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
+    metrics:
+    - type: cos_sim_accuracy
+      value: 99.81881188118813
+    - type: cos_sim_ap
+      value: 95.21196473745837
+    - type: cos_sim_f1
+      value: 90.69767441860465
+    - type: cos_sim_precision
+      value: 91.71779141104295
+    - type: cos_sim_recall
+      value: 89.7
+    - type: dot_accuracy
+      value: 99.81881188118813
+    - type: dot_ap
+      value: 95.21196473745837
+    - type: dot_f1
+      value: 90.69767441860465
+    - type: dot_precision
+      value: 91.71779141104295
+    - type: dot_recall
+      value: 89.7
+    - type: euclidean_accuracy
+      value: 99.81881188118813
+    - type: euclidean_ap
+      value: 95.21196473745839
+    - type: euclidean_f1
+      value: 90.69767441860465
+    - type: euclidean_precision
+      value: 91.71779141104295
+    - type: euclidean_recall
+      value: 89.7
+    - type: manhattan_accuracy
+      value: 99.81287128712871
+    - type: manhattan_ap
+      value: 95.16667174835017
+    - type: manhattan_f1
+      value: 90.41095890410959
+    - type: manhattan_precision
+      value: 91.7610710607621
+    - type: manhattan_recall
+      value: 89.1
+    - type: max_accuracy
+      value: 99.81881188118813
+    - type: max_ap
+      value: 95.21196473745839
+    - type: max_f1
+      value: 90.69767441860465
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering
+      name: MTEB StackExchangeClustering
+      config: default
+      split: test
+      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
+    metrics:
+    - type: v_measure
+      value: 59.54942204515638
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering-p2p
+      name: MTEB StackExchangeClusteringP2P
+      config: default
+      split: test
+      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
+    metrics:
+    - type: v_measure
+      value: 39.42892282672948
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/stackoverflowdupquestions-reranking
+      name: MTEB StackOverflowDupQuestions
+      config: default
+      split: test
+      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
+    metrics:
+    - type: map
+      value: 51.189033075914324
+    - type: mrr
+      value: 51.97014790764791
+  - task:
+      type: Summarization
+    dataset:
+      type: mteb/summeval
+      name: MTEB SummEval
+      config: default
+      split: test
+      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
+    metrics:
+    - type: cos_sim_pearson
+      value: 30.09466569775977
+    - type: cos_sim_spearman
+      value: 30.31058660775912
+    - type: dot_pearson
+      value: 30.09466438861689
+    - type: dot_spearman
+      value: 30.31058660775912
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/trec-covid
+      name: MTEB TRECCOVID
+      config: default
+      split: test
+      revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
+    metrics:
+    - type: map_at_1
+      value: 0.253
+    - type: map_at_10
+      value: 2.07
+    - type: map_at_100
+      value: 12.679000000000002
+    - type: map_at_1000
+      value: 30.412
+    - type: map_at_3
+      value: 0.688
+    - type: map_at_5
+      value: 1.079
+    - type: mrr_at_1
+      value: 96
+    - type: mrr_at_10
+      value: 98
+    - type: mrr_at_100
+      value: 98
+    - type: mrr_at_1000
+      value: 98
+    - type: mrr_at_3
+      value: 98
+    - type: mrr_at_5
+      value: 98
+    - type: ndcg_at_1
+      value: 89
+    - type: ndcg_at_10
+      value: 79.646
+    - type: ndcg_at_100
+      value: 62.217999999999996
+    - type: ndcg_at_1000
+      value: 55.13400000000001
+    - type: ndcg_at_3
+      value: 83.458
+    - type: ndcg_at_5
+      value: 80.982
+    - type: precision_at_1
+      value: 96
+    - type: precision_at_10
+      value: 84.6
+    - type: precision_at_100
+      value: 64.34
+    - type: precision_at_1000
+      value: 24.534
+    - type: precision_at_3
+      value: 88.667
+    - type: precision_at_5
+      value: 85.6
+    - type: recall_at_1
+      value: 0.253
+    - type: recall_at_10
+      value: 2.253
+    - type: recall_at_100
+      value: 15.606
+    - type: recall_at_1000
+      value: 51.595
+    - type: recall_at_3
+      value: 0.7100000000000001
+    - type: recall_at_5
+      value: 1.139
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/touche2020
+      name: MTEB Touche2020
+      config: default
+      split: test
+      revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
+    metrics:
+    - type: map_at_1
+      value: 3.0540000000000003
+    - type: map_at_10
+      value: 13.078999999999999
+    - type: map_at_100
+      value: 19.468
+    - type: map_at_1000
+      value: 21.006
+    - type: map_at_3
+      value: 6.8629999999999995
+    - type: map_at_5
+      value: 9.187
+    - type: mrr_at_1
+      value: 42.857
+    - type: mrr_at_10
+      value: 56.735
+    - type: mrr_at_100
+      value: 57.352000000000004
+    - type: mrr_at_1000
+      value: 57.352000000000004
+    - type: mrr_at_3
+      value: 52.721
+    - type: mrr_at_5
+      value: 54.66
+    - type: ndcg_at_1
+      value: 38.775999999999996
+    - type: ndcg_at_10
+      value: 31.469
+    - type: ndcg_at_100
+      value: 42.016999999999996
+    - type: ndcg_at_1000
+      value: 52.60399999999999
+    - type: ndcg_at_3
+      value: 35.894
+    - type: ndcg_at_5
+      value: 33.873
+    - type: precision_at_1
+      value: 42.857
+    - type: precision_at_10
+      value: 27.346999999999998
+    - type: precision_at_100
+      value: 8.327
+    - type: precision_at_1000
+      value: 1.551
+    - type: precision_at_3
+      value: 36.735
+    - type: precision_at_5
+      value: 33.469
+    - type: recall_at_1
+      value: 3.0540000000000003
+    - type: recall_at_10
+      value: 19.185
+    - type: recall_at_100
+      value: 51.056000000000004
+    - type: recall_at_1000
+      value: 82.814
+    - type: recall_at_3
+      value: 7.961
+    - type: recall_at_5
+      value: 11.829
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/toxic_conversations_50k
+      name: MTEB ToxicConversationsClassification
+      config: default
+      split: test
+      revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
+    metrics:
+    - type: accuracy
+      value: 64.9346
+    - type: ap
+      value: 12.121605736777527
+    - type: f1
+      value: 50.169902005887955
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/tweet_sentiment_extraction
+      name: MTEB TweetSentimentExtractionClassification
+      config: default
+      split: test
+      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
+    metrics:
+    - type: accuracy
+      value: 56.72608941709111
+    - type: f1
+      value: 57.0702928875253
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/twentynewsgroups-clustering
+      name: MTEB TwentyNewsgroupsClustering
+      config: default
+      split: test
+      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
+    metrics:
+    - type: v_measure
+      value: 37.72671554400943
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twittersemeval2015-pairclassification
+      name: MTEB TwitterSemEval2015
+      config: default
+      split: test
+      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
+    metrics:
+    - type: cos_sim_accuracy
+      value: 82.84556237706384
+    - type: cos_sim_ap
+      value: 63.28364215788651
+    - type: cos_sim_f1
+      value: 60.00000000000001
+    - type: cos_sim_precision
+      value: 54.45161290322581
+    - type: cos_sim_recall
+      value: 66.80738786279683
+    - type: dot_accuracy
+      value: 82.84556237706384
+    - type: dot_ap
+      value: 63.28364302860433
+    - type: dot_f1
+      value: 60.00000000000001
+    - type: dot_precision
+      value: 54.45161290322581
+    - type: dot_recall
+      value: 66.80738786279683
+    - type: euclidean_accuracy
+      value: 82.84556237706384
+    - type: euclidean_ap
+      value: 63.28363625097978
+    - type: euclidean_f1
+      value: 60.00000000000001
+    - type: euclidean_precision
+      value: 54.45161290322581
+    - type: euclidean_recall
+      value: 66.80738786279683
+    - type: manhattan_accuracy
+      value: 82.86940454193241
+    - type: manhattan_ap
+      value: 63.244773709836764
+    - type: manhattan_f1
+      value: 60.12680942696495
+    - type: manhattan_precision
+      value: 55.00109433136353
+    - type: manhattan_recall
+      value: 66.3060686015831
+    - type: max_accuracy
+      value: 82.86940454193241
+    - type: max_ap
+      value: 63.28364302860433
+    - type: max_f1
+      value: 60.12680942696495
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twitterurlcorpus-pairclassification
+      name: MTEB TwitterURLCorpus
+      config: default
+      split: test
+      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
+    metrics:
+    - type: cos_sim_accuracy
+      value: 88.32033220786278
+    - type: cos_sim_ap
+      value: 84.71928176006863
+    - type: cos_sim_f1
+      value: 76.51483333969684
+    - type: cos_sim_precision
+      value: 75.89184276300841
+    - type: cos_sim_recall
+      value: 77.14813674160764
+    - type: dot_accuracy
+      value: 88.32033220786278
+    - type: dot_ap
+      value: 84.71928330149228
+    - type: dot_f1
+      value: 76.51483333969684
+    - type: dot_precision
+      value: 75.89184276300841
+    - type: dot_recall
+      value: 77.14813674160764
+    - type: euclidean_accuracy
+      value: 88.32033220786278
+    - type: euclidean_ap
+      value: 84.71928045384345
+    - type: euclidean_f1
+      value: 76.51483333969684
+    - type: euclidean_precision
+      value: 75.89184276300841
+    - type: euclidean_recall
+      value: 77.14813674160764
+    - type: manhattan_accuracy
+      value: 88.27570147863545
+    - type: manhattan_ap
+      value: 84.68523541579755
+    - type: manhattan_f1
+      value: 76.51512269355146
+    - type: manhattan_precision
+      value: 75.62608107091825
+    - type: manhattan_recall
+      value: 77.42531567600862
+    - type: max_accuracy
+      value: 88.32033220786278
+    - type: max_ap
+      value: 84.71928330149228
+    - type: max_f1
+      value: 76.51512269355146
+  - task:
+      type: Clustering
+    dataset:
+      type: jinaai/cities_wiki_clustering
+      name: MTEB WikiCitiesClustering
+      config: default
+      split: test
+      revision: ddc9ee9242fa65332597f70e967ecc38b9d734fa
+    metrics:
+    - type: v_measure
+      value: 85.30624598674467
+license: apache-2.0
+---
+---
+<h1 align="center">Snowflake's Artic-embed-s</h1>
+<h4 align="center">
+   <p>
+       <a href=#news>News</a> |
+       <a href=#models>Models</a> |
+       <a href=#usage>Usage</a>  |
+       <a href="#evaluation">Evaluation</a> |
+       <a href="#contact">Contact</a> |
+       <a href="#faq">FAQ</a>
+       <a href="#license">License</a> |
+       <a href="#acknowledgement">Acknowledgement</a>
+   <p>
+</h4>
+
+
+## News
+
+
+04/16/2024: Release the ** Arctic-embed ** family of text empedding models. The releases are state-of-the-art for Retrieval quality at each of their representative size profiles. [Technical Report]() is coming shortly. For more details, please refer to our Github: [Arctic-Text-Embed](https://github.com/Snowflake/Arctic-Text-Embed).
+
+
+## Models
+
+
+Arctic-Embed is a suite of text embedding models that focuses on creating high-quality retrieval models optimized for performance.
+
+
+The `arctic-embedding` models achieve **state-of-the-art performance on the MTEB/BEIR leaderboard** for each of their size variants. Evaluation is performed using these [scripts](https://github.com/Snowflake-Labs/arctic-embed/tree/main/src). As shown below, each class of model size achieves SOTA retrieval accuracy compared to other top models.
+
+
+The models are trained by leveraging existing open-source text representation models, such as bert-base-uncased, and are trained in a multi-stage pipeline to optimize their retrieval performance. First, the models are trained with large batches of query-document pairs where negatives are derived in-batch—pretraining leverages about 400m samples of a mix of public datasets and proprietary web search data. Following pretraining models are further optimized with long training on a smaller dataset (about 1m samples) of triplets of query, positive document, and negative document derived from hard harmful mining. Mining of the negatives and data curation is crucial to retrieval accuracy. A detailed technical report will be available shortly.
+
+
+| Name                                                                    | MTEB Retrieval Score (NDCG @ 10) | Parameters (Millions) | Embedding Dimension |
+| ----------------------------------------------------------------------- | -------------------------------- | --------------------- | ------------------- |
+| [arctic-embed-s](https://huggingface.co/Snowflake/arctic-embed-xs/)     | 50.15                            | 22                    | 384                 |
+| [arctic-embed-s](https://huggingface.co/Snowflake/arctic-embed-s/)      | 51.98                            | 33                    | 384                 |
+| [arctic-embed-s](https://huggingface.co/Snowflake/arctic-embed-m/)      | 54.90                            | 110                   | 768                 |
+| [arctic-embed-s](https://huggingface.co/Snowflake/arctic-embed-m-long/) | 54.83                            | 137                   | 768                 |
+| [arctic-embed-s](https://huggingface.co/Snowflake/arctic-embed-l/)      | 55.98                            | 335                   | 1024                |
+
+
+Aside from being great open-source models, the largest model, [arctic-embed-l](https://huggingface.co/Snowflake/arctic-embed-l/), can serve as a natural replacement for closed-source embedding, as shown below.
+
+
+| Model Name                                                         | MTEB Retrieval Score (NDCG @ 10) |
+| ------------------------------------------------------------------ | -------------------------------- |
+| [arctic-embed-s](https://huggingface.co/Snowflake/arctic-embed-l/) | 55.98                            |
+| Google-gecko-text-embedding                                        | 55.7                             |
+| text-embedding-3-large                                             | 55.44                            |
+| Cohere-embed-english-v3.0                                          | 55.00                            |
+| bge-large-en-v1.5                                                  | 54.29                            |
+
+
+### [arctic-embed-s](https://huggingface.co/Snowflake/arctic-embed-xs/)
+
+
+This tiny model packs quite the punch based on the [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) model. With only 22m parameters and 384 dimensions, this model should meet even the strictest latency/TCO budgets. Despite its size, its retrieval accuracy is closer to that of models with 100m paramers.
+
+
+| Model Name                                                          | MTEB Retrieval Score (NDCG @ 10) |
+| ------------------------------------------------------------------- | -------------------------------- |
+| [arctic-embed-s](https://huggingface.co/Snowflake/arctic-embed-xs/) | 50.15                            |
+| GIST-all-MiniLM-L6-v2                                               | 45.12                            |
+| gte-tiny                                                            | 44.92                            |
+| all-MiniLM-L6-v2                                                    | 41.95                            |
+| bge-micro-v2                                                        | 42.56                            |
+
+
+### Arctic-embed-m
+
+
+Based on the [all-MiniLM-L12-v2](https://huggingface.co/intfloat/e5-base-unsupervised) model, this small model does not trade off retrieval accuracy for its small size. With only 33m parameters and 384 dimensions, this model should easily allow scaling to large datasets.
+
+
+| Model Name                                                         | MTEB Retrieval Score (NDCG @ 10) |
+| ------------------------------------------------------------------ | -------------------------------- |
+| [arctic-embed-s](https://huggingface.co/Snowflake/arctic-embed-s/) | 51.98                            |
+| bge-small-en-v1.5                                                  | 51.68                            |
+| Cohere-embed-english-light-v3.0                                    | 51.34                            |
+| text-embedding-3-small                                             | 51.08                            |
+| e5-small-v2                                                        | 49.04                            |
+
+
+### [arctic-embed-s](https://huggingface.co/Snowflake/arctic-embed-m-long/)
+
+
+Based on the [nomic-embed-text-v1](https://huggingface.co/nomic-ai/nomic-embed-text-v1) model, this long-context variant of our medium-sized model is perfect for workloads that can be constrained by the regular 512 token context of our other models. Without the use of RPE, this model supports up to 2048 tokens. With RPE, it can scale to 8192!
+
+
+| Model Name                                                         | MTEB Retrieval Score (NDCG @ 10) |
+| ------------------------------------------------------------------ | -------------------------------- |
+| [arctic-embed-s](https://huggingface.co/Snowflake/arctic-embed-m/) | 54.90                            |
+| bge-base-en-v1.5                                                   | 53.25                            |
+| nomic-embed-text-v1.5                                              | 53.01                            |
+| GIST-Embedding-v0                                                  | 52.31                            |
+| gte-base                                                           | 52.31                            |
+
+
+### Arctic-embed-m
+
+
+Based on the [intfloat/e5-base-unsupervised](https://huggingface.co/intfloat/e5-base-unsupervised) model, this medium model is the workhorse that provides the best retrieval performance without slowing down inference.
+
+
+| Model Name                                                         | MTEB Retrieval Score (NDCG @ 10) |
+| ------------------------------------------------------------------ | -------------------------------- |
+| [arctic-embed-s](https://huggingface.co/Snowflake/arctic-embed-m/) | 54.90                            |
+| bge-base-en-v1.5                                                   | 53.25                            |
+| nomic-embed-text-v1.5                                              | 53.25                            |
+| GIST-Embedding-v0                                                  | 52.31                            |
+| gte-base                                                           | 52.31                            |
+
+
+### [arctic-embed-s](https://huggingface.co/Snowflake/arctic-embed-l/)
+
+
+Based on the [intfloat/e5-large-unsupervised](https://huggingface.co/intfloat/e5-large-unsupervised) model, this small model does not sacrifice retrieval accuracy for its small size.
+
+
+| Model Name                                                         | MTEB Retrieval Score (NDCG @ 10) |
+| ------------------------------------------------------------------ | -------------------------------- |
+| [arctic-embed-s](https://huggingface.co/Snowflake/arctic-embed-l/) | 55.98                            |
+| UAE-Large-V1                                                       | 54.66                            |
+| bge-large-en-v1.5                                                  | 54.29                            |
+| mxbai-embed-large-v1                                               | 54.39                            |
+| e5-Large-v2                                                        | 50.56                            |
+
+
+## Usage
+
+
+### Using Huggingface transformers
+
+
+You can use the transformers package to use an arctic-embed model, as shown below. For optimal retrieval quality, use the CLS token to embed each text portion and use the query prefix below (just on the query).
+
+
+
+```python
+import torch
+from transformers import AutoModel, AutoTokenizer
+
+tokenizer = AutoTokenizer.from_pretrained('Snowflake/arctic-embed-')
+model = AutoModel.from_pretrained('Snowflake/arctic-embed-s', add_pooling_layer=False)
+model.eval()
+
+query_prefix = 'Represent this sentence for searching relevant passages: '
+queries  = ['what is snowflake?', 'Where can I get the best tacos?']
+queries_with_prefix = ["{}{}".format(query_prefix, i) for i in queries]
+query_tokens = tokenizer(queries_with_prefix, padding=True, truncation=True, return_tensors='pt', max_length=512)
+
+documents = ['The Data Cloud!', 'Mexico City of Course!']
+document_tokens =  tokenizer(documents, padding=True, truncation=True, return_tensors='pt', max_length=512)
+
+# Compute token embeddings
+with torch.no_grad():
+    query_embeddings = model(**query_tokens)[0][:, 0]
+    doument_embeddings = model(**document_tokens)[0][:, 0]
+
+
+# normalize embeddings
+query_embeddings = torch.nn.functional.normalize(query_embeddings, p=2, dim=1)
+doument_embeddings = torch.nn.functional.normalize(doument_embeddings, p=2, dim=1)
+
+scores = torch.mm(query_embeddings, doument_embeddings.transpose(0, 1))
+for query, query_scores in zip(queries, scores):
+    doc_score_pairs = list(zip(documents, query_scores))
+    doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
+    #Output passages & scores
+    print("Query:", query)
+    for document, score in doc_score_pairs:
+        print(score, document)
+```
+
+
+If you use the long context model with more than 2048 tokens, ensure that you initialize the model like below instead. This will use [RPE](https://arxiv.org/abs/2104.09864) to allow up to 8192 tokens.
+
+
+``` py
+model = AutoModel.from_pretrained('Snowflake/arctic-embed-m-long', trust_remote_code=True, rotary_scaling_factor=2)
+```
+
+
+## FAQ
+
+
+TBD
+
+
+## Contact
+
+
+Feel free to open an issue or pull request if you have any questions or suggestions about this project.
+You also can email Daniel Campos([email protected]).
+
+
+## License
+
+
+Arctic is licensed under the [Apache-2](https://www.apache.org/licenses/LICENSE-2.0). The released models can be used for commercial purposes free of charge.
+
+
+## Acknowledgement
+
+
+We want to thank the open-source community, which has provided the great building blocks upon which we could make our models.
+We thank our modeling engineers, Danmei Xu, Luke Merrick, Gaurav Nuti, and Daniel Campos, for making these great models possible. 
+We thank our leadership, Himabindu Pucha, Kelvin So, Vivek Raghunathan, and Sridhar Ramaswamy, for supporting this work. 
+We also thank the open-source community for producing the great models we could build on top of and making these releases possible. 
+Finally, we thank the researchers who created BEIR and MTEB benchmarks. 
+It is largely thanks to their tireless work to define what better looks like that we could improve model performance.