diff --git "a/README.md" "b/README.md"
--- "a/README.md"
+++ "b/README.md"
@@ -1,199 +1,3034 @@
 ---
-library_name: transformers
-tags: []
+library_name: sentence-transformers
+pipeline_tag: sentence-similarity
+tags:
+- mteb
+model-index:
+- name: merged_model
+  results:
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_counterfactual
+      name: MTEB AmazonCounterfactualClassification (en)
+      config: en
+      split: test
+      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
+    metrics:
+    - type: accuracy
+      value: 75.17910447761193
+    - type: ap
+      value: 37.9385904323946
+    - type: f1
+      value: 69.08121471841274
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_polarity
+      name: MTEB AmazonPolarityClassification
+      config: default
+      split: test
+      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
+    metrics:
+    - type: accuracy
+      value: 93.07292500000001
+    - type: ap
+      value: 89.99875359715712
+    - type: f1
+      value: 93.06135402357953
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_reviews_multi
+      name: MTEB AmazonReviewsClassification (en)
+      config: en
+      split: test
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+    metrics:
+    - type: accuracy
+      value: 48.42400000000001
+    - type: f1
+      value: 47.95385391493928
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/arguana
+      name: MTEB ArguAna
+      config: default
+      split: test
+      revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
+    metrics:
+    - type: map_at_1
+      value: 41.394
+    - type: map_at_10
+      value: 57.86900000000001
+    - type: map_at_100
+      value: 58.372
+    - type: map_at_1000
+      value: 58.374
+    - type: map_at_20
+      value: 58.321
+    - type: map_at_3
+      value: 53.793
+    - type: map_at_5
+      value: 56.443
+    - type: mrr_at_1
+      value: 42.745
+    - type: mrr_at_10
+      value: 58.392999999999994
+    - type: mrr_at_100
+      value: 58.887
+    - type: mrr_at_1000
+      value: 58.89
+    - type: mrr_at_20
+      value: 58.836
+    - type: mrr_at_3
+      value: 54.291
+    - type: mrr_at_5
+      value: 56.958
+    - type: ndcg_at_1
+      value: 41.394
+    - type: ndcg_at_10
+      value: 65.989
+    - type: ndcg_at_100
+      value: 67.896
+    - type: ndcg_at_1000
+      value: 67.955
+    - type: ndcg_at_20
+      value: 67.545
+    - type: ndcg_at_3
+      value: 57.859
+    - type: ndcg_at_5
+      value: 62.602999999999994
+    - type: precision_at_1
+      value: 41.394
+    - type: precision_at_10
+      value: 9.139
+    - type: precision_at_100
+      value: 0.992
+    - type: precision_at_1000
+      value: 0.1
+    - type: precision_at_20
+      value: 4.868
+    - type: precision_at_3
+      value: 23.21
+    - type: precision_at_5
+      value: 16.216
+    - type: recall_at_1
+      value: 41.394
+    - type: recall_at_10
+      value: 91.39399999999999
+    - type: recall_at_100
+      value: 99.21799999999999
+    - type: recall_at_1000
+      value: 99.644
+    - type: recall_at_20
+      value: 97.368
+    - type: recall_at_3
+      value: 69.63000000000001
+    - type: recall_at_5
+      value: 81.081
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-p2p
+      name: MTEB ArxivClusteringP2P
+      config: default
+      split: test
+      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
+    metrics:
+    - type: v_measure
+      value: 48.65949563592336
+    - type: v_measures
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+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-s2s
+      name: MTEB ArxivClusteringS2S
+      config: default
+      split: test
+      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
+    metrics:
+    - type: v_measure
+      value: 42.81101867573718
+    - type: v_measures
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+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/askubuntudupquestions-reranking
+      name: MTEB AskUbuntuDupQuestions
+      config: default
+      split: test
+      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
+    metrics:
+    - type: map
+      value: 64.42483953378505
+    - type: mrr
+      value: 77.80525876093743
+  - task:
+      type: STS
+    dataset:
+      type: mteb/biosses-sts
+      name: MTEB BIOSSES
+      config: default
+      split: test
+      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
+    metrics:
+    - type: cos_sim_pearson
+      value: 90.04392169216328
+    - type: cos_sim_spearman
+      value: 89.14721200259248
+    - type: euclidean_pearson
+      value: 87.49074189687103
+    - type: euclidean_spearman
+      value: 88.46828087003544
+    - type: manhattan_pearson
+      value: 87.30286329712442
+    - type: manhattan_spearman
+      value: 88.2580351155879
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/banking77
+      name: MTEB Banking77Classification
+      config: default
+      split: test
+      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
+    metrics:
+    - type: accuracy
+      value: 88.03246753246754
+    - type: f1
+      value: 88.01410778743103
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-p2p
+      name: MTEB BiorxivClusteringP2P
+      config: default
+      split: test
+      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
+    metrics:
+    - type: v_measure
+      value: 39.80502915453793
+    - type: v_measures
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+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-s2s
+      name: MTEB BiorxivClusteringS2S
+      config: default
+      split: test
+      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
+    metrics:
+    - type: v_measure
+      value: 36.627004544222814
+    - type: v_measures
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+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/cqadupstack-android
+      name: MTEB CQADupstackAndroidRetrieval
+      config: default
+      split: test
+      revision: f46a197baaae43b4f621051089b82a364682dfeb
+    metrics:
+    - type: map_at_1
+      value: 34.902
+    - type: map_at_10
+      value: 46.548
+    - type: map_at_100
+      value: 48.209
+    - type: map_at_1000
+      value: 48.327999999999996
+    - type: map_at_20
+      value: 47.488
+    - type: map_at_3
+      value: 42.844
+    - type: map_at_5
+      value: 44.849
+    - type: mrr_at_1
+      value: 42.632
+    - type: mrr_at_10
+      value: 53.03600000000001
+    - type: mrr_at_100
+      value: 53.749
+    - type: mrr_at_1000
+      value: 53.788000000000004
+    - type: mrr_at_20
+      value: 53.461999999999996
+    - type: mrr_at_3
+      value: 50.548
+    - type: mrr_at_5
+      value: 52.029
+    - type: ndcg_at_1
+      value: 42.632
+    - type: ndcg_at_10
+      value: 53.099
+    - type: ndcg_at_100
+      value: 58.568
+    - type: ndcg_at_1000
+      value: 60.245000000000005
+    - type: ndcg_at_20
+      value: 55.379
+    - type: ndcg_at_3
+      value: 48.211
+    - type: ndcg_at_5
+      value: 50.375
+    - type: precision_at_1
+      value: 42.632
+    - type: precision_at_10
+      value: 10.129000000000001
+    - type: precision_at_100
+      value: 1.6219999999999999
+    - type: precision_at_1000
+      value: 0.207
+    - type: precision_at_20
+      value: 6.116
+    - type: precision_at_3
+      value: 23.033
+    - type: precision_at_5
+      value: 16.509
+    - type: recall_at_1
+      value: 34.902
+    - type: recall_at_10
+      value: 64.761
+    - type: recall_at_100
+      value: 87.15
+    - type: recall_at_1000
+      value: 97.479
+    - type: recall_at_20
+      value: 72.775
+    - type: recall_at_3
+      value: 50.4
+    - type: recall_at_5
+      value: 56.711
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/cqadupstack-english
+      name: MTEB CQADupstackEnglishRetrieval
+      config: default
+      split: test
+      revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
+    metrics:
+    - type: map_at_1
+      value: 32.266
+    - type: map_at_10
+      value: 43.149
+    - type: map_at_100
+      value: 44.416
+    - type: map_at_1000
+      value: 44.545
+    - type: map_at_20
+      value: 43.829
+    - type: map_at_3
+      value: 39.995000000000005
+    - type: map_at_5
+      value: 41.737
+    - type: mrr_at_1
+      value: 40.0
+    - type: mrr_at_10
+      value: 48.921
+    - type: mrr_at_100
+      value: 49.54
+    - type: mrr_at_1000
+      value: 49.583
+    - type: mrr_at_20
+      value: 49.289
+    - type: mrr_at_3
+      value: 46.73
+    - type: mrr_at_5
+      value: 48.036
+    - type: ndcg_at_1
+      value: 40.0
+    - type: ndcg_at_10
+      value: 48.927
+    - type: ndcg_at_100
+      value: 53.222
+    - type: ndcg_at_1000
+      value: 55.202
+    - type: ndcg_at_20
+      value: 50.585
+    - type: ndcg_at_3
+      value: 44.777
+    - type: ndcg_at_5
+      value: 46.648
+    - type: precision_at_1
+      value: 40.0
+    - type: precision_at_10
+      value: 9.312
+    - type: precision_at_100
+      value: 1.48
+    - type: precision_at_1000
+      value: 0.19499999999999998
+    - type: precision_at_20
+      value: 5.4239999999999995
+    - type: precision_at_3
+      value: 21.656
+    - type: precision_at_5
+      value: 15.338
+    - type: recall_at_1
+      value: 32.266
+    - type: recall_at_10
+      value: 58.904999999999994
+    - type: recall_at_100
+      value: 77.057
+    - type: recall_at_1000
+      value: 89.517
+    - type: recall_at_20
+      value: 65.059
+    - type: recall_at_3
+      value: 46.601
+    - type: recall_at_5
+      value: 51.93600000000001
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/cqadupstack-gaming
+      name: MTEB CQADupstackGamingRetrieval
+      config: default
+      split: test
+      revision: 4885aa143210c98657558c04aaf3dc47cfb54340
+    metrics:
+    - type: map_at_1
+      value: 40.876000000000005
+    - type: map_at_10
+      value: 54.445
+    - type: map_at_100
+      value: 55.434000000000005
+    - type: map_at_1000
+      value: 55.486000000000004
+    - type: map_at_20
+      value: 55.089
+    - type: map_at_3
+      value: 50.751999999999995
+    - type: map_at_5
+      value: 52.905
+    - type: mrr_at_1
+      value: 46.583000000000006
+    - type: mrr_at_10
+      value: 57.55200000000001
+    - type: mrr_at_100
+      value: 58.165
+    - type: mrr_at_1000
+      value: 58.192
+    - type: mrr_at_20
+      value: 57.958
+    - type: mrr_at_3
+      value: 54.932
+    - type: mrr_at_5
+      value: 56.584
+    - type: ndcg_at_1
+      value: 46.583000000000006
+    - type: ndcg_at_10
+      value: 60.711999999999996
+    - type: ndcg_at_100
+      value: 64.35499999999999
+    - type: ndcg_at_1000
+      value: 65.348
+    - type: ndcg_at_20
+      value: 62.499
+    - type: ndcg_at_3
+      value: 54.681000000000004
+    - type: ndcg_at_5
+      value: 57.782
+    - type: precision_at_1
+      value: 46.583000000000006
+    - type: precision_at_10
+      value: 9.937
+    - type: precision_at_100
+      value: 1.265
+    - type: precision_at_1000
+      value: 0.13899999999999998
+    - type: precision_at_20
+      value: 5.536
+    - type: precision_at_3
+      value: 24.66
+    - type: precision_at_5
+      value: 17.041
+    - type: recall_at_1
+      value: 40.876000000000005
+    - type: recall_at_10
+      value: 75.967
+    - type: recall_at_100
+      value: 91.335
+    - type: recall_at_1000
+      value: 98.339
+    - type: recall_at_20
+      value: 82.514
+    - type: recall_at_3
+      value: 59.917
+    - type: recall_at_5
+      value: 67.57600000000001
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/cqadupstack-gis
+      name: MTEB CQADupstackGisRetrieval
+      config: default
+      split: test
+      revision: 5003b3064772da1887988e05400cf3806fe491f2
+    metrics:
+    - type: map_at_1
+      value: 27.834999999999997
+    - type: map_at_10
+      value: 37.159
+    - type: map_at_100
+      value: 38.211
+    - type: map_at_1000
+      value: 38.278
+    - type: map_at_20
+      value: 37.785999999999994
+    - type: map_at_3
+      value: 34.297
+    - type: map_at_5
+      value: 35.876999999999995
+    - type: mrr_at_1
+      value: 30.169
+    - type: mrr_at_10
+      value: 39.257999999999996
+    - type: mrr_at_100
+      value: 40.193
+    - type: mrr_at_1000
+      value: 40.243
+    - type: mrr_at_20
+      value: 39.843
+    - type: mrr_at_3
+      value: 36.685
+    - type: mrr_at_5
+      value: 38.126
+    - type: ndcg_at_1
+      value: 30.169
+    - type: ndcg_at_10
+      value: 42.436
+    - type: ndcg_at_100
+      value: 47.519
+    - type: ndcg_at_1000
+      value: 49.28
+    - type: ndcg_at_20
+      value: 44.629000000000005
+    - type: ndcg_at_3
+      value: 36.942
+    - type: ndcg_at_5
+      value: 39.543
+    - type: precision_at_1
+      value: 30.169
+    - type: precision_at_10
+      value: 6.531000000000001
+    - type: precision_at_100
+      value: 0.951
+    - type: precision_at_1000
+      value: 0.11399999999999999
+    - type: precision_at_20
+      value: 3.763
+    - type: precision_at_3
+      value: 15.706000000000001
+    - type: precision_at_5
+      value: 10.938
+    - type: recall_at_1
+      value: 27.834999999999997
+    - type: recall_at_10
+      value: 56.716
+    - type: recall_at_100
+      value: 79.85
+    - type: recall_at_1000
+      value: 93.03399999999999
+    - type: recall_at_20
+      value: 65.076
+    - type: recall_at_3
+      value: 41.784
+    - type: recall_at_5
+      value: 48.031
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/cqadupstack-mathematica
+      name: MTEB CQADupstackMathematicaRetrieval
+      config: default
+      split: test
+      revision: 90fceea13679c63fe563ded68f3b6f06e50061de
+    metrics:
+    - type: map_at_1
+      value: 18.941
+    - type: map_at_10
+      value: 27.881
+    - type: map_at_100
+      value: 29.085
+    - type: map_at_1000
+      value: 29.211
+    - type: map_at_20
+      value: 28.493000000000002
+    - type: map_at_3
+      value: 24.959999999999997
+    - type: map_at_5
+      value: 26.604
+    - type: mrr_at_1
+      value: 23.383000000000003
+    - type: mrr_at_10
+      value: 32.849000000000004
+    - type: mrr_at_100
+      value: 33.732
+    - type: mrr_at_1000
+      value: 33.803
+    - type: mrr_at_20
+      value: 33.347
+    - type: mrr_at_3
+      value: 30.037000000000003
+    - type: mrr_at_5
+      value: 31.555
+    - type: ndcg_at_1
+      value: 23.383000000000003
+    - type: ndcg_at_10
+      value: 33.585
+    - type: ndcg_at_100
+      value: 39.187
+    - type: ndcg_at_1000
+      value: 41.993
+    - type: ndcg_at_20
+      value: 35.582
+    - type: ndcg_at_3
+      value: 28.258
+    - type: ndcg_at_5
+      value: 30.714999999999996
+    - type: precision_at_1
+      value: 23.383000000000003
+    - type: precision_at_10
+      value: 6.182
+    - type: precision_at_100
+      value: 1.04
+    - type: precision_at_1000
+      value: 0.14200000000000002
+    - type: precision_at_20
+      value: 3.675
+    - type: precision_at_3
+      value: 13.639999999999999
+    - type: precision_at_5
+      value: 9.950000000000001
+    - type: recall_at_1
+      value: 18.941
+    - type: recall_at_10
+      value: 46.225
+    - type: recall_at_100
+      value: 70.416
+    - type: recall_at_1000
+      value: 90.252
+    - type: recall_at_20
+      value: 53.198
+    - type: recall_at_3
+      value: 31.483
+    - type: recall_at_5
+      value: 37.774
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/cqadupstack-physics
+      name: MTEB CQADupstackPhysicsRetrieval
+      config: default
+      split: test
+      revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
+    metrics:
+    - type: map_at_1
+      value: 32.190000000000005
+    - type: map_at_10
+      value: 43.183
+    - type: map_at_100
+      value: 44.467
+    - type: map_at_1000
+      value: 44.580999999999996
+    - type: map_at_20
+      value: 43.874
+    - type: map_at_3
+      value: 39.672000000000004
+    - type: map_at_5
+      value: 41.719
+    - type: mrr_at_1
+      value: 39.461
+    - type: mrr_at_10
+      value: 48.903999999999996
+    - type: mrr_at_100
+      value: 49.688
+    - type: mrr_at_1000
+      value: 49.729
+    - type: mrr_at_20
+      value: 49.349
+    - type: mrr_at_3
+      value: 46.439
+    - type: mrr_at_5
+      value: 47.964
+    - type: ndcg_at_1
+      value: 39.461
+    - type: ndcg_at_10
+      value: 49.307
+    - type: ndcg_at_100
+      value: 54.544000000000004
+    - type: ndcg_at_1000
+      value: 56.499
+    - type: ndcg_at_20
+      value: 51.356
+    - type: ndcg_at_3
+      value: 43.956
+    - type: ndcg_at_5
+      value: 46.662
+    - type: precision_at_1
+      value: 39.461
+    - type: precision_at_10
+      value: 8.826
+    - type: precision_at_100
+      value: 1.323
+    - type: precision_at_1000
+      value: 0.168
+    - type: precision_at_20
+      value: 5.125
+    - type: precision_at_3
+      value: 20.629
+    - type: precision_at_5
+      value: 14.745
+    - type: recall_at_1
+      value: 32.190000000000005
+    - type: recall_at_10
+      value: 61.792
+    - type: recall_at_100
+      value: 83.543
+    - type: recall_at_1000
+      value: 96.009
+    - type: recall_at_20
+      value: 68.941
+    - type: recall_at_3
+      value: 46.918
+    - type: recall_at_5
+      value: 53.909
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/cqadupstack-programmers
+      name: MTEB CQADupstackProgrammersRetrieval
+      config: default
+      split: test
+      revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
+    metrics:
+    - type: map_at_1
+      value: 26.137
+    - type: map_at_10
+      value: 37.025999999999996
+    - type: map_at_100
+      value: 38.511
+    - type: map_at_1000
+      value: 38.619
+    - type: map_at_20
+      value: 37.92
+    - type: map_at_3
+      value: 33.729
+    - type: map_at_5
+      value: 35.478
+    - type: mrr_at_1
+      value: 32.192
+    - type: mrr_at_10
+      value: 42.245
+    - type: mrr_at_100
+      value: 43.172
+    - type: mrr_at_1000
+      value: 43.225
+    - type: mrr_at_20
+      value: 42.855
+    - type: mrr_at_3
+      value: 39.669
+    - type: mrr_at_5
+      value: 41.038999999999994
+    - type: ndcg_at_1
+      value: 32.192
+    - type: ndcg_at_10
+      value: 43.132
+    - type: ndcg_at_100
+      value: 49.09
+    - type: ndcg_at_1000
+      value: 51.248000000000005
+    - type: ndcg_at_20
+      value: 45.802
+    - type: ndcg_at_3
+      value: 37.796
+    - type: ndcg_at_5
+      value: 40.064
+    - type: precision_at_1
+      value: 32.192
+    - type: precision_at_10
+      value: 8.071
+    - type: precision_at_100
+      value: 1.275
+    - type: precision_at_1000
+      value: 0.164
+    - type: precision_at_20
+      value: 4.869
+    - type: precision_at_3
+      value: 18.189
+    - type: precision_at_5
+      value: 13.059000000000001
+    - type: recall_at_1
+      value: 26.137
+    - type: recall_at_10
+      value: 55.87
+    - type: recall_at_100
+      value: 80.868
+    - type: recall_at_1000
+      value: 95.298
+    - type: recall_at_20
+      value: 65.365
+    - type: recall_at_3
+      value: 41.074
+    - type: recall_at_5
+      value: 46.945
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/cqadupstack
+      name: MTEB CQADupstackRetrieval
+      config: default
+      split: test
+      revision: 160c094312a0e1facb97e55eeddb698c0abe3571
+    metrics:
+    - type: map_at_1
+      value: 27.92966666666667
+    - type: map_at_10
+      value: 37.75758333333333
+    - type: map_at_100
+      value: 38.996750000000006
+    - type: map_at_1000
+      value: 39.10941666666666
+    - type: map_at_20
+      value: 38.44558333333334
+    - type: map_at_3
+      value: 34.70758333333333
+    - type: map_at_5
+      value: 36.39783333333333
+    - type: mrr_at_1
+      value: 33.07458333333333
+    - type: mrr_at_10
+      value: 42.112750000000005
+    - type: mrr_at_100
+      value: 42.94625
+    - type: mrr_at_1000
+      value: 42.998000000000005
+    - type: mrr_at_20
+      value: 42.61133333333333
+    - type: mrr_at_3
+      value: 39.65641666666667
+    - type: mrr_at_5
+      value: 41.06275
+    - type: ndcg_at_1
+      value: 33.07458333333333
+    - type: ndcg_at_10
+      value: 43.39091666666667
+    - type: ndcg_at_100
+      value: 48.568916666666674
+    - type: ndcg_at_1000
+      value: 50.666
+    - type: ndcg_at_20
+      value: 45.44491666666668
+    - type: ndcg_at_3
+      value: 38.349833333333336
+    - type: ndcg_at_5
+      value: 40.70983333333333
+    - type: precision_at_1
+      value: 33.07458333333333
+    - type: precision_at_10
+      value: 7.6090833333333325
+    - type: precision_at_100
+      value: 1.205
+    - type: precision_at_1000
+      value: 0.15808333333333335
+    - type: precision_at_20
+      value: 4.48525
+    - type: precision_at_3
+      value: 17.66225
+    - type: precision_at_5
+      value: 12.545833333333334
+    - type: recall_at_1
+      value: 27.92966666666667
+    - type: recall_at_10
+      value: 55.657999999999994
+    - type: recall_at_100
+      value: 78.20633333333335
+    - type: recall_at_1000
+      value: 92.58875
+    - type: recall_at_20
+      value: 63.13408333333332
+    - type: recall_at_3
+      value: 41.67841666666667
+    - type: recall_at_5
+      value: 47.74058333333333
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/cqadupstack-stats
+      name: MTEB CQADupstackStatsRetrieval
+      config: default
+      split: test
+      revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
+    metrics:
+    - type: map_at_1
+      value: 27.488
+    - type: map_at_10
+      value: 34.160000000000004
+    - type: map_at_100
+      value: 35.036
+    - type: map_at_1000
+      value: 35.125
+    - type: map_at_20
+      value: 34.594
+    - type: map_at_3
+      value: 31.941000000000003
+    - type: map_at_5
+      value: 33.007
+    - type: mrr_at_1
+      value: 31.288
+    - type: mrr_at_10
+      value: 37.345
+    - type: mrr_at_100
+      value: 38.079
+    - type: mrr_at_1000
+      value: 38.141999999999996
+    - type: mrr_at_20
+      value: 37.749
+    - type: mrr_at_3
+      value: 35.327
+    - type: mrr_at_5
+      value: 36.301
+    - type: ndcg_at_1
+      value: 31.288
+    - type: ndcg_at_10
+      value: 38.415
+    - type: ndcg_at_100
+      value: 43.018
+    - type: ndcg_at_1000
+      value: 45.322
+    - type: ndcg_at_20
+      value: 39.921
+    - type: ndcg_at_3
+      value: 34.176
+    - type: ndcg_at_5
+      value: 35.827
+    - type: precision_at_1
+      value: 31.288
+    - type: precision_at_10
+      value: 5.844
+    - type: precision_at_100
+      value: 0.91
+    - type: precision_at_1000
+      value: 0.117
+    - type: precision_at_20
+      value: 3.351
+    - type: precision_at_3
+      value: 14.315
+    - type: precision_at_5
+      value: 9.693
+    - type: recall_at_1
+      value: 27.488
+    - type: recall_at_10
+      value: 48.777
+    - type: recall_at_100
+      value: 70.253
+    - type: recall_at_1000
+      value: 87.455
+    - type: recall_at_20
+      value: 54.309
+    - type: recall_at_3
+      value: 36.791000000000004
+    - type: recall_at_5
+      value: 40.938
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/cqadupstack-tex
+      name: MTEB CQADupstackTexRetrieval
+      config: default
+      split: test
+      revision: 46989137a86843e03a6195de44b09deda022eec7
+    metrics:
+    - type: map_at_1
+      value: 19.085
+    - type: map_at_10
+      value: 26.579000000000004
+    - type: map_at_100
+      value: 27.814
+    - type: map_at_1000
+      value: 27.939000000000004
+    - type: map_at_20
+      value: 27.232
+    - type: map_at_3
+      value: 24.008
+    - type: map_at_5
+      value: 25.436999999999998
+    - type: mrr_at_1
+      value: 23.159
+    - type: mrr_at_10
+      value: 30.622
+    - type: mrr_at_100
+      value: 31.631999999999998
+    - type: mrr_at_1000
+      value: 31.705
+    - type: mrr_at_20
+      value: 31.186999999999998
+    - type: mrr_at_3
+      value: 28.292
+    - type: mrr_at_5
+      value: 29.669
+    - type: ndcg_at_1
+      value: 23.159
+    - type: ndcg_at_10
+      value: 31.422
+    - type: ndcg_at_100
+      value: 37.246
+    - type: ndcg_at_1000
+      value: 40.014
+    - type: ndcg_at_20
+      value: 33.568999999999996
+    - type: ndcg_at_3
+      value: 26.893
+    - type: ndcg_at_5
+      value: 29.048000000000002
+    - type: precision_at_1
+      value: 23.159
+    - type: precision_at_10
+      value: 5.736
+    - type: precision_at_100
+      value: 1.013
+    - type: precision_at_1000
+      value: 0.14300000000000002
+    - type: precision_at_20
+      value: 3.4840000000000004
+    - type: precision_at_3
+      value: 12.617999999999999
+    - type: precision_at_5
+      value: 9.195
+    - type: recall_at_1
+      value: 19.085
+    - type: recall_at_10
+      value: 41.881
+    - type: recall_at_100
+      value: 68.026
+    - type: recall_at_1000
+      value: 87.576
+    - type: recall_at_20
+      value: 49.886
+    - type: recall_at_3
+      value: 29.355999999999998
+    - type: recall_at_5
+      value: 34.946
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/cqadupstack-unix
+      name: MTEB CQADupstackUnixRetrieval
+      config: default
+      split: test
+      revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
+    metrics:
+    - type: map_at_1
+      value: 28.052
+    - type: map_at_10
+      value: 37.942
+    - type: map_at_100
+      value: 39.11
+    - type: map_at_1000
+      value: 39.204
+    - type: map_at_20
+      value: 38.592
+    - type: map_at_3
+      value: 35.149
+    - type: map_at_5
+      value: 36.636
+    - type: mrr_at_1
+      value: 33.022
+    - type: mrr_at_10
+      value: 42.13
+    - type: mrr_at_100
+      value: 42.992000000000004
+    - type: mrr_at_1000
+      value: 43.045
+    - type: mrr_at_20
+      value: 42.653
+    - type: mrr_at_3
+      value: 39.754
+    - type: mrr_at_5
+      value: 41.046
+    - type: ndcg_at_1
+      value: 33.022
+    - type: ndcg_at_10
+      value: 43.588
+    - type: ndcg_at_100
+      value: 48.844
+    - type: ndcg_at_1000
+      value: 50.87199999999999
+    - type: ndcg_at_20
+      value: 45.634
+    - type: ndcg_at_3
+      value: 38.653
+    - type: ndcg_at_5
+      value: 40.827000000000005
+    - type: precision_at_1
+      value: 33.022
+    - type: precision_at_10
+      value: 7.239
+    - type: precision_at_100
+      value: 1.126
+    - type: precision_at_1000
+      value: 0.14100000000000001
+    - type: precision_at_20
+      value: 4.2299999999999995
+    - type: precision_at_3
+      value: 17.755000000000003
+    - type: precision_at_5
+      value: 12.239
+    - type: recall_at_1
+      value: 28.052
+    - type: recall_at_10
+      value: 56.518
+    - type: recall_at_100
+      value: 79.081
+    - type: recall_at_1000
+      value: 93.096
+    - type: recall_at_20
+      value: 63.65
+    - type: recall_at_3
+      value: 43.061
+    - type: recall_at_5
+      value: 48.588
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/cqadupstack-webmasters
+      name: MTEB CQADupstackWebmastersRetrieval
+      config: default
+      split: test
+      revision: 160c094312a0e1facb97e55eeddb698c0abe3571
+    metrics:
+    - type: map_at_1
+      value: 24.698
+    - type: map_at_10
+      value: 34.162
+    - type: map_at_100
+      value: 35.862
+    - type: map_at_1000
+      value: 36.087
+    - type: map_at_20
+      value: 35.049
+    - type: map_at_3
+      value: 31.172
+    - type: map_at_5
+      value: 32.814
+    - type: mrr_at_1
+      value: 30.237000000000002
+    - type: mrr_at_10
+      value: 39.461
+    - type: mrr_at_100
+      value: 40.514
+    - type: mrr_at_1000
+      value: 40.552
+    - type: mrr_at_20
+      value: 40.091
+    - type: mrr_at_3
+      value: 37.088
+    - type: mrr_at_5
+      value: 38.383
+    - type: ndcg_at_1
+      value: 30.237000000000002
+    - type: ndcg_at_10
+      value: 40.308
+    - type: ndcg_at_100
+      value: 46.792
+    - type: ndcg_at_1000
+      value: 48.931999999999995
+    - type: ndcg_at_20
+      value: 42.748999999999995
+    - type: ndcg_at_3
+      value: 35.541
+    - type: ndcg_at_5
+      value: 37.812
+    - type: precision_at_1
+      value: 30.237000000000002
+    - type: precision_at_10
+      value: 7.846
+    - type: precision_at_100
+      value: 1.599
+    - type: precision_at_1000
+      value: 0.247
+    - type: precision_at_20
+      value: 4.96
+    - type: precision_at_3
+      value: 16.93
+    - type: precision_at_5
+      value: 12.49
+    - type: recall_at_1
+      value: 24.698
+    - type: recall_at_10
+      value: 51.74999999999999
+    - type: recall_at_100
+      value: 80.767
+    - type: recall_at_1000
+      value: 93.569
+    - type: recall_at_20
+      value: 61.157
+    - type: recall_at_3
+      value: 38.344
+    - type: recall_at_5
+      value: 44.184
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/cqadupstack-wordpress
+      name: MTEB CQADupstackWordpressRetrieval
+      config: default
+      split: test
+      revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
+    metrics:
+    - type: map_at_1
+      value: 22.686
+    - type: map_at_10
+      value: 30.857
+    - type: map_at_100
+      value: 31.806
+    - type: map_at_1000
+      value: 31.91
+    - type: map_at_20
+      value: 31.401
+    - type: map_at_3
+      value: 27.972
+    - type: map_at_5
+      value: 29.711
+    - type: mrr_at_1
+      value: 24.769
+    - type: mrr_at_10
+      value: 33.03
+    - type: mrr_at_100
+      value: 33.899
+    - type: mrr_at_1000
+      value: 33.969
+    - type: mrr_at_20
+      value: 33.553
+    - type: mrr_at_3
+      value: 30.375999999999998
+    - type: mrr_at_5
+      value: 32.021
+    - type: ndcg_at_1
+      value: 24.769
+    - type: ndcg_at_10
+      value: 35.76
+    - type: ndcg_at_100
+      value: 40.442
+    - type: ndcg_at_1000
+      value: 43.037
+    - type: ndcg_at_20
+      value: 37.634
+    - type: ndcg_at_3
+      value: 30.314000000000004
+    - type: ndcg_at_5
+      value: 33.215
+    - type: precision_at_1
+      value: 24.769
+    - type: precision_at_10
+      value: 5.656
+    - type: precision_at_100
+      value: 0.856
+    - type: precision_at_1000
+      value: 0.12
+    - type: precision_at_20
+      value: 3.29
+    - type: precision_at_3
+      value: 12.815999999999999
+    - type: precision_at_5
+      value: 9.353
+    - type: recall_at_1
+      value: 22.686
+    - type: recall_at_10
+      value: 48.734
+    - type: recall_at_100
+      value: 70.13000000000001
+    - type: recall_at_1000
+      value: 89.441
+    - type: recall_at_20
+      value: 55.679
+    - type: recall_at_3
+      value: 34.412
+    - type: recall_at_5
+      value: 41.349000000000004
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/climate-fever
+      name: MTEB ClimateFEVER
+      config: default
+      split: test
+      revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
+    metrics:
+    - type: map_at_1
+      value: 12.842999999999998
+    - type: map_at_10
+      value: 21.776999999999997
+    - type: map_at_100
+      value: 23.796
+    - type: map_at_1000
+      value: 23.987
+    - type: map_at_20
+      value: 22.889
+    - type: map_at_3
+      value: 18.144
+    - type: map_at_5
+      value: 19.921
+    - type: mrr_at_1
+      value: 28.794999999999998
+    - type: mrr_at_10
+      value: 40.261
+    - type: mrr_at_100
+      value: 41.187000000000005
+    - type: mrr_at_1000
+      value: 41.224
+    - type: mrr_at_20
+      value: 40.853
+    - type: mrr_at_3
+      value: 36.895
+    - type: mrr_at_5
+      value: 38.781
+    - type: ndcg_at_1
+      value: 28.794999999999998
+    - type: ndcg_at_10
+      value: 30.37
+    - type: ndcg_at_100
+      value: 37.936
+    - type: ndcg_at_1000
+      value: 41.332
+    - type: ndcg_at_20
+      value: 33.452
+    - type: ndcg_at_3
+      value: 24.723
+    - type: ndcg_at_5
+      value: 26.562
+    - type: precision_at_1
+      value: 28.794999999999998
+    - type: precision_at_10
+      value: 9.498
+    - type: precision_at_100
+      value: 1.7590000000000001
+    - type: precision_at_1000
+      value: 0.23900000000000002
+    - type: precision_at_20
+      value: 6.085
+    - type: precision_at_3
+      value: 18.284
+    - type: precision_at_5
+      value: 14.046
+    - type: recall_at_1
+      value: 12.842999999999998
+    - type: recall_at_10
+      value: 36.524
+    - type: recall_at_100
+      value: 62.197
+    - type: recall_at_1000
+      value: 81.25
+    - type: recall_at_20
+      value: 45.21
+    - type: recall_at_3
+      value: 22.549
+    - type: recall_at_5
+      value: 27.938000000000002
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/dbpedia
+      name: MTEB DBPedia
+      config: default
+      split: test
+      revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
+    metrics:
+    - type: map_at_1
+      value: 9.041
+    - type: map_at_10
+      value: 20.801
+    - type: map_at_100
+      value: 30.377
+    - type: map_at_1000
+      value: 32.106
+    - type: map_at_20
+      value: 24.453
+    - type: map_at_3
+      value: 14.698
+    - type: map_at_5
+      value: 17.301
+    - type: mrr_at_1
+      value: 67.75
+    - type: mrr_at_10
+      value: 76.409
+    - type: mrr_at_100
+      value: 76.727
+    - type: mrr_at_1000
+      value: 76.73400000000001
+    - type: mrr_at_20
+      value: 76.669
+    - type: mrr_at_3
+      value: 74.833
+    - type: mrr_at_5
+      value: 75.783
+    - type: ndcg_at_1
+      value: 55.875
+    - type: ndcg_at_10
+      value: 43.308
+    - type: ndcg_at_100
+      value: 49.183
+    - type: ndcg_at_1000
+      value: 56.660999999999994
+    - type: ndcg_at_20
+      value: 43.074
+    - type: ndcg_at_3
+      value: 47.758
+    - type: ndcg_at_5
+      value: 45.111000000000004
+    - type: precision_at_1
+      value: 67.75
+    - type: precision_at_10
+      value: 34.8
+    - type: precision_at_100
+      value: 11.417
+    - type: precision_at_1000
+      value: 2.114
+    - type: precision_at_20
+      value: 26.712000000000003
+    - type: precision_at_3
+      value: 52.25
+    - type: precision_at_5
+      value: 44.45
+    - type: recall_at_1
+      value: 9.041
+    - type: recall_at_10
+      value: 26.863999999999997
+    - type: recall_at_100
+      value: 57.403999999999996
+    - type: recall_at_1000
+      value: 81.22200000000001
+    - type: recall_at_20
+      value: 35.132999999999996
+    - type: recall_at_3
+      value: 15.955
+    - type: recall_at_5
+      value: 20.304
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/emotion
+      name: MTEB EmotionClassification
+      config: default
+      split: test
+      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
+    metrics:
+    - type: accuracy
+      value: 51.934999999999995
+    - type: f1
+      value: 46.90330636364514
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/fever
+      name: MTEB FEVER
+      config: default
+      split: test
+      revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
+    metrics:
+    - type: map_at_1
+      value: 70.231
+    - type: map_at_10
+      value: 79.506
+    - type: map_at_100
+      value: 79.777
+    - type: map_at_1000
+      value: 79.794
+    - type: map_at_20
+      value: 79.69000000000001
+    - type: map_at_3
+      value: 78.237
+    - type: map_at_5
+      value: 79.061
+    - type: mrr_at_1
+      value: 75.728
+    - type: mrr_at_10
+      value: 83.839
+    - type: mrr_at_100
+      value: 83.965
+    - type: mrr_at_1000
+      value: 83.97
+    - type: mrr_at_20
+      value: 83.93
+    - type: mrr_at_3
+      value: 82.908
+    - type: mrr_at_5
+      value: 83.539
+    - type: ndcg_at_1
+      value: 75.728
+    - type: ndcg_at_10
+      value: 83.576
+    - type: ndcg_at_100
+      value: 84.544
+    - type: ndcg_at_1000
+      value: 84.868
+    - type: ndcg_at_20
+      value: 84.096
+    - type: ndcg_at_3
+      value: 81.49499999999999
+    - type: ndcg_at_5
+      value: 82.69999999999999
+    - type: precision_at_1
+      value: 75.728
+    - type: precision_at_10
+      value: 10.174
+    - type: precision_at_100
+      value: 1.085
+    - type: precision_at_1000
+      value: 0.11299999999999999
+    - type: precision_at_20
+      value: 5.234
+    - type: precision_at_3
+      value: 31.383
+    - type: precision_at_5
+      value: 19.625
+    - type: recall_at_1
+      value: 70.231
+    - type: recall_at_10
+      value: 91.774
+    - type: recall_at_100
+      value: 95.639
+    - type: recall_at_1000
+      value: 97.78
+    - type: recall_at_20
+      value: 93.60300000000001
+    - type: recall_at_3
+      value: 86.107
+    - type: recall_at_5
+      value: 89.164
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/fiqa
+      name: MTEB FiQA2018
+      config: default
+      split: test
+      revision: 27a168819829fe9bcd655c2df245fb19452e8e06
+    metrics:
+    - type: map_at_1
+      value: 22.043
+    - type: map_at_10
+      value: 36.831
+    - type: map_at_100
+      value: 38.929
+    - type: map_at_1000
+      value: 39.102
+    - type: map_at_20
+      value: 38.039
+    - type: map_at_3
+      value: 32.202999999999996
+    - type: map_at_5
+      value: 35.04
+    - type: mrr_at_1
+      value: 43.980999999999995
+    - type: mrr_at_10
+      value: 53.592
+    - type: mrr_at_100
+      value: 54.384
+    - type: mrr_at_1000
+      value: 54.413999999999994
+    - type: mrr_at_20
+      value: 54.118
+    - type: mrr_at_3
+      value: 51.595
+    - type: mrr_at_5
+      value: 52.744
+    - type: ndcg_at_1
+      value: 43.980999999999995
+    - type: ndcg_at_10
+      value: 45.009
+    - type: ndcg_at_100
+      value: 52.129000000000005
+    - type: ndcg_at_1000
+      value: 54.788000000000004
+    - type: ndcg_at_20
+      value: 48.001
+    - type: ndcg_at_3
+      value: 41.46
+    - type: ndcg_at_5
+      value: 42.797000000000004
+    - type: precision_at_1
+      value: 43.980999999999995
+    - type: precision_at_10
+      value: 12.438
+    - type: precision_at_100
+      value: 1.9800000000000002
+    - type: precision_at_1000
+      value: 0.246
+    - type: precision_at_20
+      value: 7.515
+    - type: precision_at_3
+      value: 27.881
+    - type: precision_at_5
+      value: 20.463
+    - type: recall_at_1
+      value: 22.043
+    - type: recall_at_10
+      value: 51.796
+    - type: recall_at_100
+      value: 77.888
+    - type: recall_at_1000
+      value: 93.459
+    - type: recall_at_20
+      value: 60.953
+    - type: recall_at_3
+      value: 37.779
+    - type: recall_at_5
+      value: 44.666
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/hotpotqa
+      name: MTEB HotpotQA
+      config: default
+      split: test
+      revision: ab518f4d6fcca38d87c25209f94beba119d02014
+    metrics:
+    - type: map_at_1
+      value: 39.061
+    - type: map_at_10
+      value: 62.934999999999995
+    - type: map_at_100
+      value: 63.844
+    - type: map_at_1000
+      value: 63.904
+    - type: map_at_20
+      value: 63.479
+    - type: map_at_3
+      value: 59.15899999999999
+    - type: map_at_5
+      value: 61.499
+    - type: mrr_at_1
+      value: 78.123
+    - type: mrr_at_10
+      value: 84.059
+    - type: mrr_at_100
+      value: 84.235
+    - type: mrr_at_1000
+      value: 84.241
+    - type: mrr_at_20
+      value: 84.16799999999999
+    - type: mrr_at_3
+      value: 83.086
+    - type: mrr_at_5
+      value: 83.709
+    - type: ndcg_at_1
+      value: 78.123
+    - type: ndcg_at_10
+      value: 71.26
+    - type: ndcg_at_100
+      value: 74.372
+    - type: ndcg_at_1000
+      value: 75.484
+    - type: ndcg_at_20
+      value: 72.587
+    - type: ndcg_at_3
+      value: 65.984
+    - type: ndcg_at_5
+      value: 68.89699999999999
+    - type: precision_at_1
+      value: 78.123
+    - type: precision_at_10
+      value: 15.076
+    - type: precision_at_100
+      value: 1.7500000000000002
+    - type: precision_at_1000
+      value: 0.19
+    - type: precision_at_20
+      value: 7.964
+    - type: precision_at_3
+      value: 42.494
+    - type: precision_at_5
+      value: 27.792
+    - type: recall_at_1
+      value: 39.061
+    - type: recall_at_10
+      value: 75.381
+    - type: recall_at_100
+      value: 87.522
+    - type: recall_at_1000
+      value: 94.828
+    - type: recall_at_20
+      value: 79.642
+    - type: recall_at_3
+      value: 63.741
+    - type: recall_at_5
+      value: 69.48
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/imdb
+      name: MTEB ImdbClassification
+      config: default
+      split: test
+      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
+    metrics:
+    - type: accuracy
+      value: 91.9088
+    - type: ap
+      value: 88.23414041783927
+    - type: f1
+      value: 91.8949910564831
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/msmarco
+      name: MTEB MSMARCO
+      config: default
+      split: dev
+      revision: c5a29a104738b98a9e76336939199e264163d4a0
+    metrics:
+    - type: map_at_1
+      value: 22.102
+    - type: map_at_10
+      value: 34.666999999999994
+    - type: map_at_100
+      value: 35.849
+    - type: map_at_1000
+      value: 35.897
+    - type: map_at_20
+      value: 35.415
+    - type: map_at_3
+      value: 30.805
+    - type: map_at_5
+      value: 33.042
+    - type: mrr_at_1
+      value: 22.665
+    - type: mrr_at_10
+      value: 35.276999999999994
+    - type: mrr_at_100
+      value: 36.388999999999996
+    - type: mrr_at_1000
+      value: 36.43
+    - type: mrr_at_20
+      value: 35.984
+    - type: mrr_at_3
+      value: 31.453999999999997
+    - type: mrr_at_5
+      value: 33.701
+    - type: ndcg_at_1
+      value: 22.665
+    - type: ndcg_at_10
+      value: 41.63
+    - type: ndcg_at_100
+      value: 47.257
+    - type: ndcg_at_1000
+      value: 48.425000000000004
+    - type: ndcg_at_20
+      value: 44.26
+    - type: ndcg_at_3
+      value: 33.756
+    - type: ndcg_at_5
+      value: 37.771
+    - type: precision_at_1
+      value: 22.665
+    - type: precision_at_10
+      value: 6.583
+    - type: precision_at_100
+      value: 0.9400000000000001
+    - type: precision_at_1000
+      value: 0.104
+    - type: precision_at_20
+      value: 3.837
+    - type: precision_at_3
+      value: 14.379
+    - type: precision_at_5
+      value: 10.662
+    - type: recall_at_1
+      value: 22.102
+    - type: recall_at_10
+      value: 63.007000000000005
+    - type: recall_at_100
+      value: 88.942
+    - type: recall_at_1000
+      value: 97.80799999999999
+    - type: recall_at_20
+      value: 73.195
+    - type: recall_at_3
+      value: 41.632000000000005
+    - type: recall_at_5
+      value: 51.275999999999996
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_domain
+      name: MTEB MTOPDomainClassification (en)
+      config: en
+      split: test
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+    metrics:
+    - type: accuracy
+      value: 94.32512539899682
+    - type: f1
+      value: 94.08399309589969
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_intent
+      name: MTEB MTOPIntentClassification (en)
+      config: en
+      split: test
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+    metrics:
+    - type: accuracy
+      value: 76.60510715914273
+    - type: f1
+      value: 58.21529064999782
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (en)
+      config: en
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 75.90786819098857
+    - type: f1
+      value: 74.0025337373784
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (en)
+      config: en
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 79.43174176193679
+    - type: f1
+      value: 79.80377677179487
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-p2p
+      name: MTEB MedrxivClusteringP2P
+      config: default
+      split: test
+      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
+    metrics:
+    - type: v_measure
+      value: 33.625500288734244
+    - type: v_measures
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+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-s2s
+      name: MTEB MedrxivClusteringS2S
+      config: default
+      split: test
+      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
+    metrics:
+    - type: v_measure
+      value: 31.70226358971163
+    - type: v_measures
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+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/mind_small
+      name: MTEB MindSmallReranking
+      config: default
+      split: test
+      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
+    metrics:
+    - type: map
+      value: 32.77671285103453
+    - type: mrr
+      value: 34.069523934828844
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/nfcorpus
+      name: MTEB NFCorpus
+      config: default
+      split: test
+      revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
+    metrics:
+    - type: map_at_1
+      value: 7.281
+    - type: map_at_10
+      value: 15.652
+    - type: map_at_100
+      value: 20.165
+    - type: map_at_1000
+      value: 21.834
+    - type: map_at_20
+      value: 17.604
+    - type: map_at_3
+      value: 11.363
+    - type: map_at_5
+      value: 13.418
+    - type: mrr_at_1
+      value: 49.536
+    - type: mrr_at_10
+      value: 58.689
+    - type: mrr_at_100
+      value: 59.153
+    - type: mrr_at_1000
+      value: 59.184000000000005
+    - type: mrr_at_20
+      value: 58.958999999999996
+    - type: mrr_at_3
+      value: 56.192
+    - type: mrr_at_5
+      value: 57.91
+    - type: ndcg_at_1
+      value: 47.214
+    - type: ndcg_at_10
+      value: 39.126
+    - type: ndcg_at_100
+      value: 36.852000000000004
+    - type: ndcg_at_1000
+      value: 45.65
+    - type: ndcg_at_20
+      value: 37.263000000000005
+    - type: ndcg_at_3
+      value: 43.804
+    - type: ndcg_at_5
+      value: 42.01
+    - type: precision_at_1
+      value: 48.607
+    - type: precision_at_10
+      value: 28.762
+    - type: precision_at_100
+      value: 9.316
+    - type: precision_at_1000
+      value: 2.254
+    - type: precision_at_20
+      value: 21.95
+    - type: precision_at_3
+      value: 40.660000000000004
+    - type: precision_at_5
+      value: 35.913000000000004
+    - type: recall_at_1
+      value: 7.281
+    - type: recall_at_10
+      value: 20.006
+    - type: recall_at_100
+      value: 37.525
+    - type: recall_at_1000
+      value: 69.112
+    - type: recall_at_20
+      value: 24.396
+    - type: recall_at_3
+      value: 12.249
+    - type: recall_at_5
+      value: 15.946
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/nq
+      name: MTEB NQ
+      config: default
+      split: test
+      revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
+    metrics:
+    - type: map_at_1
+      value: 30.779
+    - type: map_at_10
+      value: 46.973
+    - type: map_at_100
+      value: 47.964
+    - type: map_at_1000
+      value: 47.99
+    - type: map_at_20
+      value: 47.653
+    - type: map_at_3
+      value: 42.323
+    - type: map_at_5
+      value: 45.076
+    - type: mrr_at_1
+      value: 34.82
+    - type: mrr_at_10
+      value: 49.458999999999996
+    - type: mrr_at_100
+      value: 50.17700000000001
+    - type: mrr_at_1000
+      value: 50.195
+    - type: mrr_at_20
+      value: 49.968
+    - type: mrr_at_3
+      value: 45.606
+    - type: mrr_at_5
+      value: 47.946
+    - type: ndcg_at_1
+      value: 34.82
+    - type: ndcg_at_10
+      value: 55.131
+    - type: ndcg_at_100
+      value: 59.17400000000001
+    - type: ndcg_at_1000
+      value: 59.763
+    - type: ndcg_at_20
+      value: 57.306999999999995
+    - type: ndcg_at_3
+      value: 46.455
+    - type: ndcg_at_5
+      value: 51.034
+    - type: precision_at_1
+      value: 34.82
+    - type: precision_at_10
+      value: 9.241000000000001
+    - type: precision_at_100
+      value: 1.1520000000000001
+    - type: precision_at_1000
+      value: 0.121
+    - type: precision_at_20
+      value: 5.1450000000000005
+    - type: precision_at_3
+      value: 21.34
+    - type: precision_at_5
+      value: 15.423
+    - type: recall_at_1
+      value: 30.779
+    - type: recall_at_10
+      value: 77.424
+    - type: recall_at_100
+      value: 94.728
+    - type: recall_at_1000
+      value: 99.104
+    - type: recall_at_20
+      value: 85.458
+    - type: recall_at_3
+      value: 55.113
+    - type: recall_at_5
+      value: 65.67
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/quora
+      name: MTEB QuoraRetrieval
+      config: default
+      split: test
+      revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
+    metrics:
+    - type: map_at_1
+      value: 71.588
+    - type: map_at_10
+      value: 85.57000000000001
+    - type: map_at_100
+      value: 86.20100000000001
+    - type: map_at_1000
+      value: 86.215
+    - type: map_at_20
+      value: 85.982
+    - type: map_at_3
+      value: 82.722
+    - type: map_at_5
+      value: 84.493
+    - type: mrr_at_1
+      value: 82.46
+    - type: mrr_at_10
+      value: 88.369
+    - type: mrr_at_100
+      value: 88.47
+    - type: mrr_at_1000
+      value: 88.47
+    - type: mrr_at_20
+      value: 88.449
+    - type: mrr_at_3
+      value: 87.485
+    - type: mrr_at_5
+      value: 88.098
+    - type: ndcg_at_1
+      value: 82.43
+    - type: ndcg_at_10
+      value: 89.119
+    - type: ndcg_at_100
+      value: 90.29700000000001
+    - type: ndcg_at_1000
+      value: 90.363
+    - type: ndcg_at_20
+      value: 89.77199999999999
+    - type: ndcg_at_3
+      value: 86.504
+    - type: ndcg_at_5
+      value: 87.934
+    - type: precision_at_1
+      value: 82.43
+    - type: precision_at_10
+      value: 13.501
+    - type: precision_at_100
+      value: 1.537
+    - type: precision_at_1000
+      value: 0.157
+    - type: precision_at_20
+      value: 7.156999999999999
+    - type: precision_at_3
+      value: 37.877
+    - type: precision_at_5
+      value: 24.8
+    - type: recall_at_1
+      value: 71.588
+    - type: recall_at_10
+      value: 95.8
+    - type: recall_at_100
+      value: 99.74499999999999
+    - type: recall_at_1000
+      value: 99.99
+    - type: recall_at_20
+      value: 97.89
+    - type: recall_at_3
+      value: 88.15899999999999
+    - type: recall_at_5
+      value: 92.35
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering
+      name: MTEB RedditClustering
+      config: default
+      split: test
+      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
+    metrics:
+    - type: v_measure
+      value: 59.768148638646366
+    - type: v_measures
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+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering-p2p
+      name: MTEB RedditClusteringP2P
+      config: default
+      split: test
+      revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
+    metrics:
+    - type: v_measure
+      value: 63.79386989587679
+    - type: v_measures
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+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/scidocs
+      name: MTEB SCIDOCS
+      config: default
+      split: test
+      revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
+    metrics:
+    - type: map_at_1
+      value: 5.143
+    - type: map_at_10
+      value: 14.493
+    - type: map_at_100
+      value: 17.131
+    - type: map_at_1000
+      value: 17.527
+    - type: map_at_20
+      value: 15.815999999999999
+    - type: map_at_3
+      value: 10.133000000000001
+    - type: map_at_5
+      value: 12.288
+    - type: mrr_at_1
+      value: 25.4
+    - type: mrr_at_10
+      value: 38.671
+    - type: mrr_at_100
+      value: 39.715
+    - type: mrr_at_1000
+      value: 39.745999999999995
+    - type: mrr_at_20
+      value: 39.333
+    - type: mrr_at_3
+      value: 35.467
+    - type: mrr_at_5
+      value: 37.347
+    - type: ndcg_at_1
+      value: 25.4
+    - type: ndcg_at_10
+      value: 23.785
+    - type: ndcg_at_100
+      value: 33.478
+    - type: ndcg_at_1000
+      value: 39.425
+    - type: ndcg_at_20
+      value: 27.156999999999996
+    - type: ndcg_at_3
+      value: 22.597
+    - type: ndcg_at_5
+      value: 19.798
+    - type: precision_at_1
+      value: 25.4
+    - type: precision_at_10
+      value: 12.520000000000001
+    - type: precision_at_100
+      value: 2.662
+    - type: precision_at_1000
+      value: 0.40800000000000003
+    - type: precision_at_20
+      value: 8.215
+    - type: precision_at_3
+      value: 21.767
+    - type: precision_at_5
+      value: 17.8
+    - type: recall_at_1
+      value: 5.143
+    - type: recall_at_10
+      value: 25.378
+    - type: recall_at_100
+      value: 54.032000000000004
+    - type: recall_at_1000
+      value: 82.73
+    - type: recall_at_20
+      value: 33.312000000000005
+    - type: recall_at_3
+      value: 13.222999999999999
+    - type: recall_at_5
+      value: 18.062
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sickr-sts
+      name: MTEB SICK-R
+      config: default
+      split: test
+      revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
+    metrics:
+    - type: cos_sim_pearson
+      value: 87.57401378797366
+    - type: cos_sim_spearman
+      value: 82.83001707430854
+    - type: euclidean_pearson
+      value: 84.86793164498624
+    - type: euclidean_spearman
+      value: 82.55413453843204
+    - type: manhattan_pearson
+      value: 84.8851834466949
+    - type: manhattan_spearman
+      value: 82.5582994454054
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts12-sts
+      name: MTEB STS12
+      config: default
+      split: test
+      revision: a0d554a64d88156834ff5ae9920b964011b16384
+    metrics:
+    - type: cos_sim_pearson
+      value: 87.42938681941963
+    - type: cos_sim_spearman
+      value: 78.65009395911503
+    - type: euclidean_pearson
+      value: 85.83478468305478
+    - type: euclidean_spearman
+      value: 79.01427999514746
+    - type: manhattan_pearson
+      value: 85.81496883353536
+    - type: manhattan_spearman
+      value: 78.99456935403117
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts13-sts
+      name: MTEB STS13
+      config: default
+      split: test
+      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
+    metrics:
+    - type: cos_sim_pearson
+      value: 89.44529804367387
+    - type: cos_sim_spearman
+      value: 90.00142148909681
+    - type: euclidean_pearson
+      value: 89.00052026000864
+    - type: euclidean_spearman
+      value: 89.86653252628048
+    - type: manhattan_pearson
+      value: 88.95743893759386
+    - type: manhattan_spearman
+      value: 89.83494500063517
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts14-sts
+      name: MTEB STS14
+      config: default
+      split: test
+      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
+    metrics:
+    - type: cos_sim_pearson
+      value: 87.45360957773492
+    - type: cos_sim_spearman
+      value: 84.96999168443674
+    - type: euclidean_pearson
+      value: 86.73163292656861
+    - type: euclidean_spearman
+      value: 85.16035306962318
+    - type: manhattan_pearson
+      value: 86.71055630525136
+    - type: manhattan_spearman
+      value: 85.14629965640846
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts15-sts
+      name: MTEB STS15
+      config: default
+      split: test
+      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
+    metrics:
+    - type: cos_sim_pearson
+      value: 88.63706368456388
+    - type: cos_sim_spearman
+      value: 89.81153125001883
+    - type: euclidean_pearson
+      value: 88.83649620738461
+    - type: euclidean_spearman
+      value: 89.47909072703986
+    - type: manhattan_pearson
+      value: 88.83193018422992
+    - type: manhattan_spearman
+      value: 89.47672272039262
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts16-sts
+      name: MTEB STS16
+      config: default
+      split: test
+      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
+    metrics:
+    - type: cos_sim_pearson
+      value: 85.34235491663839
+    - type: cos_sim_spearman
+      value: 86.70854613787373
+    - type: euclidean_pearson
+      value: 85.73730484853073
+    - type: euclidean_spearman
+      value: 86.28313894663437
+    - type: manhattan_pearson
+      value: 85.70285004041696
+    - type: manhattan_spearman
+      value: 86.26723700895138
+  - 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: 90.10976781396273
+    - type: cos_sim_spearman
+      value: 89.79699475327726
+    - type: euclidean_pearson
+      value: 89.51007666708566
+    - type: euclidean_spearman
+      value: 88.97696159087126
+    - type: manhattan_pearson
+      value: 89.5441850001744
+    - type: manhattan_spearman
+      value: 89.04684488385651
+  - 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: 69.8918539910347
+    - type: cos_sim_spearman
+      value: 69.66706227647323
+    - type: euclidean_pearson
+      value: 70.87888342240508
+    - type: euclidean_spearman
+      value: 69.34119085154248
+    - type: manhattan_pearson
+      value: 70.8912286820092
+    - type: manhattan_spearman
+      value: 69.5009524916871
+  - task:
+      type: STS
+    dataset:
+      type: mteb/stsbenchmark-sts
+      name: MTEB STSBenchmark
+      config: default
+      split: test
+      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
+    metrics:
+    - type: cos_sim_pearson
+      value: 87.29883016932499
+    - type: cos_sim_spearman
+      value: 88.76691675006461
+    - type: euclidean_pearson
+      value: 88.20225127014815
+    - type: euclidean_spearman
+      value: 88.48087977970427
+    - type: manhattan_pearson
+      value: 88.2072233596074
+    - type: manhattan_spearman
+      value: 88.47336658990169
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/scidocs-reranking
+      name: MTEB SciDocsRR
+      config: default
+      split: test
+      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
+    metrics:
+    - type: map
+      value: 87.61294576605022
+    - type: mrr
+      value: 96.31477092261404
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/scifact
+      name: MTEB SciFact
+      config: default
+      split: test
+      revision: 0228b52cf27578f30900b9e5271d331663a030d7
+    metrics:
+    - type: map_at_1
+      value: 60.260999999999996
+    - type: map_at_10
+      value: 70.462
+    - type: map_at_100
+      value: 70.86200000000001
+    - type: map_at_1000
+      value: 70.884
+    - type: map_at_20
+      value: 70.75
+    - type: map_at_3
+      value: 67.422
+    - type: map_at_5
+      value: 68.95400000000001
+    - type: mrr_at_1
+      value: 63.0
+    - type: mrr_at_10
+      value: 71.435
+    - type: mrr_at_100
+      value: 71.755
+    - type: mrr_at_1000
+      value: 71.776
+    - type: mrr_at_20
+      value: 71.65599999999999
+    - type: mrr_at_3
+      value: 69.167
+    - type: mrr_at_5
+      value: 70.467
+    - type: ndcg_at_1
+      value: 63.0
+    - type: ndcg_at_10
+      value: 75.247
+    - type: ndcg_at_100
+      value: 76.926
+    - type: ndcg_at_1000
+      value: 77.402
+    - type: ndcg_at_20
+      value: 76.164
+    - type: ndcg_at_3
+      value: 69.966
+    - type: ndcg_at_5
+      value: 72.25200000000001
+    - type: precision_at_1
+      value: 63.0
+    - type: precision_at_10
+      value: 10.100000000000001
+    - type: precision_at_100
+      value: 1.093
+    - type: precision_at_1000
+      value: 0.11299999999999999
+    - type: precision_at_20
+      value: 5.25
+    - type: precision_at_3
+      value: 27.222
+    - type: precision_at_5
+      value: 17.933
+    - type: recall_at_1
+      value: 60.260999999999996
+    - type: recall_at_10
+      value: 88.98899999999999
+    - type: recall_at_100
+      value: 96.5
+    - type: recall_at_1000
+      value: 100.0
+    - type: recall_at_20
+      value: 92.43299999999999
+    - type: recall_at_3
+      value: 74.506
+    - type: recall_at_5
+      value: 80.217
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/sprintduplicatequestions-pairclassification
+      name: MTEB SprintDuplicateQuestions
+      config: default
+      split: test
+      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
+    metrics:
+    - type: cos_sim_accuracy
+      value: 99.86039603960396
+    - type: cos_sim_ap
+      value: 96.87211054415707
+    - type: cos_sim_f1
+      value: 92.98856290402784
+    - type: cos_sim_precision
+      value: 92.48269040553907
+    - type: cos_sim_recall
+      value: 93.5
+    - type: dot_accuracy
+      value: 99.7990099009901
+    - type: dot_ap
+      value: 94.78284318973266
+    - type: dot_f1
+      value: 89.66921119592874
+    - type: dot_precision
+      value: 91.29533678756476
+    - type: dot_recall
+      value: 88.1
+    - type: euclidean_accuracy
+      value: 99.85643564356435
+    - type: euclidean_ap
+      value: 96.67239701870625
+    - type: euclidean_f1
+      value: 92.68784669692386
+    - type: euclidean_precision
+      value: 93.48931841302137
+    - type: euclidean_recall
+      value: 91.9
+    - type: manhattan_accuracy
+      value: 99.85643564356435
+    - type: manhattan_ap
+      value: 96.68690502730702
+    - type: manhattan_f1
+      value: 92.77528649725959
+    - type: manhattan_precision
+      value: 92.45283018867924
+    - type: manhattan_recall
+      value: 93.10000000000001
+    - type: max_accuracy
+      value: 99.86039603960396
+    - type: max_ap
+      value: 96.87211054415707
+    - type: max_f1
+      value: 92.98856290402784
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering
+      name: MTEB StackExchangeClustering
+      config: default
+      split: test
+      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
+    metrics:
+    - type: v_measure
+      value: 66.31370326221715
+    - type: v_measures
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+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering-p2p
+      name: MTEB StackExchangeClusteringP2P
+      config: default
+      split: test
+      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
+    metrics:
+    - type: v_measure
+      value: 34.98820897729802
+    - type: v_measures
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+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/stackoverflowdupquestions-reranking
+      name: MTEB StackOverflowDupQuestions
+      config: default
+      split: test
+      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
+    metrics:
+    - type: map
+      value: 55.185955556406554
+    - type: mrr
+      value: 56.137862341906455
+  - task:
+      type: Summarization
+    dataset:
+      type: mteb/summeval
+      name: MTEB SummEval
+      config: default
+      split: test
+      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
+    metrics:
+    - type: cos_sim_pearson
+      value: 30.657368209428938
+    - type: cos_sim_spearman
+      value: 31.926391208280304
+    - type: dot_pearson
+      value: 28.723660986211748
+    - type: dot_spearman
+      value: 29.051223656612642
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/trec-covid
+      name: MTEB TRECCOVID
+      config: default
+      split: test
+      revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
+    metrics:
+    - type: map_at_1
+      value: 0.218
+    - type: map_at_10
+      value: 1.746
+    - type: map_at_100
+      value: 9.815
+    - type: map_at_1000
+      value: 24.196
+    - type: map_at_20
+      value: 3.097
+    - type: map_at_3
+      value: 0.616
+    - type: map_at_5
+      value: 0.991
+    - type: mrr_at_1
+      value: 80.0
+    - type: mrr_at_10
+      value: 88.667
+    - type: mrr_at_100
+      value: 88.667
+    - type: mrr_at_1000
+      value: 88.667
+    - type: mrr_at_20
+      value: 88.667
+    - type: mrr_at_3
+      value: 87.667
+    - type: mrr_at_5
+      value: 88.667
+    - type: ndcg_at_1
+      value: 73.0
+    - type: ndcg_at_10
+      value: 69.377
+    - type: ndcg_at_100
+      value: 53.878
+    - type: ndcg_at_1000
+      value: 49.589
+    - type: ndcg_at_20
+      value: 66.31
+    - type: ndcg_at_3
+      value: 74.654
+    - type: ndcg_at_5
+      value: 73.56899999999999
+    - type: precision_at_1
+      value: 80.0
+    - type: precision_at_10
+      value: 73.8
+    - type: precision_at_100
+      value: 55.74
+    - type: precision_at_1000
+      value: 21.814
+    - type: precision_at_20
+      value: 70.3
+    - type: precision_at_3
+      value: 80.0
+    - type: precision_at_5
+      value: 78.0
+    - type: recall_at_1
+      value: 0.218
+    - type: recall_at_10
+      value: 1.983
+    - type: recall_at_100
+      value: 13.499
+    - type: recall_at_1000
+      value: 46.869
+    - type: recall_at_20
+      value: 3.703
+    - type: recall_at_3
+      value: 0.656
+    - type: recall_at_5
+      value: 1.0739999999999998
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/touche2020
+      name: MTEB Touche2020
+      config: default
+      split: test
+      revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
+    metrics:
+    - type: map_at_1
+      value: 2.358
+    - type: map_at_10
+      value: 9.494
+    - type: map_at_100
+      value: 15.809999999999999
+    - type: map_at_1000
+      value: 17.308
+    - type: map_at_20
+      value: 12.171
+    - type: map_at_3
+      value: 4.727
+    - type: map_at_5
+      value: 6.798
+    - type: mrr_at_1
+      value: 30.612000000000002
+    - type: mrr_at_10
+      value: 44.615
+    - type: mrr_at_100
+      value: 45.794000000000004
+    - type: mrr_at_1000
+      value: 45.812999999999995
+    - type: mrr_at_20
+      value: 45.519999999999996
+    - type: mrr_at_3
+      value: 41.156
+    - type: mrr_at_5
+      value: 42.483
+    - type: ndcg_at_1
+      value: 26.531
+    - type: ndcg_at_10
+      value: 23.115
+    - type: ndcg_at_100
+      value: 36.082
+    - type: ndcg_at_1000
+      value: 47.467999999999996
+    - type: ndcg_at_20
+      value: 25.224999999999998
+    - type: ndcg_at_3
+      value: 25.238
+    - type: ndcg_at_5
+      value: 24.299
+    - type: precision_at_1
+      value: 30.612000000000002
+    - type: precision_at_10
+      value: 20.816000000000003
+    - type: precision_at_100
+      value: 7.796
+    - type: precision_at_1000
+      value: 1.545
+    - type: precision_at_20
+      value: 17.347
+    - type: precision_at_3
+      value: 27.211000000000002
+    - type: precision_at_5
+      value: 25.306
+    - type: recall_at_1
+      value: 2.358
+    - type: recall_at_10
+      value: 15.433
+    - type: recall_at_100
+      value: 48.715
+    - type: recall_at_1000
+      value: 83.574
+    - type: recall_at_20
+      value: 24.038999999999998
+    - type: recall_at_3
+      value: 5.652
+    - type: recall_at_5
+      value: 9.327
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/toxic_conversations_50k
+      name: MTEB ToxicConversationsClassification
+      config: default
+      split: test
+      revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
+    metrics:
+    - type: accuracy
+      value: 67.9052734375
+    - type: ap
+      value: 12.464903195452706
+    - type: f1
+      value: 51.75730802861531
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/tweet_sentiment_extraction
+      name: MTEB TweetSentimentExtractionClassification
+      config: default
+      split: test
+      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
+    metrics:
+    - type: accuracy
+      value: 59.21618562535371
+    - type: f1
+      value: 59.5671083304645
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/twentynewsgroups-clustering
+      name: MTEB TwentyNewsgroupsClustering
+      config: default
+      split: test
+      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
+    metrics:
+    - type: v_measure
+      value: 52.98411009798346
+    - type: v_measures
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0.5174886066510178]
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twittersemeval2015-pairclassification
+      name: MTEB TwitterSemEval2015
+      config: default
+      split: test
+      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
+    metrics:
+    - type: cos_sim_accuracy
+      value: 87.30404720748643
+    - type: cos_sim_ap
+      value: 78.24262856109937
+    - type: cos_sim_f1
+      value: 72.08312468703055
+    - type: cos_sim_precision
+      value: 68.58027632205813
+    - type: cos_sim_recall
+      value: 75.96306068601582
+    - type: dot_accuracy
+      value: 84.48471121177803
+    - type: dot_ap
+      value: 67.78610175988638
+    - type: dot_f1
+      value: 63.75754527162978
+    - type: dot_precision
+      value: 60.908217203267654
+    - type: dot_recall
+      value: 66.88654353562006
+    - type: euclidean_accuracy
+      value: 87.24444179531503
+    - type: euclidean_ap
+      value: 78.16169396391096
+    - type: euclidean_f1
+      value: 72.19500244977952
+    - type: euclidean_precision
+      value: 67.37540009144948
+    - type: euclidean_recall
+      value: 77.75725593667546
+    - type: manhattan_accuracy
+      value: 87.20867854801216
+    - type: manhattan_ap
+      value: 78.10430615026713
+    - type: manhattan_f1
+      value: 72.25504677498769
+    - type: manhattan_precision
+      value: 67.72035071527456
+    - type: manhattan_recall
+      value: 77.44063324538259
+    - type: max_accuracy
+      value: 87.30404720748643
+    - type: max_ap
+      value: 78.24262856109937
+    - type: max_f1
+      value: 72.25504677498769
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twitterurlcorpus-pairclassification
+      name: MTEB TwitterURLCorpus
+      config: default
+      split: test
+      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
+    metrics:
+    - type: cos_sim_accuracy
+      value: 89.08681647067955
+    - type: cos_sim_ap
+      value: 86.10715470590844
+    - type: cos_sim_f1
+      value: 78.62958187511512
+    - type: cos_sim_precision
+      value: 75.38320265592992
+    - type: cos_sim_recall
+      value: 82.16815522020326
+    - type: dot_accuracy
+      value: 88.00985756975977
+    - type: dot_ap
+      value: 83.27536710177887
+    - type: dot_f1
+      value: 76.57026000584284
+    - type: dot_precision
+      value: 72.82578494026119
+    - type: dot_recall
+      value: 80.72066522944257
+    - type: euclidean_accuracy
+      value: 88.9024721543059
+    - type: euclidean_ap
+      value: 85.83507000245919
+    - type: euclidean_f1
+      value: 78.354072605807
+    - type: euclidean_precision
+      value: 74.87197474570326
+    - type: euclidean_recall
+      value: 82.17585463504774
+    - type: manhattan_accuracy
+      value: 88.90829355377032
+    - type: manhattan_ap
+      value: 85.82130285331947
+    - type: manhattan_f1
+      value: 78.28887843364338
+    - type: manhattan_precision
+      value: 73.86464522297344
+    - type: manhattan_recall
+      value: 83.2768709578072
+    - type: max_accuracy
+      value: 89.08681647067955
+    - type: max_ap
+      value: 86.10715470590844
+    - type: max_f1
+      value: 78.62958187511512
 ---
 
-# Model Card for Model ID
+# b1ade-embed-kd
 
-<!-- Provide a quick summary of what the model is/does. -->
+This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
 
+<!--- Describe your model here -->
 
+## Usage (Sentence-Transformers)
 
-## Model Details
+Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
 
-### Model Description
+```
+pip install -U sentence-transformers
+```
 
-<!-- Provide a longer summary of what this model is. -->
+Then you can use the model like this:
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+```python
+from sentence_transformers import SentenceTransformer
+sentences = ["This is an example sentence", "Each sentence is converted"]
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+model = SentenceTransformer('{MODEL_NAME}')
+embeddings = model.encode(sentences)
+print(embeddings)
+```
 
-### Model Sources [optional]
 
-<!-- Provide the basic links for the model. -->
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
+## Usage (HuggingFace Transformers)
+Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
 
-## Uses
+```python
+from transformers import AutoTokenizer, AutoModel
+import torch
 
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
 
-### Direct Use
+#Mean Pooling - Take attention mask into account for correct averaging
+def mean_pooling(model_output, attention_mask):
+    token_embeddings = model_output[0] #First element of model_output contains all token embeddings
+    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
+    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
 
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
 
-[More Information Needed]
+# Sentences we want sentence embeddings for
+sentences = ['This is an example sentence', 'Each sentence is converted']
 
-### Downstream Use [optional]
+# Load model from HuggingFace Hub
+tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
+model = AutoModel.from_pretrained('{MODEL_NAME}')
 
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+# Tokenize sentences
+encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
 
-[More Information Needed]
+# Compute token embeddings
+with torch.no_grad():
+    model_output = model(**encoded_input)
 
-### Out-of-Scope Use
+# Perform pooling. In this case, mean pooling.
+sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+print("Sentence embeddings:")
+print(sentence_embeddings)
+```
 
-[More Information Needed]
 
-## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+## Evaluation Results
 
-[More Information Needed]
+<!--- Describe how your model was evaluated -->
 
-### Recommendations
+For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
 
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
 
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+## Training
+The model was distilled with teacher model as 
 
-## How to Get Started with the Model
 
-Use the code below to get started with the model.
+and student model as b1ade-embed
 
-[More Information Needed]
+**DataLoader**:
 
-## Training Details
+`torch.utils.data.dataloader.DataLoader` of length 275105 with parameters:
+```
+{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
+```
 
-### Training Data
+**Loss**:
 
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+`sentence_transformers.losses.MSELoss.MSELoss` 
 
-[More Information Needed]
+Parameters of the fit()-Method:
+```
+{
+    "epochs": 3,
+    "evaluation_steps": 5000,
+    "evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
+    "max_grad_norm": 1,
+    "optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
+    "optimizer_params": {
+        "eps": 1e-06,
+        "lr": 5e-05
+    },
+    "scheduler": "WarmupLinear",
+    "steps_per_epoch": null,
+    "warmup_steps": 1000,
+    "weight_decay": 0.01
+}
+```
 
-### Training Procedure
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+## Full Model Architecture
+```
+SentenceTransformer(
+  (0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel 
+  (1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
+)
+```
 
-#### Preprocessing [optional]
+## Results:
 
-[More Information Needed]
+Good agreement with teacher model, at least on STS:
 
+Teacher:
+```
+2024-05-20 16:29:07 - Teacher Performance:
+2024-05-20 16:29:07 - EmbeddingSimilarityEvaluator: Evaluating the model on the sts-dev dataset:
+2024-05-20 16:29:12 - Cosine-Similarity :	Pearson: 0.8561	Spearman: 0.8597
+2024-05-20 16:29:12 - Manhattan-Distance:	Pearson: 0.8569	Spearman: 0.8567
+2024-05-20 16:29:12 - Euclidean-Distance:	Pearson: 0.8575	Spearman: 0.8571
+2024-05-20 16:29:12 - Dot-Product-Similarity:	Pearson: 0.8624	Spearman: 0.8662
+```
 
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
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+Student: 
+```
+2024-05-20 16:29:12 - Student Performance:
+2024-05-20 16:29:12 - EmbeddingSimilarityEvaluator: Evaluating the model on the sts-dev dataset:
+2024-05-20 16:29:17 - Cosine-Similarity :	Pearson: 0.8561	Spearman: 0.8597
+2024-05-20 16:29:17 - Manhattan-Distance:	Pearson: 0.8569	Spearman: 0.8567
+2024-05-20 16:29:17 - Euclidean-Distance:	Pearson: 0.8575	Spearman: 0.8571
+2024-05-20 16:29:17 - Dot-Product-Similarity:	Pearson: 0.8624	Spearman: 0.8662
+```
\ No newline at end of file