Add MTEB

README.md

@@ -4,6 +4,2398 @@ tags:
 - sentence-transformers
 - feature-extraction
 - sentence-similarity
+- mteb
+model-index:
+- name: SGPT-2.7B-weightedmean-msmarco-specb-bitfit
+  results:
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_counterfactual
+      name: MTEB AmazonCounterfactualClassification (en)
+    metrics:
+    - type: accuracy
+      value: 67.56716417910448
+    - type: ap
+      value: 30.75574629595259
+    - type: f1
+      value: 61.805121301858655
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_polarity
+      name: MTEB AmazonPolarityClassification
+    metrics:
+    - type: accuracy
+      value: 71.439575
+    - type: ap
+      value: 65.91341330532453
+    - type: f1
+      value: 70.90561852619555
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_reviews_multi
+      name: MTEB AmazonReviewsClassification (en)
+    metrics:
+    - type: accuracy
+      value: 35.748000000000005
+    - type: f1
+      value: 35.48576287186347
+  - task:
+      type: Retrieval
+    dataset:
+      type: arguana
+      name: MTEB ArguAna
+    metrics:
+    - type: map_at_1
+      value: 25.96
+    - type: map_at_10
+      value: 41.619
+    - type: map_at_100
+      value: 42.673
+    - type: map_at_1000
+      value: 42.684
+    - type: map_at_3
+      value: 36.569
+    - type: map_at_5
+      value: 39.397
+    - type: mrr_at_1
+      value: 26.316
+    - type: mrr_at_10
+      value: 41.772
+    - type: mrr_at_100
+      value: 42.82
+    - type: mrr_at_1000
+      value: 42.83
+    - type: mrr_at_3
+      value: 36.724000000000004
+    - type: mrr_at_5
+      value: 39.528999999999996
+    - type: ndcg_at_1
+      value: 25.96
+    - type: ndcg_at_10
+      value: 50.491
+    - type: ndcg_at_100
+      value: 54.864999999999995
+    - type: ndcg_at_1000
+      value: 55.10699999999999
+    - type: ndcg_at_3
+      value: 40.053
+    - type: ndcg_at_5
+      value: 45.134
+    - type: precision_at_1
+      value: 25.96
+    - type: precision_at_10
+      value: 7.8950000000000005
+    - type: precision_at_100
+      value: 0.9780000000000001
+    - type: precision_at_1000
+      value: 0.1
+    - type: precision_at_3
+      value: 16.714000000000002
+    - type: precision_at_5
+      value: 12.489
+    - type: recall_at_1
+      value: 25.96
+    - type: recall_at_10
+      value: 78.947
+    - type: recall_at_100
+      value: 97.795
+    - type: recall_at_1000
+      value: 99.644
+    - type: recall_at_3
+      value: 50.141999999999996
+    - type: recall_at_5
+      value: 62.446999999999996
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-p2p
+      name: MTEB ArxivClusteringP2P
+    metrics:
+    - type: v_measure
+      value: 44.72125714642202
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-s2s
+      name: MTEB ArxivClusteringS2S
+    metrics:
+    - type: v_measure
+      value: 35.081451519142064
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/askubuntudupquestions-reranking
+      name: MTEB AskUbuntuDupQuestions
+    metrics:
+    - type: map
+      value: 59.634661990392054
+    - type: mrr
+      value: 73.6813525040672
+  - task:
+      type: STS
+    dataset:
+      type: mteb/biosses-sts
+      name: MTEB BIOSSES
+    metrics:
+    - type: cos_sim_pearson
+      value: 87.42754550496836
+    - type: cos_sim_spearman
+      value: 84.84289705838664
+    - type: euclidean_pearson
+      value: 85.59331970450859
+    - type: euclidean_spearman
+      value: 85.8525586184271
+    - type: manhattan_pearson
+      value: 85.41233134466698
+    - type: manhattan_spearman
+      value: 85.52303303767404
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/banking77
+      name: MTEB Banking77Classification
+    metrics:
+    - type: accuracy
+      value: 83.21753246753246
+    - type: f1
+      value: 83.15394543120915
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-p2p
+      name: MTEB BiorxivClusteringP2P
+    metrics:
+    - type: v_measure
+      value: 34.41414219680629
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-s2s
+      name: MTEB BiorxivClusteringS2S
+    metrics:
+    - type: v_measure
+      value: 30.533275862270028
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackAndroidRetrieval
+    metrics:
+    - type: map_at_1
+      value: 30.808999999999997
+    - type: map_at_10
+      value: 40.617
+    - type: map_at_100
+      value: 41.894999999999996
+    - type: map_at_1000
+      value: 42.025
+    - type: map_at_3
+      value: 37.0
+    - type: map_at_5
+      value: 38.993
+    - type: mrr_at_1
+      value: 37.482
+    - type: mrr_at_10
+      value: 46.497
+    - type: mrr_at_100
+      value: 47.144000000000005
+    - type: mrr_at_1000
+      value: 47.189
+    - type: mrr_at_3
+      value: 43.705
+    - type: mrr_at_5
+      value: 45.193
+    - type: ndcg_at_1
+      value: 37.482
+    - type: ndcg_at_10
+      value: 46.688
+    - type: ndcg_at_100
+      value: 51.726000000000006
+    - type: ndcg_at_1000
+      value: 53.825
+    - type: ndcg_at_3
+      value: 41.242000000000004
+    - type: ndcg_at_5
+      value: 43.657000000000004
+    - type: precision_at_1
+      value: 37.482
+    - type: precision_at_10
+      value: 8.827
+    - type: precision_at_100
+      value: 1.393
+    - type: precision_at_1000
+      value: 0.186
+    - type: precision_at_3
+      value: 19.361
+    - type: precision_at_5
+      value: 14.106
+    - type: recall_at_1
+      value: 30.808999999999997
+    - type: recall_at_10
+      value: 58.47
+    - type: recall_at_100
+      value: 80.51899999999999
+    - type: recall_at_1000
+      value: 93.809
+    - type: recall_at_3
+      value: 42.462
+    - type: recall_at_5
+      value: 49.385
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackEnglishRetrieval
+    metrics:
+    - type: map_at_1
+      value: 26.962000000000003
+    - type: map_at_10
+      value: 36.93
+    - type: map_at_100
+      value: 38.102000000000004
+    - type: map_at_1000
+      value: 38.22
+    - type: map_at_3
+      value: 34.065
+    - type: map_at_5
+      value: 35.72
+    - type: mrr_at_1
+      value: 33.567
+    - type: mrr_at_10
+      value: 42.269
+    - type: mrr_at_100
+      value: 42.99
+    - type: mrr_at_1000
+      value: 43.033
+    - type: mrr_at_3
+      value: 40.064
+    - type: mrr_at_5
+      value: 41.258
+    - type: ndcg_at_1
+      value: 33.567
+    - type: ndcg_at_10
+      value: 42.405
+    - type: ndcg_at_100
+      value: 46.847
+    - type: ndcg_at_1000
+      value: 48.951
+    - type: ndcg_at_3
+      value: 38.312000000000005
+    - type: ndcg_at_5
+      value: 40.242
+    - type: precision_at_1
+      value: 33.567
+    - type: precision_at_10
+      value: 8.032
+    - type: precision_at_100
+      value: 1.295
+    - type: precision_at_1000
+      value: 0.17600000000000002
+    - type: precision_at_3
+      value: 18.662
+    - type: precision_at_5
+      value: 13.299
+    - type: recall_at_1
+      value: 26.962000000000003
+    - type: recall_at_10
+      value: 52.489
+    - type: recall_at_100
+      value: 71.635
+    - type: recall_at_1000
+      value: 85.141
+    - type: recall_at_3
+      value: 40.28
+    - type: recall_at_5
+      value: 45.757
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGamingRetrieval
+    metrics:
+    - type: map_at_1
+      value: 36.318
+    - type: map_at_10
+      value: 47.97
+    - type: map_at_100
+      value: 49.003
+    - type: map_at_1000
+      value: 49.065999999999995
+    - type: map_at_3
+      value: 45.031
+    - type: map_at_5
+      value: 46.633
+    - type: mrr_at_1
+      value: 41.504999999999995
+    - type: mrr_at_10
+      value: 51.431000000000004
+    - type: mrr_at_100
+      value: 52.129000000000005
+    - type: mrr_at_1000
+      value: 52.161
+    - type: mrr_at_3
+      value: 48.934
+    - type: mrr_at_5
+      value: 50.42
+    - type: ndcg_at_1
+      value: 41.504999999999995
+    - type: ndcg_at_10
+      value: 53.676
+    - type: ndcg_at_100
+      value: 57.867000000000004
+    - type: ndcg_at_1000
+      value: 59.166
+    - type: ndcg_at_3
+      value: 48.516
+    - type: ndcg_at_5
+      value: 50.983999999999995
+    - type: precision_at_1
+      value: 41.504999999999995
+    - type: precision_at_10
+      value: 8.608
+    - type: precision_at_100
+      value: 1.1560000000000001
+    - type: precision_at_1000
+      value: 0.133
+    - type: precision_at_3
+      value: 21.462999999999997
+    - type: precision_at_5
+      value: 14.721
+    - type: recall_at_1
+      value: 36.318
+    - type: recall_at_10
+      value: 67.066
+    - type: recall_at_100
+      value: 85.34
+    - type: recall_at_1000
+      value: 94.491
+    - type: recall_at_3
+      value: 53.215999999999994
+    - type: recall_at_5
+      value: 59.214
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGisRetrieval
+    metrics:
+    - type: map_at_1
+      value: 22.167
+    - type: map_at_10
+      value: 29.543999999999997
+    - type: map_at_100
+      value: 30.579
+    - type: map_at_1000
+      value: 30.669999999999998
+    - type: map_at_3
+      value: 26.982
+    - type: map_at_5
+      value: 28.474
+    - type: mrr_at_1
+      value: 24.068
+    - type: mrr_at_10
+      value: 31.237
+    - type: mrr_at_100
+      value: 32.222
+    - type: mrr_at_1000
+      value: 32.292
+    - type: mrr_at_3
+      value: 28.776000000000003
+    - type: mrr_at_5
+      value: 30.233999999999998
+    - type: ndcg_at_1
+      value: 24.068
+    - type: ndcg_at_10
+      value: 33.973
+    - type: ndcg_at_100
+      value: 39.135
+    - type: ndcg_at_1000
+      value: 41.443999999999996
+    - type: ndcg_at_3
+      value: 29.018
+    - type: ndcg_at_5
+      value: 31.558999999999997
+    - type: precision_at_1
+      value: 24.068
+    - type: precision_at_10
+      value: 5.299
+    - type: precision_at_100
+      value: 0.823
+    - type: precision_at_1000
+      value: 0.106
+    - type: precision_at_3
+      value: 12.166
+    - type: precision_at_5
+      value: 8.767999999999999
+    - type: recall_at_1
+      value: 22.167
+    - type: recall_at_10
+      value: 46.115
+    - type: recall_at_100
+      value: 69.867
+    - type: recall_at_1000
+      value: 87.234
+    - type: recall_at_3
+      value: 32.798
+    - type: recall_at_5
+      value: 38.951
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackMathematicaRetrieval
+    metrics:
+    - type: map_at_1
+      value: 12.033000000000001
+    - type: map_at_10
+      value: 19.314
+    - type: map_at_100
+      value: 20.562
+    - type: map_at_1000
+      value: 20.695
+    - type: map_at_3
+      value: 16.946
+    - type: map_at_5
+      value: 18.076999999999998
+    - type: mrr_at_1
+      value: 14.801
+    - type: mrr_at_10
+      value: 22.74
+    - type: mrr_at_100
+      value: 23.876
+    - type: mrr_at_1000
+      value: 23.949
+    - type: mrr_at_3
+      value: 20.211000000000002
+    - type: mrr_at_5
+      value: 21.573
+    - type: ndcg_at_1
+      value: 14.801
+    - type: ndcg_at_10
+      value: 24.038
+    - type: ndcg_at_100
+      value: 30.186
+    - type: ndcg_at_1000
+      value: 33.321
+    - type: ndcg_at_3
+      value: 19.431
+    - type: ndcg_at_5
+      value: 21.34
+    - type: precision_at_1
+      value: 14.801
+    - type: precision_at_10
+      value: 4.776
+    - type: precision_at_100
+      value: 0.897
+    - type: precision_at_1000
+      value: 0.133
+    - type: precision_at_3
+      value: 9.66
+    - type: precision_at_5
+      value: 7.239
+    - type: recall_at_1
+      value: 12.033000000000001
+    - type: recall_at_10
+      value: 35.098
+    - type: recall_at_100
+      value: 62.175000000000004
+    - type: recall_at_1000
+      value: 84.17099999999999
+    - type: recall_at_3
+      value: 22.61
+    - type: recall_at_5
+      value: 27.278999999999996
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackPhysicsRetrieval
+    metrics:
+    - type: map_at_1
+      value: 26.651000000000003
+    - type: map_at_10
+      value: 36.901
+    - type: map_at_100
+      value: 38.249
+    - type: map_at_1000
+      value: 38.361000000000004
+    - type: map_at_3
+      value: 33.891
+    - type: map_at_5
+      value: 35.439
+    - type: mrr_at_1
+      value: 32.724
+    - type: mrr_at_10
+      value: 42.504
+    - type: mrr_at_100
+      value: 43.391999999999996
+    - type: mrr_at_1000
+      value: 43.436
+    - type: mrr_at_3
+      value: 39.989999999999995
+    - type: mrr_at_5
+      value: 41.347
+    - type: ndcg_at_1
+      value: 32.724
+    - type: ndcg_at_10
+      value: 43.007
+    - type: ndcg_at_100
+      value: 48.601
+    - type: ndcg_at_1000
+      value: 50.697
+    - type: ndcg_at_3
+      value: 37.99
+    - type: ndcg_at_5
+      value: 40.083999999999996
+    - type: precision_at_1
+      value: 32.724
+    - type: precision_at_10
+      value: 7.872999999999999
+    - type: precision_at_100
+      value: 1.247
+    - type: precision_at_1000
+      value: 0.16199999999999998
+    - type: precision_at_3
+      value: 18.062
+    - type: precision_at_5
+      value: 12.666
+    - type: recall_at_1
+      value: 26.651000000000003
+    - type: recall_at_10
+      value: 55.674
+    - type: recall_at_100
+      value: 78.904
+    - type: recall_at_1000
+      value: 92.55799999999999
+    - type: recall_at_3
+      value: 41.36
+    - type: recall_at_5
+      value: 46.983999999999995
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackProgrammersRetrieval
+    metrics:
+    - type: map_at_1
+      value: 22.589000000000002
+    - type: map_at_10
+      value: 32.244
+    - type: map_at_100
+      value: 33.46
+    - type: map_at_1000
+      value: 33.593
+    - type: map_at_3
+      value: 29.21
+    - type: map_at_5
+      value: 31.019999999999996
+    - type: mrr_at_1
+      value: 28.425
+    - type: mrr_at_10
+      value: 37.282
+    - type: mrr_at_100
+      value: 38.187
+    - type: mrr_at_1000
+      value: 38.248
+    - type: mrr_at_3
+      value: 34.684
+    - type: mrr_at_5
+      value: 36.123
+    - type: ndcg_at_1
+      value: 28.425
+    - type: ndcg_at_10
+      value: 37.942
+    - type: ndcg_at_100
+      value: 43.443
+    - type: ndcg_at_1000
+      value: 45.995999999999995
+    - type: ndcg_at_3
+      value: 32.873999999999995
+    - type: ndcg_at_5
+      value: 35.325
+    - type: precision_at_1
+      value: 28.425
+    - type: precision_at_10
+      value: 7.1
+    - type: precision_at_100
+      value: 1.166
+    - type: precision_at_1000
+      value: 0.158
+    - type: precision_at_3
+      value: 16.02
+    - type: precision_at_5
+      value: 11.644
+    - type: recall_at_1
+      value: 22.589000000000002
+    - type: recall_at_10
+      value: 50.03999999999999
+    - type: recall_at_100
+      value: 73.973
+    - type: recall_at_1000
+      value: 91.128
+    - type: recall_at_3
+      value: 35.882999999999996
+    - type: recall_at_5
+      value: 42.187999999999995
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackRetrieval
+    metrics:
+    - type: map_at_1
+      value: 23.190833333333334
+    - type: map_at_10
+      value: 31.504916666666666
+    - type: map_at_100
+      value: 32.64908333333334
+    - type: map_at_1000
+      value: 32.77075
+    - type: map_at_3
+      value: 28.82575
+    - type: map_at_5
+      value: 30.2755
+    - type: mrr_at_1
+      value: 27.427499999999995
+    - type: mrr_at_10
+      value: 35.36483333333334
+    - type: mrr_at_100
+      value: 36.23441666666666
+    - type: mrr_at_1000
+      value: 36.297583333333336
+    - type: mrr_at_3
+      value: 32.97966666666667
+    - type: mrr_at_5
+      value: 34.294583333333335
+    - type: ndcg_at_1
+      value: 27.427499999999995
+    - type: ndcg_at_10
+      value: 36.53358333333333
+    - type: ndcg_at_100
+      value: 41.64508333333333
+    - type: ndcg_at_1000
+      value: 44.14499999999999
+    - type: ndcg_at_3
+      value: 31.88908333333333
+    - type: ndcg_at_5
+      value: 33.98433333333333
+    - type: precision_at_1
+      value: 27.427499999999995
+    - type: precision_at_10
+      value: 6.481083333333333
+    - type: precision_at_100
+      value: 1.0610833333333334
+    - type: precision_at_1000
+      value: 0.14691666666666667
+    - type: precision_at_3
+      value: 14.656749999999999
+    - type: precision_at_5
+      value: 10.493583333333332
+    - type: recall_at_1
+      value: 23.190833333333334
+    - type: recall_at_10
+      value: 47.65175
+    - type: recall_at_100
+      value: 70.41016666666667
+    - type: recall_at_1000
+      value: 87.82708333333332
+    - type: recall_at_3
+      value: 34.637583333333325
+    - type: recall_at_5
+      value: 40.05008333333333
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackStatsRetrieval
+    metrics:
+    - type: map_at_1
+      value: 20.409
+    - type: map_at_10
+      value: 26.794
+    - type: map_at_100
+      value: 27.682000000000002
+    - type: map_at_1000
+      value: 27.783
+    - type: map_at_3
+      value: 24.461
+    - type: map_at_5
+      value: 25.668000000000003
+    - type: mrr_at_1
+      value: 22.853
+    - type: mrr_at_10
+      value: 29.296
+    - type: mrr_at_100
+      value: 30.103
+    - type: mrr_at_1000
+      value: 30.179000000000002
+    - type: mrr_at_3
+      value: 27.173000000000002
+    - type: mrr_at_5
+      value: 28.223
+    - type: ndcg_at_1
+      value: 22.853
+    - type: ndcg_at_10
+      value: 31.007
+    - type: ndcg_at_100
+      value: 35.581
+    - type: ndcg_at_1000
+      value: 38.147
+    - type: ndcg_at_3
+      value: 26.590999999999998
+    - type: ndcg_at_5
+      value: 28.43
+    - type: precision_at_1
+      value: 22.853
+    - type: precision_at_10
+      value: 5.031
+    - type: precision_at_100
+      value: 0.7939999999999999
+    - type: precision_at_1000
+      value: 0.11
+    - type: precision_at_3
+      value: 11.401
+    - type: precision_at_5
+      value: 8.16
+    - type: recall_at_1
+      value: 20.409
+    - type: recall_at_10
+      value: 41.766
+    - type: recall_at_100
+      value: 62.964
+    - type: recall_at_1000
+      value: 81.682
+    - type: recall_at_3
+      value: 29.281000000000002
+    - type: recall_at_5
+      value: 33.83
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackTexRetrieval
+    metrics:
+    - type: map_at_1
+      value: 14.549000000000001
+    - type: map_at_10
+      value: 20.315
+    - type: map_at_100
+      value: 21.301000000000002
+    - type: map_at_1000
+      value: 21.425
+    - type: map_at_3
+      value: 18.132
+    - type: map_at_5
+      value: 19.429
+    - type: mrr_at_1
+      value: 17.86
+    - type: mrr_at_10
+      value: 23.860999999999997
+    - type: mrr_at_100
+      value: 24.737000000000002
+    - type: mrr_at_1000
+      value: 24.82
+    - type: mrr_at_3
+      value: 21.685
+    - type: mrr_at_5
+      value: 23.008
+    - type: ndcg_at_1
+      value: 17.86
+    - type: ndcg_at_10
+      value: 24.396
+    - type: ndcg_at_100
+      value: 29.328
+    - type: ndcg_at_1000
+      value: 32.486
+    - type: ndcg_at_3
+      value: 20.375
+    - type: ndcg_at_5
+      value: 22.411
+    - type: precision_at_1
+      value: 17.86
+    - type: precision_at_10
+      value: 4.47
+    - type: precision_at_100
+      value: 0.8099999999999999
+    - type: precision_at_1000
+      value: 0.125
+    - type: precision_at_3
+      value: 9.475
+    - type: precision_at_5
+      value: 7.170999999999999
+    - type: recall_at_1
+      value: 14.549000000000001
+    - type: recall_at_10
+      value: 33.365
+    - type: recall_at_100
+      value: 55.797
+    - type: recall_at_1000
+      value: 78.632
+    - type: recall_at_3
+      value: 22.229
+    - type: recall_at_5
+      value: 27.339000000000002
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackUnixRetrieval
+    metrics:
+    - type: map_at_1
+      value: 23.286
+    - type: map_at_10
+      value: 30.728
+    - type: map_at_100
+      value: 31.840000000000003
+    - type: map_at_1000
+      value: 31.953
+    - type: map_at_3
+      value: 28.302
+    - type: map_at_5
+      value: 29.615000000000002
+    - type: mrr_at_1
+      value: 27.239
+    - type: mrr_at_10
+      value: 34.408
+    - type: mrr_at_100
+      value: 35.335
+    - type: mrr_at_1000
+      value: 35.405
+    - type: mrr_at_3
+      value: 32.151999999999994
+    - type: mrr_at_5
+      value: 33.355000000000004
+    - type: ndcg_at_1
+      value: 27.239
+    - type: ndcg_at_10
+      value: 35.324
+    - type: ndcg_at_100
+      value: 40.866
+    - type: ndcg_at_1000
+      value: 43.584
+    - type: ndcg_at_3
+      value: 30.898999999999997
+    - type: ndcg_at_5
+      value: 32.812999999999995
+    - type: precision_at_1
+      value: 27.239
+    - type: precision_at_10
+      value: 5.896
+    - type: precision_at_100
+      value: 0.979
+    - type: precision_at_1000
+      value: 0.133
+    - type: precision_at_3
+      value: 13.713000000000001
+    - type: precision_at_5
+      value: 9.683
+    - type: recall_at_1
+      value: 23.286
+    - type: recall_at_10
+      value: 45.711
+    - type: recall_at_100
+      value: 70.611
+    - type: recall_at_1000
+      value: 90.029
+    - type: recall_at_3
+      value: 33.615
+    - type: recall_at_5
+      value: 38.41
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWebmastersRetrieval
+    metrics:
+    - type: map_at_1
+      value: 23.962
+    - type: map_at_10
+      value: 31.942999999999998
+    - type: map_at_100
+      value: 33.384
+    - type: map_at_1000
+      value: 33.611000000000004
+    - type: map_at_3
+      value: 29.243000000000002
+    - type: map_at_5
+      value: 30.446
+    - type: mrr_at_1
+      value: 28.458
+    - type: mrr_at_10
+      value: 36.157000000000004
+    - type: mrr_at_100
+      value: 37.092999999999996
+    - type: mrr_at_1000
+      value: 37.163000000000004
+    - type: mrr_at_3
+      value: 33.86
+    - type: mrr_at_5
+      value: 35.086
+    - type: ndcg_at_1
+      value: 28.458
+    - type: ndcg_at_10
+      value: 37.201
+    - type: ndcg_at_100
+      value: 42.591
+    - type: ndcg_at_1000
+      value: 45.539
+    - type: ndcg_at_3
+      value: 32.889
+    - type: ndcg_at_5
+      value: 34.483000000000004
+    - type: precision_at_1
+      value: 28.458
+    - type: precision_at_10
+      value: 7.332
+    - type: precision_at_100
+      value: 1.437
+    - type: precision_at_1000
+      value: 0.233
+    - type: precision_at_3
+      value: 15.547
+    - type: precision_at_5
+      value: 11.146
+    - type: recall_at_1
+      value: 23.962
+    - type: recall_at_10
+      value: 46.751
+    - type: recall_at_100
+      value: 71.626
+    - type: recall_at_1000
+      value: 90.93900000000001
+    - type: recall_at_3
+      value: 34.138000000000005
+    - type: recall_at_5
+      value: 38.673
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWordpressRetrieval
+    metrics:
+    - type: map_at_1
+      value: 18.555
+    - type: map_at_10
+      value: 24.759
+    - type: map_at_100
+      value: 25.732
+    - type: map_at_1000
+      value: 25.846999999999998
+    - type: map_at_3
+      value: 22.646
+    - type: map_at_5
+      value: 23.791999999999998
+    - type: mrr_at_1
+      value: 20.148
+    - type: mrr_at_10
+      value: 26.695999999999998
+    - type: mrr_at_100
+      value: 27.605
+    - type: mrr_at_1000
+      value: 27.695999999999998
+    - type: mrr_at_3
+      value: 24.522
+    - type: mrr_at_5
+      value: 25.715
+    - type: ndcg_at_1
+      value: 20.148
+    - type: ndcg_at_10
+      value: 28.746
+    - type: ndcg_at_100
+      value: 33.57
+    - type: ndcg_at_1000
+      value: 36.584
+    - type: ndcg_at_3
+      value: 24.532
+    - type: ndcg_at_5
+      value: 26.484
+    - type: precision_at_1
+      value: 20.148
+    - type: precision_at_10
+      value: 4.529
+    - type: precision_at_100
+      value: 0.736
+    - type: precision_at_1000
+      value: 0.108
+    - type: precision_at_3
+      value: 10.351
+    - type: precision_at_5
+      value: 7.32
+    - type: recall_at_1
+      value: 18.555
+    - type: recall_at_10
+      value: 39.275999999999996
+    - type: recall_at_100
+      value: 61.511
+    - type: recall_at_1000
+      value: 84.111
+    - type: recall_at_3
+      value: 27.778999999999996
+    - type: recall_at_5
+      value: 32.591
+  - task:
+      type: Retrieval
+    dataset:
+      type: climate-fever
+      name: MTEB ClimateFEVER
+    metrics:
+    - type: map_at_1
+      value: 10.366999999999999
+    - type: map_at_10
+      value: 18.953999999999997
+    - type: map_at_100
+      value: 20.674999999999997
+    - type: map_at_1000
+      value: 20.868000000000002
+    - type: map_at_3
+      value: 15.486
+    - type: map_at_5
+      value: 17.347
+    - type: mrr_at_1
+      value: 23.257
+    - type: mrr_at_10
+      value: 35.419
+    - type: mrr_at_100
+      value: 36.361
+    - type: mrr_at_1000
+      value: 36.403
+    - type: mrr_at_3
+      value: 31.747999999999998
+    - type: mrr_at_5
+      value: 34.077
+    - type: ndcg_at_1
+      value: 23.257
+    - type: ndcg_at_10
+      value: 27.11
+    - type: ndcg_at_100
+      value: 33.981
+    - type: ndcg_at_1000
+      value: 37.444
+    - type: ndcg_at_3
+      value: 21.471999999999998
+    - type: ndcg_at_5
+      value: 23.769000000000002
+    - type: precision_at_1
+      value: 23.257
+    - type: precision_at_10
+      value: 8.704
+    - type: precision_at_100
+      value: 1.606
+    - type: precision_at_1000
+      value: 0.22499999999999998
+    - type: precision_at_3
+      value: 16.287
+    - type: precision_at_5
+      value: 13.068
+    - type: recall_at_1
+      value: 10.366999999999999
+    - type: recall_at_10
+      value: 33.706
+    - type: recall_at_100
+      value: 57.375
+    - type: recall_at_1000
+      value: 76.79
+    - type: recall_at_3
+      value: 20.18
+    - type: recall_at_5
+      value: 26.215
+  - task:
+      type: Retrieval
+    dataset:
+      type: dbpedia-entity
+      name: MTEB DBPedia
+    metrics:
+    - type: map_at_1
+      value: 8.246
+    - type: map_at_10
+      value: 15.979
+    - type: map_at_100
+      value: 21.025
+    - type: map_at_1000
+      value: 22.189999999999998
+    - type: map_at_3
+      value: 11.997
+    - type: map_at_5
+      value: 13.697000000000001
+    - type: mrr_at_1
+      value: 60.75000000000001
+    - type: mrr_at_10
+      value: 68.70100000000001
+    - type: mrr_at_100
+      value: 69.1
+    - type: mrr_at_1000
+      value: 69.111
+    - type: mrr_at_3
+      value: 66.583
+    - type: mrr_at_5
+      value: 67.87100000000001
+    - type: ndcg_at_1
+      value: 49.75
+    - type: ndcg_at_10
+      value: 34.702
+    - type: ndcg_at_100
+      value: 37.607
+    - type: ndcg_at_1000
+      value: 44.322
+    - type: ndcg_at_3
+      value: 39.555
+    - type: ndcg_at_5
+      value: 36.684
+    - type: precision_at_1
+      value: 60.75000000000001
+    - type: precision_at_10
+      value: 26.625
+    - type: precision_at_100
+      value: 7.969999999999999
+    - type: precision_at_1000
+      value: 1.678
+    - type: precision_at_3
+      value: 41.833
+    - type: precision_at_5
+      value: 34.5
+    - type: recall_at_1
+      value: 8.246
+    - type: recall_at_10
+      value: 20.968
+    - type: recall_at_100
+      value: 42.065000000000005
+    - type: recall_at_1000
+      value: 63.671
+    - type: recall_at_3
+      value: 13.039000000000001
+    - type: recall_at_5
+      value: 16.042
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/emotion
+      name: MTEB EmotionClassification
+    metrics:
+    - type: accuracy
+      value: 49.214999999999996
+    - type: f1
+      value: 44.85952451163755
+  - task:
+      type: Retrieval
+    dataset:
+      type: fever
+      name: MTEB FEVER
+    metrics:
+    - type: map_at_1
+      value: 56.769000000000005
+    - type: map_at_10
+      value: 67.30199999999999
+    - type: map_at_100
+      value: 67.692
+    - type: map_at_1000
+      value: 67.712
+    - type: map_at_3
+      value: 65.346
+    - type: map_at_5
+      value: 66.574
+    - type: mrr_at_1
+      value: 61.370999999999995
+    - type: mrr_at_10
+      value: 71.875
+    - type: mrr_at_100
+      value: 72.195
+    - type: mrr_at_1000
+      value: 72.206
+    - type: mrr_at_3
+      value: 70.04
+    - type: mrr_at_5
+      value: 71.224
+    - type: ndcg_at_1
+      value: 61.370999999999995
+    - type: ndcg_at_10
+      value: 72.731
+    - type: ndcg_at_100
+      value: 74.468
+    - type: ndcg_at_1000
+      value: 74.91600000000001
+    - type: ndcg_at_3
+      value: 69.077
+    - type: ndcg_at_5
+      value: 71.111
+    - type: precision_at_1
+      value: 61.370999999999995
+    - type: precision_at_10
+      value: 9.325999999999999
+    - type: precision_at_100
+      value: 1.03
+    - type: precision_at_1000
+      value: 0.108
+    - type: precision_at_3
+      value: 27.303
+    - type: precision_at_5
+      value: 17.525
+    - type: recall_at_1
+      value: 56.769000000000005
+    - type: recall_at_10
+      value: 85.06
+    - type: recall_at_100
+      value: 92.767
+    - type: recall_at_1000
+      value: 95.933
+    - type: recall_at_3
+      value: 75.131
+    - type: recall_at_5
+      value: 80.17
+  - task:
+      type: Retrieval
+    dataset:
+      type: fiqa
+      name: MTEB FiQA2018
+    metrics:
+    - type: map_at_1
+      value: 15.753
+    - type: map_at_10
+      value: 25.875999999999998
+    - type: map_at_100
+      value: 27.415
+    - type: map_at_1000
+      value: 27.590999999999998
+    - type: map_at_3
+      value: 22.17
+    - type: map_at_5
+      value: 24.236
+    - type: mrr_at_1
+      value: 31.019000000000002
+    - type: mrr_at_10
+      value: 39.977000000000004
+    - type: mrr_at_100
+      value: 40.788999999999994
+    - type: mrr_at_1000
+      value: 40.832
+    - type: mrr_at_3
+      value: 37.088
+    - type: mrr_at_5
+      value: 38.655
+    - type: ndcg_at_1
+      value: 31.019000000000002
+    - type: ndcg_at_10
+      value: 33.286
+    - type: ndcg_at_100
+      value: 39.528999999999996
+    - type: ndcg_at_1000
+      value: 42.934
+    - type: ndcg_at_3
+      value: 29.29
+    - type: ndcg_at_5
+      value: 30.615
+    - type: precision_at_1
+      value: 31.019000000000002
+    - type: precision_at_10
+      value: 9.383
+    - type: precision_at_100
+      value: 1.6019999999999999
+    - type: precision_at_1000
+      value: 0.22200000000000003
+    - type: precision_at_3
+      value: 19.753
+    - type: precision_at_5
+      value: 14.815000000000001
+    - type: recall_at_1
+      value: 15.753
+    - type: recall_at_10
+      value: 40.896
+    - type: recall_at_100
+      value: 64.443
+    - type: recall_at_1000
+      value: 85.218
+    - type: recall_at_3
+      value: 26.526
+    - type: recall_at_5
+      value: 32.452999999999996
+  - task:
+      type: Retrieval
+    dataset:
+      type: hotpotqa
+      name: MTEB HotpotQA
+    metrics:
+    - type: map_at_1
+      value: 32.153999999999996
+    - type: map_at_10
+      value: 43.651
+    - type: map_at_100
+      value: 44.41
+    - type: map_at_1000
+      value: 44.487
+    - type: map_at_3
+      value: 41.239
+    - type: map_at_5
+      value: 42.659000000000006
+    - type: mrr_at_1
+      value: 64.30799999999999
+    - type: mrr_at_10
+      value: 71.22500000000001
+    - type: mrr_at_100
+      value: 71.57
+    - type: mrr_at_1000
+      value: 71.59100000000001
+    - type: mrr_at_3
+      value: 69.95
+    - type: mrr_at_5
+      value: 70.738
+    - type: ndcg_at_1
+      value: 64.30799999999999
+    - type: ndcg_at_10
+      value: 52.835
+    - type: ndcg_at_100
+      value: 55.840999999999994
+    - type: ndcg_at_1000
+      value: 57.484
+    - type: ndcg_at_3
+      value: 49.014
+    - type: ndcg_at_5
+      value: 51.01599999999999
+    - type: precision_at_1
+      value: 64.30799999999999
+    - type: precision_at_10
+      value: 10.77
+    - type: precision_at_100
+      value: 1.315
+    - type: precision_at_1000
+      value: 0.153
+    - type: precision_at_3
+      value: 30.223
+    - type: precision_at_5
+      value: 19.716
+    - type: recall_at_1
+      value: 32.153999999999996
+    - type: recall_at_10
+      value: 53.849000000000004
+    - type: recall_at_100
+      value: 65.75999999999999
+    - type: recall_at_1000
+      value: 76.705
+    - type: recall_at_3
+      value: 45.334
+    - type: recall_at_5
+      value: 49.291000000000004
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/imdb
+      name: MTEB ImdbClassification
+    metrics:
+    - type: accuracy
+      value: 63.5316
+    - type: ap
+      value: 58.90084300359825
+    - type: f1
+      value: 63.35727889030892
+  - task:
+      type: Retrieval
+    dataset:
+      type: msmarco
+      name: MTEB MSMARCO
+    metrics:
+    - type: map_at_1
+      value: 20.566000000000003
+    - type: map_at_10
+      value: 32.229
+    - type: map_at_100
+      value: 33.445
+    - type: map_at_1000
+      value: 33.501
+    - type: map_at_3
+      value: 28.504
+    - type: map_at_5
+      value: 30.681000000000004
+    - type: mrr_at_1
+      value: 21.218
+    - type: mrr_at_10
+      value: 32.816
+    - type: mrr_at_100
+      value: 33.986
+    - type: mrr_at_1000
+      value: 34.035
+    - type: mrr_at_3
+      value: 29.15
+    - type: mrr_at_5
+      value: 31.290000000000003
+    - type: ndcg_at_1
+      value: 21.218
+    - type: ndcg_at_10
+      value: 38.832
+    - type: ndcg_at_100
+      value: 44.743
+    - type: ndcg_at_1000
+      value: 46.138
+    - type: ndcg_at_3
+      value: 31.232
+    - type: ndcg_at_5
+      value: 35.099999999999994
+    - type: precision_at_1
+      value: 21.218
+    - type: precision_at_10
+      value: 6.186
+    - type: precision_at_100
+      value: 0.914
+    - type: precision_at_1000
+      value: 0.10300000000000001
+    - type: precision_at_3
+      value: 13.314
+    - type: precision_at_5
+      value: 9.943
+    - type: recall_at_1
+      value: 20.566000000000003
+    - type: recall_at_10
+      value: 59.192
+    - type: recall_at_100
+      value: 86.626
+    - type: recall_at_1000
+      value: 97.283
+    - type: recall_at_3
+      value: 38.492
+    - type: recall_at_5
+      value: 47.760000000000005
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_domain
+      name: MTEB MTOPDomainClassification (en)
+    metrics:
+    - type: accuracy
+      value: 92.56269949840402
+    - type: f1
+      value: 92.1020975473988
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_intent
+      name: MTEB MTOPIntentClassification (en)
+    metrics:
+    - type: accuracy
+      value: 71.8467852257182
+    - type: f1
+      value: 53.652719348592015
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (en)
+    metrics:
+    - type: accuracy
+      value: 69.00806993947546
+    - type: f1
+      value: 67.41429618885515
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (en)
+    metrics:
+    - type: accuracy
+      value: 75.90114324142569
+    - type: f1
+      value: 76.25183590651454
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-p2p
+      name: MTEB MedrxivClusteringP2P
+    metrics:
+    - type: v_measure
+      value: 31.350109978273395
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-s2s
+      name: MTEB MedrxivClusteringS2S
+    metrics:
+    - type: v_measure
+      value: 28.768923695767327
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/mind_small
+      name: MTEB MindSmallReranking
+    metrics:
+    - type: map
+      value: 31.716396735210754
+    - type: mrr
+      value: 32.88970538547634
+  - task:
+      type: Retrieval
+    dataset:
+      type: nfcorpus
+      name: MTEB NFCorpus
+    metrics:
+    - type: map_at_1
+      value: 5.604
+    - type: map_at_10
+      value: 12.379999999999999
+    - type: map_at_100
+      value: 15.791
+    - type: map_at_1000
+      value: 17.327
+    - type: map_at_3
+      value: 9.15
+    - type: map_at_5
+      value: 10.599
+    - type: mrr_at_1
+      value: 45.201
+    - type: mrr_at_10
+      value: 53.374
+    - type: mrr_at_100
+      value: 54.089
+    - type: mrr_at_1000
+      value: 54.123
+    - type: mrr_at_3
+      value: 51.44499999999999
+    - type: mrr_at_5
+      value: 52.59
+    - type: ndcg_at_1
+      value: 42.879
+    - type: ndcg_at_10
+      value: 33.891
+    - type: ndcg_at_100
+      value: 31.391999999999996
+    - type: ndcg_at_1000
+      value: 40.36
+    - type: ndcg_at_3
+      value: 39.076
+    - type: ndcg_at_5
+      value: 37.047000000000004
+    - type: precision_at_1
+      value: 44.582
+    - type: precision_at_10
+      value: 25.294
+    - type: precision_at_100
+      value: 8.285
+    - type: precision_at_1000
+      value: 2.1479999999999997
+    - type: precision_at_3
+      value: 36.120000000000005
+    - type: precision_at_5
+      value: 31.95
+    - type: recall_at_1
+      value: 5.604
+    - type: recall_at_10
+      value: 16.239
+    - type: recall_at_100
+      value: 32.16
+    - type: recall_at_1000
+      value: 64.513
+    - type: recall_at_3
+      value: 10.406
+    - type: recall_at_5
+      value: 12.684999999999999
+  - task:
+      type: Retrieval
+    dataset:
+      type: nq
+      name: MTEB NQ
+    metrics:
+    - type: map_at_1
+      value: 25.881
+    - type: map_at_10
+      value: 39.501
+    - type: map_at_100
+      value: 40.615
+    - type: map_at_1000
+      value: 40.661
+    - type: map_at_3
+      value: 35.559000000000005
+    - type: map_at_5
+      value: 37.773
+    - type: mrr_at_1
+      value: 29.229
+    - type: mrr_at_10
+      value: 41.955999999999996
+    - type: mrr_at_100
+      value: 42.86
+    - type: mrr_at_1000
+      value: 42.893
+    - type: mrr_at_3
+      value: 38.562000000000005
+    - type: mrr_at_5
+      value: 40.542
+    - type: ndcg_at_1
+      value: 29.2
+    - type: ndcg_at_10
+      value: 46.703
+    - type: ndcg_at_100
+      value: 51.644
+    - type: ndcg_at_1000
+      value: 52.771
+    - type: ndcg_at_3
+      value: 39.141999999999996
+    - type: ndcg_at_5
+      value: 42.892
+    - type: precision_at_1
+      value: 29.2
+    - type: precision_at_10
+      value: 7.920000000000001
+    - type: precision_at_100
+      value: 1.0659999999999998
+    - type: precision_at_1000
+      value: 0.117
+    - type: precision_at_3
+      value: 18.105
+    - type: precision_at_5
+      value: 13.036
+    - type: recall_at_1
+      value: 25.881
+    - type: recall_at_10
+      value: 66.266
+    - type: recall_at_100
+      value: 88.116
+    - type: recall_at_1000
+      value: 96.58200000000001
+    - type: recall_at_3
+      value: 46.526
+    - type: recall_at_5
+      value: 55.154
+  - task:
+      type: Retrieval
+    dataset:
+      type: quora
+      name: MTEB QuoraRetrieval
+    metrics:
+    - type: map_at_1
+      value: 67.553
+    - type: map_at_10
+      value: 81.34
+    - type: map_at_100
+      value: 82.002
+    - type: map_at_1000
+      value: 82.027
+    - type: map_at_3
+      value: 78.281
+    - type: map_at_5
+      value: 80.149
+    - type: mrr_at_1
+      value: 77.72
+    - type: mrr_at_10
+      value: 84.733
+    - type: mrr_at_100
+      value: 84.878
+    - type: mrr_at_1000
+      value: 84.879
+    - type: mrr_at_3
+      value: 83.587
+    - type: mrr_at_5
+      value: 84.32600000000001
+    - type: ndcg_at_1
+      value: 77.75
+    - type: ndcg_at_10
+      value: 85.603
+    - type: ndcg_at_100
+      value: 87.069
+    - type: ndcg_at_1000
+      value: 87.25
+    - type: ndcg_at_3
+      value: 82.303
+    - type: ndcg_at_5
+      value: 84.03699999999999
+    - type: precision_at_1
+      value: 77.75
+    - type: precision_at_10
+      value: 13.04
+    - type: precision_at_100
+      value: 1.5070000000000001
+    - type: precision_at_1000
+      value: 0.156
+    - type: precision_at_3
+      value: 35.903
+    - type: precision_at_5
+      value: 23.738
+    - type: recall_at_1
+      value: 67.553
+    - type: recall_at_10
+      value: 93.903
+    - type: recall_at_100
+      value: 99.062
+    - type: recall_at_1000
+      value: 99.935
+    - type: recall_at_3
+      value: 84.58099999999999
+    - type: recall_at_5
+      value: 89.316
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering
+      name: MTEB RedditClustering
+    metrics:
+    - type: v_measure
+      value: 46.46887711230235
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering-p2p
+      name: MTEB RedditClusteringP2P
+    metrics:
+    - type: v_measure
+      value: 54.166876298246926
+  - task:
+      type: Retrieval
+    dataset:
+      type: scidocs
+      name: MTEB SCIDOCS
+    metrics:
+    - type: map_at_1
+      value: 4.053
+    - type: map_at_10
+      value: 9.693999999999999
+    - type: map_at_100
+      value: 11.387
+    - type: map_at_1000
+      value: 11.654
+    - type: map_at_3
+      value: 7.053
+    - type: map_at_5
+      value: 8.439
+    - type: mrr_at_1
+      value: 19.900000000000002
+    - type: mrr_at_10
+      value: 29.359
+    - type: mrr_at_100
+      value: 30.484
+    - type: mrr_at_1000
+      value: 30.553
+    - type: mrr_at_3
+      value: 26.200000000000003
+    - type: mrr_at_5
+      value: 28.115000000000002
+    - type: ndcg_at_1
+      value: 19.900000000000002
+    - type: ndcg_at_10
+      value: 16.575
+    - type: ndcg_at_100
+      value: 23.655
+    - type: ndcg_at_1000
+      value: 28.853
+    - type: ndcg_at_3
+      value: 15.848
+    - type: ndcg_at_5
+      value: 14.026
+    - type: precision_at_1
+      value: 19.900000000000002
+    - type: precision_at_10
+      value: 8.450000000000001
+    - type: precision_at_100
+      value: 1.872
+    - type: precision_at_1000
+      value: 0.313
+    - type: precision_at_3
+      value: 14.667
+    - type: precision_at_5
+      value: 12.32
+    - type: recall_at_1
+      value: 4.053
+    - type: recall_at_10
+      value: 17.169999999999998
+    - type: recall_at_100
+      value: 38.025
+    - type: recall_at_1000
+      value: 63.571999999999996
+    - type: recall_at_3
+      value: 8.903
+    - type: recall_at_5
+      value: 12.477
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sickr-sts
+      name: MTEB SICK-R
+    metrics:
+    - type: cos_sim_pearson
+      value: 77.7548748519677
+    - type: cos_sim_spearman
+      value: 68.19926431966059
+    - type: euclidean_pearson
+      value: 71.69016204991725
+    - type: euclidean_spearman
+      value: 66.98099673026834
+    - type: manhattan_pearson
+      value: 71.62994072488664
+    - type: manhattan_spearman
+      value: 67.03435950744577
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts12-sts
+      name: MTEB STS12
+    metrics:
+    - type: cos_sim_pearson
+      value: 75.91051402657887
+    - type: cos_sim_spearman
+      value: 66.99390786191645
+    - type: euclidean_pearson
+      value: 71.54128036454578
+    - type: euclidean_spearman
+      value: 69.25605675649068
+    - type: manhattan_pearson
+      value: 71.60981030780171
+    - type: manhattan_spearman
+      value: 69.27513670128046
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts13-sts
+      name: MTEB STS13
+    metrics:
+    - type: cos_sim_pearson
+      value: 77.23835466417793
+    - type: cos_sim_spearman
+      value: 77.57623085766706
+    - type: euclidean_pearson
+      value: 77.5090992200725
+    - type: euclidean_spearman
+      value: 77.88601688144924
+    - type: manhattan_pearson
+      value: 77.39045060647423
+    - type: manhattan_spearman
+      value: 77.77552718279098
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts14-sts
+      name: MTEB STS14
+    metrics:
+    - type: cos_sim_pearson
+      value: 77.91692485139602
+    - type: cos_sim_spearman
+      value: 72.78258293483495
+    - type: euclidean_pearson
+      value: 74.64773017077789
+    - type: euclidean_spearman
+      value: 71.81662299104619
+    - type: manhattan_pearson
+      value: 74.71043337995533
+    - type: manhattan_spearman
+      value: 71.83960860845646
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts15-sts
+      name: MTEB STS15
+    metrics:
+    - type: cos_sim_pearson
+      value: 82.13422113617578
+    - type: cos_sim_spearman
+      value: 82.61707296911949
+    - type: euclidean_pearson
+      value: 81.42487480400861
+    - type: euclidean_spearman
+      value: 82.17970991273835
+    - type: manhattan_pearson
+      value: 81.41985055477845
+    - type: manhattan_spearman
+      value: 82.15823204362937
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts16-sts
+      name: MTEB STS16
+    metrics:
+    - type: cos_sim_pearson
+      value: 79.07989542843826
+    - type: cos_sim_spearman
+      value: 80.09839524406284
+    - type: euclidean_pearson
+      value: 76.43186028364195
+    - type: euclidean_spearman
+      value: 76.76720323266471
+    - type: manhattan_pearson
+      value: 76.4674747409161
+    - type: manhattan_spearman
+      value: 76.81797407068667
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts17-crosslingual-sts
+      name: MTEB STS17 (en-en)
+    metrics:
+    - type: cos_sim_pearson
+      value: 87.0420983224933
+    - type: cos_sim_spearman
+      value: 87.25017540413702
+    - type: euclidean_pearson
+      value: 84.56384596473421
+    - type: euclidean_spearman
+      value: 84.72557417564886
+    - type: manhattan_pearson
+      value: 84.7329954474549
+    - type: manhattan_spearman
+      value: 84.75071371008909
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts22-crosslingual-sts
+      name: MTEB STS22 (en)
+    metrics:
+    - type: cos_sim_pearson
+      value: 68.47031320016424
+    - type: cos_sim_spearman
+      value: 68.7486910762485
+    - type: euclidean_pearson
+      value: 71.30330985913915
+    - type: euclidean_spearman
+      value: 71.59666258520735
+    - type: manhattan_pearson
+      value: 71.4423884279027
+    - type: manhattan_spearman
+      value: 71.67460706861044
+  - task:
+      type: STS
+    dataset:
+      type: mteb/stsbenchmark-sts
+      name: MTEB STSBenchmark
+    metrics:
+    - type: cos_sim_pearson
+      value: 80.79514366062675
+    - type: cos_sim_spearman
+      value: 79.20585637461048
+    - type: euclidean_pearson
+      value: 78.6591557395699
+    - type: euclidean_spearman
+      value: 77.86455794285718
+    - type: manhattan_pearson
+      value: 78.67754806486865
+    - type: manhattan_spearman
+      value: 77.88178687200732
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/scidocs-reranking
+      name: MTEB SciDocsRR
+    metrics:
+    - type: map
+      value: 77.71580844366375
+    - type: mrr
+      value: 93.04215845882513
+  - task:
+      type: Retrieval
+    dataset:
+      type: scifact
+      name: MTEB SciFact
+    metrics:
+    - type: map_at_1
+      value: 56.39999999999999
+    - type: map_at_10
+      value: 65.701
+    - type: map_at_100
+      value: 66.32000000000001
+    - type: map_at_1000
+      value: 66.34100000000001
+    - type: map_at_3
+      value: 62.641999999999996
+    - type: map_at_5
+      value: 64.342
+    - type: mrr_at_1
+      value: 58.667
+    - type: mrr_at_10
+      value: 66.45299999999999
+    - type: mrr_at_100
+      value: 66.967
+    - type: mrr_at_1000
+      value: 66.988
+    - type: mrr_at_3
+      value: 64.11099999999999
+    - type: mrr_at_5
+      value: 65.411
+    - type: ndcg_at_1
+      value: 58.667
+    - type: ndcg_at_10
+      value: 70.165
+    - type: ndcg_at_100
+      value: 72.938
+    - type: ndcg_at_1000
+      value: 73.456
+    - type: ndcg_at_3
+      value: 64.79
+    - type: ndcg_at_5
+      value: 67.28
+    - type: precision_at_1
+      value: 58.667
+    - type: precision_at_10
+      value: 9.4
+    - type: precision_at_100
+      value: 1.087
+    - type: precision_at_1000
+      value: 0.11299999999999999
+    - type: precision_at_3
+      value: 24.889
+    - type: precision_at_5
+      value: 16.667
+    - type: recall_at_1
+      value: 56.39999999999999
+    - type: recall_at_10
+      value: 83.122
+    - type: recall_at_100
+      value: 95.667
+    - type: recall_at_1000
+      value: 99.667
+    - type: recall_at_3
+      value: 68.378
+    - type: recall_at_5
+      value: 74.68299999999999
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/sprintduplicatequestions-pairclassification
+      name: MTEB SprintDuplicateQuestions
+    metrics:
+    - type: cos_sim_accuracy
+      value: 99.76831683168317
+    - type: cos_sim_ap
+      value: 93.47124923047998
+    - type: cos_sim_f1
+      value: 88.06122448979592
+    - type: cos_sim_precision
+      value: 89.89583333333333
+    - type: cos_sim_recall
+      value: 86.3
+    - type: dot_accuracy
+      value: 99.57326732673268
+    - type: dot_ap
+      value: 84.06577868167207
+    - type: dot_f1
+      value: 77.82629791363416
+    - type: dot_precision
+      value: 75.58906691800189
+    - type: dot_recall
+      value: 80.2
+    - type: euclidean_accuracy
+      value: 99.74257425742574
+    - type: euclidean_ap
+      value: 92.1904681653555
+    - type: euclidean_f1
+      value: 86.74821610601427
+    - type: euclidean_precision
+      value: 88.46153846153845
+    - type: euclidean_recall
+      value: 85.1
+    - type: manhattan_accuracy
+      value: 99.74554455445545
+    - type: manhattan_ap
+      value: 92.4337790809948
+    - type: manhattan_f1
+      value: 86.86765457332653
+    - type: manhattan_precision
+      value: 88.81922675026124
+    - type: manhattan_recall
+      value: 85.0
+    - type: max_accuracy
+      value: 99.76831683168317
+    - type: max_ap
+      value: 93.47124923047998
+    - type: max_f1
+      value: 88.06122448979592
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering
+      name: MTEB StackExchangeClustering
+    metrics:
+    - type: v_measure
+      value: 59.194098673976484
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering-p2p
+      name: MTEB StackExchangeClusteringP2P
+    metrics:
+    - type: v_measure
+      value: 32.5744032578115
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/stackoverflowdupquestions-reranking
+      name: MTEB StackOverflowDupQuestions
+    metrics:
+    - type: map
+      value: 49.61186384154483
+    - type: mrr
+      value: 50.55424253034547
+  - task:
+      type: Summarization
+    dataset:
+      type: mteb/summeval
+      name: MTEB SummEval
+    metrics:
+    - type: cos_sim_pearson
+      value: 26.047224542079068
+    - type: cos_sim_spearman
+      value: 27.870478281195467
+    - type: dot_pearson
+      value: 25.182420685701217
+    - type: dot_spearman
+      value: 25.116243491984985
+  - task:
+      type: Retrieval
+    dataset:
+      type: trec-covid
+      name: MTEB TRECCOVID
+    metrics:
+    - type: map_at_1
+      value: 0.22300000000000003
+    - type: map_at_10
+      value: 1.762
+    - type: map_at_100
+      value: 9.984
+    - type: map_at_1000
+      value: 24.265
+    - type: map_at_3
+      value: 0.631
+    - type: map_at_5
+      value: 0.9950000000000001
+    - type: mrr_at_1
+      value: 88.0
+    - type: mrr_at_10
+      value: 92.833
+    - type: mrr_at_100
+      value: 92.833
+    - type: mrr_at_1000
+      value: 92.833
+    - type: mrr_at_3
+      value: 92.333
+    - type: mrr_at_5
+      value: 92.833
+    - type: ndcg_at_1
+      value: 83.0
+    - type: ndcg_at_10
+      value: 75.17
+    - type: ndcg_at_100
+      value: 55.432
+    - type: ndcg_at_1000
+      value: 49.482
+    - type: ndcg_at_3
+      value: 82.184
+    - type: ndcg_at_5
+      value: 79.712
+    - type: precision_at_1
+      value: 88.0
+    - type: precision_at_10
+      value: 78.60000000000001
+    - type: precision_at_100
+      value: 56.56
+    - type: precision_at_1000
+      value: 22.334
+    - type: precision_at_3
+      value: 86.667
+    - type: precision_at_5
+      value: 83.6
+    - type: recall_at_1
+      value: 0.22300000000000003
+    - type: recall_at_10
+      value: 1.9879999999999998
+    - type: recall_at_100
+      value: 13.300999999999998
+    - type: recall_at_1000
+      value: 46.587
+    - type: recall_at_3
+      value: 0.6629999999999999
+    - type: recall_at_5
+      value: 1.079
+  - task:
+      type: Retrieval
+    dataset:
+      type: webis-touche2020
+      name: MTEB Touche2020
+    metrics:
+    - type: map_at_1
+      value: 3.047
+    - type: map_at_10
+      value: 8.792
+    - type: map_at_100
+      value: 14.631
+    - type: map_at_1000
+      value: 16.127
+    - type: map_at_3
+      value: 4.673
+    - type: map_at_5
+      value: 5.897
+    - type: mrr_at_1
+      value: 38.775999999999996
+    - type: mrr_at_10
+      value: 49.271
+    - type: mrr_at_100
+      value: 50.181
+    - type: mrr_at_1000
+      value: 50.2
+    - type: mrr_at_3
+      value: 44.558
+    - type: mrr_at_5
+      value: 47.925000000000004
+    - type: ndcg_at_1
+      value: 35.714
+    - type: ndcg_at_10
+      value: 23.44
+    - type: ndcg_at_100
+      value: 35.345
+    - type: ndcg_at_1000
+      value: 46.495
+    - type: ndcg_at_3
+      value: 26.146
+    - type: ndcg_at_5
+      value: 24.878
+    - type: precision_at_1
+      value: 38.775999999999996
+    - type: precision_at_10
+      value: 20.816000000000003
+    - type: precision_at_100
+      value: 7.428999999999999
+    - type: precision_at_1000
+      value: 1.494
+    - type: precision_at_3
+      value: 25.85
+    - type: precision_at_5
+      value: 24.082
+    - type: recall_at_1
+      value: 3.047
+    - type: recall_at_10
+      value: 14.975
+    - type: recall_at_100
+      value: 45.943
+    - type: recall_at_1000
+      value: 80.31099999999999
+    - type: recall_at_3
+      value: 5.478000000000001
+    - type: recall_at_5
+      value: 8.294
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/toxic_conversations_50k
+      name: MTEB ToxicConversationsClassification
+    metrics:
+    - type: accuracy
+      value: 68.84080000000002
+    - type: ap
+      value: 13.135219251019848
+    - type: f1
+      value: 52.849999421995506
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/tweet_sentiment_extraction
+      name: MTEB TweetSentimentExtractionClassification
+    metrics:
+    - type: accuracy
+      value: 56.68647425014149
+    - type: f1
+      value: 56.97981427365949
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/twentynewsgroups-clustering
+      name: MTEB TwentyNewsgroupsClustering
+    metrics:
+    - type: v_measure
+      value: 40.8911707239219
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twittersemeval2015-pairclassification
+      name: MTEB TwitterSemEval2015
+    metrics:
+    - type: cos_sim_accuracy
+      value: 83.04226023722954
+    - type: cos_sim_ap
+      value: 63.681339908301325
+    - type: cos_sim_f1
+      value: 60.349184470480125
+    - type: cos_sim_precision
+      value: 53.437754271765655
+    - type: cos_sim_recall
+      value: 69.31398416886545
+    - type: dot_accuracy
+      value: 81.46271681468677
+    - type: dot_ap
+      value: 57.78072296265885
+    - type: dot_f1
+      value: 56.28769265132901
+    - type: dot_precision
+      value: 48.7993803253292
+    - type: dot_recall
+      value: 66.49076517150397
+    - type: euclidean_accuracy
+      value: 82.16606067830959
+    - type: euclidean_ap
+      value: 59.974530371203514
+    - type: euclidean_f1
+      value: 56.856023506366306
+    - type: euclidean_precision
+      value: 53.037916857012334
+    - type: euclidean_recall
+      value: 61.2664907651715
+    - type: manhattan_accuracy
+      value: 82.16606067830959
+    - type: manhattan_ap
+      value: 59.98962379571767
+    - type: manhattan_f1
+      value: 56.98153158451947
+    - type: manhattan_precision
+      value: 51.41158989598811
+    - type: manhattan_recall
+      value: 63.90501319261214
+    - type: max_accuracy
+      value: 83.04226023722954
+    - type: max_ap
+      value: 63.681339908301325
+    - type: max_f1
+      value: 60.349184470480125
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twitterurlcorpus-pairclassification
+      name: MTEB TwitterURLCorpus
+    metrics:
+    - type: cos_sim_accuracy
+      value: 88.56871191834517
+    - type: cos_sim_ap
+      value: 84.80240716354544
+    - type: cos_sim_f1
+      value: 77.07765285922385
+    - type: cos_sim_precision
+      value: 74.84947406601378
+    - type: cos_sim_recall
+      value: 79.44256236526024
+    - type: dot_accuracy
+      value: 86.00923662048356
+    - type: dot_ap
+      value: 78.6556459012073
+    - type: dot_f1
+      value: 72.7583749109052
+    - type: dot_precision
+      value: 67.72823779193206
+    - type: dot_recall
+      value: 78.59562673236834
+    - type: euclidean_accuracy
+      value: 87.84103698529127
+    - type: euclidean_ap
+      value: 83.50424424952834
+    - type: euclidean_f1
+      value: 75.74496544549307
+    - type: euclidean_precision
+      value: 73.19402556369381
+    - type: euclidean_recall
+      value: 78.48013550970127
+    - type: manhattan_accuracy
+      value: 87.9225365777933
+    - type: manhattan_ap
+      value: 83.49479248597825
+    - type: manhattan_f1
+      value: 75.67748162447101
+    - type: manhattan_precision
+      value: 73.06810035842294
+    - type: manhattan_recall
+      value: 78.48013550970127
+    - type: max_accuracy
+      value: 88.56871191834517
+    - type: max_ap
+      value: 84.80240716354544
+    - type: max_f1
+      value: 77.07765285922385
 ---
 
 # SGPT-2.7B-weightedmean-msmarco-specb-bitfit