Add MTEB

README.md

@@ -4,6 +4,2397 @@ tags:
 - sentence-transformers
 - feature-extraction
 - sentence-similarity
+model-index:
+- name: SGPT-1.3B-weightedmean-msmarco-specb-bitfit
+  results:
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_counterfactual
+      name: MTEB AmazonCounterfactualClassification (en)
+    metrics:
+    - type: accuracy
+      value: 65.20895522388061
+    - type: ap
+      value: 29.59212705444778
+    - type: f1
+      value: 59.97099864321921
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_polarity
+      name: MTEB AmazonPolarityClassification
+    metrics:
+    - type: accuracy
+      value: 73.20565
+    - type: ap
+      value: 67.36680643550963
+    - type: f1
+      value: 72.90420520325125
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_reviews_multi
+      name: MTEB AmazonReviewsClassification (en)
+    metrics:
+    - type: accuracy
+      value: 34.955999999999996
+    - type: f1
+      value: 34.719324437696955
+  - task:
+      type: Retrieval
+    dataset:
+      type: arguana
+      name: MTEB ArguAna
+    metrics:
+    - type: map_at_1
+      value: 26.101999999999997
+    - type: map_at_10
+      value: 40.958
+    - type: map_at_100
+      value: 42.033
+    - type: map_at_1000
+      value: 42.042
+    - type: map_at_3
+      value: 36.332
+    - type: map_at_5
+      value: 38.608
+    - type: mrr_at_1
+      value: 26.387
+    - type: mrr_at_10
+      value: 41.051
+    - type: mrr_at_100
+      value: 42.118
+    - type: mrr_at_1000
+      value: 42.126999999999995
+    - type: mrr_at_3
+      value: 36.415
+    - type: mrr_at_5
+      value: 38.72
+    - type: ndcg_at_1
+      value: 26.101999999999997
+    - type: ndcg_at_10
+      value: 49.68
+    - type: ndcg_at_100
+      value: 54.257999999999996
+    - type: ndcg_at_1000
+      value: 54.486000000000004
+    - type: ndcg_at_3
+      value: 39.864
+    - type: ndcg_at_5
+      value: 43.980000000000004
+    - type: precision_at_1
+      value: 26.101999999999997
+    - type: precision_at_10
+      value: 7.781000000000001
+    - type: precision_at_100
+      value: 0.979
+    - type: precision_at_1000
+      value: 0.1
+    - type: precision_at_3
+      value: 16.714000000000002
+    - type: precision_at_5
+      value: 12.034
+    - type: recall_at_1
+      value: 26.101999999999997
+    - type: recall_at_10
+      value: 77.809
+    - type: recall_at_100
+      value: 97.866
+    - type: recall_at_1000
+      value: 99.644
+    - type: recall_at_3
+      value: 50.141999999999996
+    - type: recall_at_5
+      value: 60.171
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-p2p
+      name: MTEB ArxivClusteringP2P
+    metrics:
+    - type: v_measure
+      value: 43.384194916953774
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-s2s
+      name: MTEB ArxivClusteringS2S
+    metrics:
+    - type: v_measure
+      value: 33.70962633433912
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/askubuntudupquestions-reranking
+      name: MTEB AskUbuntuDupQuestions
+    metrics:
+    - type: map
+      value: 58.133058996870076
+    - type: mrr
+      value: 72.10922041946972
+  - task:
+      type: STS
+    dataset:
+      type: mteb/biosses-sts
+      name: MTEB BIOSSES
+    metrics:
+    - type: cos_sim_pearson
+      value: 86.62153841660047
+    - type: cos_sim_spearman
+      value: 83.01514456843276
+    - type: euclidean_pearson
+      value: 86.00431518427241
+    - type: euclidean_spearman
+      value: 83.85552516285783
+    - type: manhattan_pearson
+      value: 85.83025803351181
+    - type: manhattan_spearman
+      value: 83.86636878343106
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/banking77
+      name: MTEB Banking77Classification
+    metrics:
+    - type: accuracy
+      value: 82.05844155844156
+    - type: f1
+      value: 82.0185837884764
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-p2p
+      name: MTEB BiorxivClusteringP2P
+    metrics:
+    - type: v_measure
+      value: 35.05918333141837
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-s2s
+      name: MTEB BiorxivClusteringS2S
+    metrics:
+    - type: v_measure
+      value: 30.71055028830579
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackAndroidRetrieval
+    metrics:
+    - type: map_at_1
+      value: 26.519
+    - type: map_at_10
+      value: 35.634
+    - type: map_at_100
+      value: 36.961
+    - type: map_at_1000
+      value: 37.088
+    - type: map_at_3
+      value: 32.254
+    - type: map_at_5
+      value: 34.22
+    - type: mrr_at_1
+      value: 32.332
+    - type: mrr_at_10
+      value: 41.168
+    - type: mrr_at_100
+      value: 41.977
+    - type: mrr_at_1000
+      value: 42.028999999999996
+    - type: mrr_at_3
+      value: 38.196999999999996
+    - type: mrr_at_5
+      value: 40.036
+    - type: ndcg_at_1
+      value: 32.332
+    - type: ndcg_at_10
+      value: 41.471000000000004
+    - type: ndcg_at_100
+      value: 46.955999999999996
+    - type: ndcg_at_1000
+      value: 49.262
+    - type: ndcg_at_3
+      value: 35.937999999999995
+    - type: ndcg_at_5
+      value: 38.702999999999996
+    - type: precision_at_1
+      value: 32.332
+    - type: precision_at_10
+      value: 7.7829999999999995
+    - type: precision_at_100
+      value: 1.29
+    - type: precision_at_1000
+      value: 0.178
+    - type: precision_at_3
+      value: 16.834
+    - type: precision_at_5
+      value: 12.418
+    - type: recall_at_1
+      value: 26.519
+    - type: recall_at_10
+      value: 53.190000000000005
+    - type: recall_at_100
+      value: 76.56500000000001
+    - type: recall_at_1000
+      value: 91.47800000000001
+    - type: recall_at_3
+      value: 38.034
+    - type: recall_at_5
+      value: 45.245999999999995
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackEnglishRetrieval
+    metrics:
+    - type: map_at_1
+      value: 25.356
+    - type: map_at_10
+      value: 34.596
+    - type: map_at_100
+      value: 35.714
+    - type: map_at_1000
+      value: 35.839999999999996
+    - type: map_at_3
+      value: 32.073
+    - type: map_at_5
+      value: 33.475
+    - type: mrr_at_1
+      value: 31.274
+    - type: mrr_at_10
+      value: 39.592
+    - type: mrr_at_100
+      value: 40.284
+    - type: mrr_at_1000
+      value: 40.339999999999996
+    - type: mrr_at_3
+      value: 37.378
+    - type: mrr_at_5
+      value: 38.658
+    - type: ndcg_at_1
+      value: 31.274
+    - type: ndcg_at_10
+      value: 39.766
+    - type: ndcg_at_100
+      value: 44.028
+    - type: ndcg_at_1000
+      value: 46.445
+    - type: ndcg_at_3
+      value: 35.934
+    - type: ndcg_at_5
+      value: 37.751000000000005
+    - type: precision_at_1
+      value: 31.274
+    - type: precision_at_10
+      value: 7.452
+    - type: precision_at_100
+      value: 1.217
+    - type: precision_at_1000
+      value: 0.16999999999999998
+    - type: precision_at_3
+      value: 17.431
+    - type: precision_at_5
+      value: 12.306000000000001
+    - type: recall_at_1
+      value: 25.356
+    - type: recall_at_10
+      value: 49.344
+    - type: recall_at_100
+      value: 67.497
+    - type: recall_at_1000
+      value: 83.372
+    - type: recall_at_3
+      value: 38.227
+    - type: recall_at_5
+      value: 43.187999999999995
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGamingRetrieval
+    metrics:
+    - type: map_at_1
+      value: 32.759
+    - type: map_at_10
+      value: 43.937
+    - type: map_at_100
+      value: 45.004
+    - type: map_at_1000
+      value: 45.07
+    - type: map_at_3
+      value: 40.805
+    - type: map_at_5
+      value: 42.497
+    - type: mrr_at_1
+      value: 37.367
+    - type: mrr_at_10
+      value: 47.237
+    - type: mrr_at_100
+      value: 47.973
+    - type: mrr_at_1000
+      value: 48.010999999999996
+    - type: mrr_at_3
+      value: 44.65
+    - type: mrr_at_5
+      value: 46.050999999999995
+    - type: ndcg_at_1
+      value: 37.367
+    - type: ndcg_at_10
+      value: 49.659
+    - type: ndcg_at_100
+      value: 54.069
+    - type: ndcg_at_1000
+      value: 55.552
+    - type: ndcg_at_3
+      value: 44.169000000000004
+    - type: ndcg_at_5
+      value: 46.726
+    - type: precision_at_1
+      value: 37.367
+    - type: precision_at_10
+      value: 8.163
+    - type: precision_at_100
+      value: 1.133
+    - type: precision_at_1000
+      value: 0.131
+    - type: precision_at_3
+      value: 19.707
+    - type: precision_at_5
+      value: 13.718
+    - type: recall_at_1
+      value: 32.759
+    - type: recall_at_10
+      value: 63.341
+    - type: recall_at_100
+      value: 82.502
+    - type: recall_at_1000
+      value: 93.259
+    - type: recall_at_3
+      value: 48.796
+    - type: recall_at_5
+      value: 54.921
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGisRetrieval
+    metrics:
+    - type: map_at_1
+      value: 18.962
+    - type: map_at_10
+      value: 25.863000000000003
+    - type: map_at_100
+      value: 26.817999999999998
+    - type: map_at_1000
+      value: 26.918
+    - type: map_at_3
+      value: 23.043
+    - type: map_at_5
+      value: 24.599
+    - type: mrr_at_1
+      value: 20.452
+    - type: mrr_at_10
+      value: 27.301
+    - type: mrr_at_100
+      value: 28.233000000000004
+    - type: mrr_at_1000
+      value: 28.310000000000002
+    - type: mrr_at_3
+      value: 24.539
+    - type: mrr_at_5
+      value: 26.108999999999998
+    - type: ndcg_at_1
+      value: 20.452
+    - type: ndcg_at_10
+      value: 30.354999999999997
+    - type: ndcg_at_100
+      value: 35.336
+    - type: ndcg_at_1000
+      value: 37.927
+    - type: ndcg_at_3
+      value: 24.705
+    - type: ndcg_at_5
+      value: 27.42
+    - type: precision_at_1
+      value: 20.452
+    - type: precision_at_10
+      value: 4.949
+    - type: precision_at_100
+      value: 0.7799999999999999
+    - type: precision_at_1000
+      value: 0.104
+    - type: precision_at_3
+      value: 10.358
+    - type: precision_at_5
+      value: 7.774
+    - type: recall_at_1
+      value: 18.962
+    - type: recall_at_10
+      value: 43.056
+    - type: recall_at_100
+      value: 66.27300000000001
+    - type: recall_at_1000
+      value: 85.96000000000001
+    - type: recall_at_3
+      value: 27.776
+    - type: recall_at_5
+      value: 34.287
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackMathematicaRetrieval
+    metrics:
+    - type: map_at_1
+      value: 11.24
+    - type: map_at_10
+      value: 18.503
+    - type: map_at_100
+      value: 19.553
+    - type: map_at_1000
+      value: 19.689999999999998
+    - type: map_at_3
+      value: 16.150000000000002
+    - type: map_at_5
+      value: 17.254
+    - type: mrr_at_1
+      value: 13.806
+    - type: mrr_at_10
+      value: 21.939
+    - type: mrr_at_100
+      value: 22.827
+    - type: mrr_at_1000
+      value: 22.911
+    - type: mrr_at_3
+      value: 19.32
+    - type: mrr_at_5
+      value: 20.558
+    - type: ndcg_at_1
+      value: 13.806
+    - type: ndcg_at_10
+      value: 23.383000000000003
+    - type: ndcg_at_100
+      value: 28.834
+    - type: ndcg_at_1000
+      value: 32.175
+    - type: ndcg_at_3
+      value: 18.651999999999997
+    - type: ndcg_at_5
+      value: 20.505000000000003
+    - type: precision_at_1
+      value: 13.806
+    - type: precision_at_10
+      value: 4.714
+    - type: precision_at_100
+      value: 0.864
+    - type: precision_at_1000
+      value: 0.13
+    - type: precision_at_3
+      value: 9.328
+    - type: precision_at_5
+      value: 6.841
+    - type: recall_at_1
+      value: 11.24
+    - type: recall_at_10
+      value: 34.854
+    - type: recall_at_100
+      value: 59.50299999999999
+    - type: recall_at_1000
+      value: 83.25
+    - type: recall_at_3
+      value: 22.02
+    - type: recall_at_5
+      value: 26.715
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackPhysicsRetrieval
+    metrics:
+    - type: map_at_1
+      value: 23.012
+    - type: map_at_10
+      value: 33.048
+    - type: map_at_100
+      value: 34.371
+    - type: map_at_1000
+      value: 34.489
+    - type: map_at_3
+      value: 29.942999999999998
+    - type: map_at_5
+      value: 31.602000000000004
+    - type: mrr_at_1
+      value: 28.104000000000003
+    - type: mrr_at_10
+      value: 37.99
+    - type: mrr_at_100
+      value: 38.836
+    - type: mrr_at_1000
+      value: 38.891
+    - type: mrr_at_3
+      value: 35.226
+    - type: mrr_at_5
+      value: 36.693999999999996
+    - type: ndcg_at_1
+      value: 28.104000000000003
+    - type: ndcg_at_10
+      value: 39.037
+    - type: ndcg_at_100
+      value: 44.643
+    - type: ndcg_at_1000
+      value: 46.939
+    - type: ndcg_at_3
+      value: 33.784
+    - type: ndcg_at_5
+      value: 36.126000000000005
+    - type: precision_at_1
+      value: 28.104000000000003
+    - type: precision_at_10
+      value: 7.2669999999999995
+    - type: precision_at_100
+      value: 1.193
+    - type: precision_at_1000
+      value: 0.159
+    - type: precision_at_3
+      value: 16.298000000000002
+    - type: precision_at_5
+      value: 11.684
+    - type: recall_at_1
+      value: 23.012
+    - type: recall_at_10
+      value: 52.054
+    - type: recall_at_100
+      value: 75.622
+    - type: recall_at_1000
+      value: 90.675
+    - type: recall_at_3
+      value: 37.282
+    - type: recall_at_5
+      value: 43.307
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackProgrammersRetrieval
+    metrics:
+    - type: map_at_1
+      value: 21.624
+    - type: map_at_10
+      value: 30.209999999999997
+    - type: map_at_100
+      value: 31.52
+    - type: map_at_1000
+      value: 31.625999999999998
+    - type: map_at_3
+      value: 26.951000000000004
+    - type: map_at_5
+      value: 28.938999999999997
+    - type: mrr_at_1
+      value: 26.941
+    - type: mrr_at_10
+      value: 35.13
+    - type: mrr_at_100
+      value: 36.15
+    - type: mrr_at_1000
+      value: 36.204
+    - type: mrr_at_3
+      value: 32.42
+    - type: mrr_at_5
+      value: 34.155
+    - type: ndcg_at_1
+      value: 26.941
+    - type: ndcg_at_10
+      value: 35.726
+    - type: ndcg_at_100
+      value: 41.725
+    - type: ndcg_at_1000
+      value: 44.105
+    - type: ndcg_at_3
+      value: 30.184
+    - type: ndcg_at_5
+      value: 33.176
+    - type: precision_at_1
+      value: 26.941
+    - type: precision_at_10
+      value: 6.654999999999999
+    - type: precision_at_100
+      value: 1.1520000000000001
+    - type: precision_at_1000
+      value: 0.152
+    - type: precision_at_3
+      value: 14.346
+    - type: precision_at_5
+      value: 10.868
+    - type: recall_at_1
+      value: 21.624
+    - type: recall_at_10
+      value: 47.359
+    - type: recall_at_100
+      value: 73.436
+    - type: recall_at_1000
+      value: 89.988
+    - type: recall_at_3
+      value: 32.34
+    - type: recall_at_5
+      value: 39.856
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackRetrieval
+    metrics:
+    - type: map_at_1
+      value: 20.67566666666667
+    - type: map_at_10
+      value: 28.479333333333333
+    - type: map_at_100
+      value: 29.612249999999996
+    - type: map_at_1000
+      value: 29.731166666666663
+    - type: map_at_3
+      value: 25.884
+    - type: map_at_5
+      value: 27.298916666666667
+    - type: mrr_at_1
+      value: 24.402583333333332
+    - type: mrr_at_10
+      value: 32.07041666666667
+    - type: mrr_at_100
+      value: 32.95841666666667
+    - type: mrr_at_1000
+      value: 33.025416666666665
+    - type: mrr_at_3
+      value: 29.677749999999996
+    - type: mrr_at_5
+      value: 31.02391666666667
+    - type: ndcg_at_1
+      value: 24.402583333333332
+    - type: ndcg_at_10
+      value: 33.326166666666666
+    - type: ndcg_at_100
+      value: 38.51566666666667
+    - type: ndcg_at_1000
+      value: 41.13791666666667
+    - type: ndcg_at_3
+      value: 28.687749999999994
+    - type: ndcg_at_5
+      value: 30.84766666666667
+    - type: precision_at_1
+      value: 24.402583333333332
+    - type: precision_at_10
+      value: 5.943749999999999
+    - type: precision_at_100
+      value: 1.0098333333333334
+    - type: precision_at_1000
+      value: 0.14183333333333334
+    - type: precision_at_3
+      value: 13.211500000000001
+    - type: precision_at_5
+      value: 9.548416666666668
+    - type: recall_at_1
+      value: 20.67566666666667
+    - type: recall_at_10
+      value: 44.245583333333336
+    - type: recall_at_100
+      value: 67.31116666666667
+    - type: recall_at_1000
+      value: 85.87841666666665
+    - type: recall_at_3
+      value: 31.49258333333333
+    - type: recall_at_5
+      value: 36.93241666666667
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackStatsRetrieval
+    metrics:
+    - type: map_at_1
+      value: 18.34
+    - type: map_at_10
+      value: 23.988
+    - type: map_at_100
+      value: 24.895
+    - type: map_at_1000
+      value: 24.992
+    - type: map_at_3
+      value: 21.831
+    - type: map_at_5
+      value: 23.0
+    - type: mrr_at_1
+      value: 20.399
+    - type: mrr_at_10
+      value: 26.186
+    - type: mrr_at_100
+      value: 27.017999999999997
+    - type: mrr_at_1000
+      value: 27.090999999999998
+    - type: mrr_at_3
+      value: 24.08
+    - type: mrr_at_5
+      value: 25.230000000000004
+    - type: ndcg_at_1
+      value: 20.399
+    - type: ndcg_at_10
+      value: 27.799000000000003
+    - type: ndcg_at_100
+      value: 32.579
+    - type: ndcg_at_1000
+      value: 35.209
+    - type: ndcg_at_3
+      value: 23.684
+    - type: ndcg_at_5
+      value: 25.521
+    - type: precision_at_1
+      value: 20.399
+    - type: precision_at_10
+      value: 4.585999999999999
+    - type: precision_at_100
+      value: 0.755
+    - type: precision_at_1000
+      value: 0.105
+    - type: precision_at_3
+      value: 10.276
+    - type: precision_at_5
+      value: 7.362
+    - type: recall_at_1
+      value: 18.34
+    - type: recall_at_10
+      value: 37.456
+    - type: recall_at_100
+      value: 59.86
+    - type: recall_at_1000
+      value: 79.703
+    - type: recall_at_3
+      value: 26.163999999999998
+    - type: recall_at_5
+      value: 30.652
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackTexRetrieval
+    metrics:
+    - type: map_at_1
+      value: 12.327
+    - type: map_at_10
+      value: 17.572
+    - type: map_at_100
+      value: 18.534
+    - type: map_at_1000
+      value: 18.653
+    - type: map_at_3
+      value: 15.703
+    - type: map_at_5
+      value: 16.752
+    - type: mrr_at_1
+      value: 15.038000000000002
+    - type: mrr_at_10
+      value: 20.726
+    - type: mrr_at_100
+      value: 21.61
+    - type: mrr_at_1000
+      value: 21.695
+    - type: mrr_at_3
+      value: 18.829
+    - type: mrr_at_5
+      value: 19.885
+    - type: ndcg_at_1
+      value: 15.038000000000002
+    - type: ndcg_at_10
+      value: 21.241
+    - type: ndcg_at_100
+      value: 26.179000000000002
+    - type: ndcg_at_1000
+      value: 29.316
+    - type: ndcg_at_3
+      value: 17.762
+    - type: ndcg_at_5
+      value: 19.413
+    - type: precision_at_1
+      value: 15.038000000000002
+    - type: precision_at_10
+      value: 3.8920000000000003
+    - type: precision_at_100
+      value: 0.75
+    - type: precision_at_1000
+      value: 0.11800000000000001
+    - type: precision_at_3
+      value: 8.351
+    - type: precision_at_5
+      value: 6.187
+    - type: recall_at_1
+      value: 12.327
+    - type: recall_at_10
+      value: 29.342000000000002
+    - type: recall_at_100
+      value: 51.854
+    - type: recall_at_1000
+      value: 74.648
+    - type: recall_at_3
+      value: 19.596
+    - type: recall_at_5
+      value: 23.899
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackUnixRetrieval
+    metrics:
+    - type: map_at_1
+      value: 20.594
+    - type: map_at_10
+      value: 27.878999999999998
+    - type: map_at_100
+      value: 28.926000000000002
+    - type: map_at_1000
+      value: 29.041
+    - type: map_at_3
+      value: 25.668999999999997
+    - type: map_at_5
+      value: 26.773999999999997
+    - type: mrr_at_1
+      value: 23.694000000000003
+    - type: mrr_at_10
+      value: 31.335
+    - type: mrr_at_100
+      value: 32.218
+    - type: mrr_at_1000
+      value: 32.298
+    - type: mrr_at_3
+      value: 29.26
+    - type: mrr_at_5
+      value: 30.328
+    - type: ndcg_at_1
+      value: 23.694000000000003
+    - type: ndcg_at_10
+      value: 32.456
+    - type: ndcg_at_100
+      value: 37.667
+    - type: ndcg_at_1000
+      value: 40.571
+    - type: ndcg_at_3
+      value: 28.283
+    - type: ndcg_at_5
+      value: 29.986
+    - type: precision_at_1
+      value: 23.694000000000003
+    - type: precision_at_10
+      value: 5.448
+    - type: precision_at_100
+      value: 0.9119999999999999
+    - type: precision_at_1000
+      value: 0.127
+    - type: precision_at_3
+      value: 12.717999999999998
+    - type: precision_at_5
+      value: 8.843
+    - type: recall_at_1
+      value: 20.594
+    - type: recall_at_10
+      value: 43.004999999999995
+    - type: recall_at_100
+      value: 66.228
+    - type: recall_at_1000
+      value: 87.17099999999999
+    - type: recall_at_3
+      value: 31.554
+    - type: recall_at_5
+      value: 35.838
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWebmastersRetrieval
+    metrics:
+    - type: map_at_1
+      value: 20.855999999999998
+    - type: map_at_10
+      value: 28.372000000000003
+    - type: map_at_100
+      value: 29.87
+    - type: map_at_1000
+      value: 30.075000000000003
+    - type: map_at_3
+      value: 26.054
+    - type: map_at_5
+      value: 27.128999999999998
+    - type: mrr_at_1
+      value: 25.494
+    - type: mrr_at_10
+      value: 32.735
+    - type: mrr_at_100
+      value: 33.794000000000004
+    - type: mrr_at_1000
+      value: 33.85
+    - type: mrr_at_3
+      value: 30.731
+    - type: mrr_at_5
+      value: 31.897
+    - type: ndcg_at_1
+      value: 25.494
+    - type: ndcg_at_10
+      value: 33.385
+    - type: ndcg_at_100
+      value: 39.436
+    - type: ndcg_at_1000
+      value: 42.313
+    - type: ndcg_at_3
+      value: 29.612
+    - type: ndcg_at_5
+      value: 31.186999999999998
+    - type: precision_at_1
+      value: 25.494
+    - type: precision_at_10
+      value: 6.422999999999999
+    - type: precision_at_100
+      value: 1.383
+    - type: precision_at_1000
+      value: 0.22399999999999998
+    - type: precision_at_3
+      value: 13.834
+    - type: precision_at_5
+      value: 10.0
+    - type: recall_at_1
+      value: 20.855999999999998
+    - type: recall_at_10
+      value: 42.678
+    - type: recall_at_100
+      value: 70.224
+    - type: recall_at_1000
+      value: 89.369
+    - type: recall_at_3
+      value: 31.957
+    - type: recall_at_5
+      value: 36.026
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWordpressRetrieval
+    metrics:
+    - type: map_at_1
+      value: 16.519000000000002
+    - type: map_at_10
+      value: 22.15
+    - type: map_at_100
+      value: 23.180999999999997
+    - type: map_at_1000
+      value: 23.291999999999998
+    - type: map_at_3
+      value: 20.132
+    - type: map_at_5
+      value: 21.346
+    - type: mrr_at_1
+      value: 17.93
+    - type: mrr_at_10
+      value: 23.506
+    - type: mrr_at_100
+      value: 24.581
+    - type: mrr_at_1000
+      value: 24.675
+    - type: mrr_at_3
+      value: 21.503
+    - type: mrr_at_5
+      value: 22.686
+    - type: ndcg_at_1
+      value: 17.93
+    - type: ndcg_at_10
+      value: 25.636
+    - type: ndcg_at_100
+      value: 30.736
+    - type: ndcg_at_1000
+      value: 33.841
+    - type: ndcg_at_3
+      value: 21.546000000000003
+    - type: ndcg_at_5
+      value: 23.658
+    - type: precision_at_1
+      value: 17.93
+    - type: precision_at_10
+      value: 3.993
+    - type: precision_at_100
+      value: 0.6890000000000001
+    - type: precision_at_1000
+      value: 0.104
+    - type: precision_at_3
+      value: 9.057
+    - type: precision_at_5
+      value: 6.58
+    - type: recall_at_1
+      value: 16.519000000000002
+    - type: recall_at_10
+      value: 35.268
+    - type: recall_at_100
+      value: 58.17
+    - type: recall_at_1000
+      value: 81.66799999999999
+    - type: recall_at_3
+      value: 24.165
+    - type: recall_at_5
+      value: 29.254
+  - task:
+      type: Retrieval
+    dataset:
+      type: climate-fever
+      name: MTEB ClimateFEVER
+    metrics:
+    - type: map_at_1
+      value: 10.363
+    - type: map_at_10
+      value: 18.301000000000002
+    - type: map_at_100
+      value: 20.019000000000002
+    - type: map_at_1000
+      value: 20.207
+    - type: map_at_3
+      value: 14.877
+    - type: map_at_5
+      value: 16.544
+    - type: mrr_at_1
+      value: 22.866
+    - type: mrr_at_10
+      value: 34.935
+    - type: mrr_at_100
+      value: 35.802
+    - type: mrr_at_1000
+      value: 35.839999999999996
+    - type: mrr_at_3
+      value: 30.965999999999998
+    - type: mrr_at_5
+      value: 33.204
+    - type: ndcg_at_1
+      value: 22.866
+    - type: ndcg_at_10
+      value: 26.595000000000002
+    - type: ndcg_at_100
+      value: 33.513999999999996
+    - type: ndcg_at_1000
+      value: 36.872
+    - type: ndcg_at_3
+      value: 20.666999999999998
+    - type: ndcg_at_5
+      value: 22.728
+    - type: precision_at_1
+      value: 22.866
+    - type: precision_at_10
+      value: 8.632
+    - type: precision_at_100
+      value: 1.6119999999999999
+    - type: precision_at_1000
+      value: 0.22399999999999998
+    - type: precision_at_3
+      value: 15.504999999999999
+    - type: precision_at_5
+      value: 12.404
+    - type: recall_at_1
+      value: 10.363
+    - type: recall_at_10
+      value: 33.494
+    - type: recall_at_100
+      value: 57.593
+    - type: recall_at_1000
+      value: 76.342
+    - type: recall_at_3
+      value: 19.157
+    - type: recall_at_5
+      value: 24.637999999999998
+  - task:
+      type: Retrieval
+    dataset:
+      type: dbpedia-entity
+      name: MTEB DBPedia
+    metrics:
+    - type: map_at_1
+      value: 7.436
+    - type: map_at_10
+      value: 14.760000000000002
+    - type: map_at_100
+      value: 19.206
+    - type: map_at_1000
+      value: 20.267
+    - type: map_at_3
+      value: 10.894
+    - type: map_at_5
+      value: 12.828999999999999
+    - type: mrr_at_1
+      value: 54.25
+    - type: mrr_at_10
+      value: 63.769
+    - type: mrr_at_100
+      value: 64.193
+    - type: mrr_at_1000
+      value: 64.211
+    - type: mrr_at_3
+      value: 61.458
+    - type: mrr_at_5
+      value: 63.096
+    - type: ndcg_at_1
+      value: 42.875
+    - type: ndcg_at_10
+      value: 31.507
+    - type: ndcg_at_100
+      value: 34.559
+    - type: ndcg_at_1000
+      value: 41.246
+    - type: ndcg_at_3
+      value: 35.058
+    - type: ndcg_at_5
+      value: 33.396
+    - type: precision_at_1
+      value: 54.25
+    - type: precision_at_10
+      value: 24.45
+    - type: precision_at_100
+      value: 7.383000000000001
+    - type: precision_at_1000
+      value: 1.582
+    - type: precision_at_3
+      value: 38.083
+    - type: precision_at_5
+      value: 32.6
+    - type: recall_at_1
+      value: 7.436
+    - type: recall_at_10
+      value: 19.862
+    - type: recall_at_100
+      value: 38.981
+    - type: recall_at_1000
+      value: 61.038000000000004
+    - type: recall_at_3
+      value: 11.949
+    - type: recall_at_5
+      value: 15.562000000000001
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/emotion
+      name: MTEB EmotionClassification
+    metrics:
+    - type: accuracy
+      value: 46.39
+    - type: f1
+      value: 42.26424885856703
+  - task:
+      type: Retrieval
+    dataset:
+      type: fever
+      name: MTEB FEVER
+    metrics:
+    - type: map_at_1
+      value: 50.916
+    - type: map_at_10
+      value: 62.258
+    - type: map_at_100
+      value: 62.741
+    - type: map_at_1000
+      value: 62.763000000000005
+    - type: map_at_3
+      value: 60.01800000000001
+    - type: map_at_5
+      value: 61.419999999999995
+    - type: mrr_at_1
+      value: 54.964999999999996
+    - type: mrr_at_10
+      value: 66.554
+    - type: mrr_at_100
+      value: 66.96600000000001
+    - type: mrr_at_1000
+      value: 66.97800000000001
+    - type: mrr_at_3
+      value: 64.414
+    - type: mrr_at_5
+      value: 65.77
+    - type: ndcg_at_1
+      value: 54.964999999999996
+    - type: ndcg_at_10
+      value: 68.12
+    - type: ndcg_at_100
+      value: 70.282
+    - type: ndcg_at_1000
+      value: 70.788
+    - type: ndcg_at_3
+      value: 63.861999999999995
+    - type: ndcg_at_5
+      value: 66.216
+    - type: precision_at_1
+      value: 54.964999999999996
+    - type: precision_at_10
+      value: 8.998000000000001
+    - type: precision_at_100
+      value: 1.016
+    - type: precision_at_1000
+      value: 0.107
+    - type: precision_at_3
+      value: 25.618000000000002
+    - type: precision_at_5
+      value: 16.676
+    - type: recall_at_1
+      value: 50.916
+    - type: recall_at_10
+      value: 82.04
+    - type: recall_at_100
+      value: 91.689
+    - type: recall_at_1000
+      value: 95.34899999999999
+    - type: recall_at_3
+      value: 70.512
+    - type: recall_at_5
+      value: 76.29899999999999
+  - task:
+      type: Retrieval
+    dataset:
+      type: fiqa
+      name: MTEB FiQA2018
+    metrics:
+    - type: map_at_1
+      value: 13.568
+    - type: map_at_10
+      value: 23.264000000000003
+    - type: map_at_100
+      value: 24.823999999999998
+    - type: map_at_1000
+      value: 25.013999999999996
+    - type: map_at_3
+      value: 19.724
+    - type: map_at_5
+      value: 21.772
+    - type: mrr_at_1
+      value: 27.315
+    - type: mrr_at_10
+      value: 35.935
+    - type: mrr_at_100
+      value: 36.929
+    - type: mrr_at_1000
+      value: 36.985
+    - type: mrr_at_3
+      value: 33.591
+    - type: mrr_at_5
+      value: 34.848
+    - type: ndcg_at_1
+      value: 27.315
+    - type: ndcg_at_10
+      value: 29.988
+    - type: ndcg_at_100
+      value: 36.41
+    - type: ndcg_at_1000
+      value: 40.184999999999995
+    - type: ndcg_at_3
+      value: 26.342
+    - type: ndcg_at_5
+      value: 27.68
+    - type: precision_at_1
+      value: 27.315
+    - type: precision_at_10
+      value: 8.565000000000001
+    - type: precision_at_100
+      value: 1.508
+    - type: precision_at_1000
+      value: 0.219
+    - type: precision_at_3
+      value: 17.849999999999998
+    - type: precision_at_5
+      value: 13.672999999999998
+    - type: recall_at_1
+      value: 13.568
+    - type: recall_at_10
+      value: 37.133
+    - type: recall_at_100
+      value: 61.475
+    - type: recall_at_1000
+      value: 84.372
+    - type: recall_at_3
+      value: 24.112000000000002
+    - type: recall_at_5
+      value: 29.507
+  - task:
+      type: Retrieval
+    dataset:
+      type: hotpotqa
+      name: MTEB HotpotQA
+    metrics:
+    - type: map_at_1
+      value: 30.878
+    - type: map_at_10
+      value: 40.868
+    - type: map_at_100
+      value: 41.693999999999996
+    - type: map_at_1000
+      value: 41.775
+    - type: map_at_3
+      value: 38.56
+    - type: map_at_5
+      value: 39.947
+    - type: mrr_at_1
+      value: 61.756
+    - type: mrr_at_10
+      value: 68.265
+    - type: mrr_at_100
+      value: 68.671
+    - type: mrr_at_1000
+      value: 68.694
+    - type: mrr_at_3
+      value: 66.78399999999999
+    - type: mrr_at_5
+      value: 67.704
+    - type: ndcg_at_1
+      value: 61.756
+    - type: ndcg_at_10
+      value: 49.931
+    - type: ndcg_at_100
+      value: 53.179
+    - type: ndcg_at_1000
+      value: 54.94799999999999
+    - type: ndcg_at_3
+      value: 46.103
+    - type: ndcg_at_5
+      value: 48.147
+    - type: precision_at_1
+      value: 61.756
+    - type: precision_at_10
+      value: 10.163
+    - type: precision_at_100
+      value: 1.2710000000000001
+    - type: precision_at_1000
+      value: 0.151
+    - type: precision_at_3
+      value: 28.179
+    - type: precision_at_5
+      value: 18.528
+    - type: recall_at_1
+      value: 30.878
+    - type: recall_at_10
+      value: 50.817
+    - type: recall_at_100
+      value: 63.544999999999995
+    - type: recall_at_1000
+      value: 75.361
+    - type: recall_at_3
+      value: 42.269
+    - type: recall_at_5
+      value: 46.32
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/imdb
+      name: MTEB ImdbClassification
+    metrics:
+    - type: accuracy
+      value: 64.04799999999999
+    - type: ap
+      value: 59.185251455339284
+    - type: f1
+      value: 63.947123181349255
+  - task:
+      type: Retrieval
+    dataset:
+      type: msmarco
+      name: MTEB MSMARCO
+    metrics:
+    - type: map_at_1
+      value: 18.9
+    - type: map_at_10
+      value: 29.748
+    - type: map_at_100
+      value: 30.976
+    - type: map_at_1000
+      value: 31.041
+    - type: map_at_3
+      value: 26.112999999999996
+    - type: map_at_5
+      value: 28.197
+    - type: mrr_at_1
+      value: 19.413
+    - type: mrr_at_10
+      value: 30.322
+    - type: mrr_at_100
+      value: 31.497000000000003
+    - type: mrr_at_1000
+      value: 31.555
+    - type: mrr_at_3
+      value: 26.729000000000003
+    - type: mrr_at_5
+      value: 28.788999999999998
+    - type: ndcg_at_1
+      value: 19.413
+    - type: ndcg_at_10
+      value: 36.048
+    - type: ndcg_at_100
+      value: 42.152
+    - type: ndcg_at_1000
+      value: 43.772
+    - type: ndcg_at_3
+      value: 28.642
+    - type: ndcg_at_5
+      value: 32.358
+    - type: precision_at_1
+      value: 19.413
+    - type: precision_at_10
+      value: 5.785
+    - type: precision_at_100
+      value: 0.8869999999999999
+    - type: precision_at_1000
+      value: 0.10300000000000001
+    - type: precision_at_3
+      value: 12.192
+    - type: precision_at_5
+      value: 9.189
+    - type: recall_at_1
+      value: 18.9
+    - type: recall_at_10
+      value: 55.457
+    - type: recall_at_100
+      value: 84.09100000000001
+    - type: recall_at_1000
+      value: 96.482
+    - type: recall_at_3
+      value: 35.359
+    - type: recall_at_5
+      value: 44.275
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_domain
+      name: MTEB MTOPDomainClassification (en)
+    metrics:
+    - type: accuracy
+      value: 92.07706338349293
+    - type: f1
+      value: 91.56680443236652
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_intent
+      name: MTEB MTOPIntentClassification (en)
+    metrics:
+    - type: accuracy
+      value: 71.18559051527589
+    - type: f1
+      value: 52.42887061726789
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (en)
+    metrics:
+    - type: accuracy
+      value: 68.64828513786148
+    - type: f1
+      value: 66.54281381596097
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (en)
+    metrics:
+    - type: accuracy
+      value: 76.04236718224612
+    - type: f1
+      value: 75.89170458655639
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-p2p
+      name: MTEB MedrxivClusteringP2P
+    metrics:
+    - type: v_measure
+      value: 32.0840369055247
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-s2s
+      name: MTEB MedrxivClusteringS2S
+    metrics:
+    - type: v_measure
+      value: 29.448729560244537
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/mind_small
+      name: MTEB MindSmallReranking
+    metrics:
+    - type: map
+      value: 31.340856463122375
+    - type: mrr
+      value: 32.398547669840916
+  - task:
+      type: Retrieval
+    dataset:
+      type: nfcorpus
+      name: MTEB NFCorpus
+    metrics:
+    - type: map_at_1
+      value: 5.526
+    - type: map_at_10
+      value: 11.745
+    - type: map_at_100
+      value: 14.831
+    - type: map_at_1000
+      value: 16.235
+    - type: map_at_3
+      value: 8.716
+    - type: map_at_5
+      value: 10.101
+    - type: mrr_at_1
+      value: 43.653
+    - type: mrr_at_10
+      value: 51.06699999999999
+    - type: mrr_at_100
+      value: 51.881
+    - type: mrr_at_1000
+      value: 51.912000000000006
+    - type: mrr_at_3
+      value: 49.02
+    - type: mrr_at_5
+      value: 50.288999999999994
+    - type: ndcg_at_1
+      value: 41.949999999999996
+    - type: ndcg_at_10
+      value: 32.083
+    - type: ndcg_at_100
+      value: 30.049999999999997
+    - type: ndcg_at_1000
+      value: 38.661
+    - type: ndcg_at_3
+      value: 37.940000000000005
+    - type: ndcg_at_5
+      value: 35.455999999999996
+    - type: precision_at_1
+      value: 43.344
+    - type: precision_at_10
+      value: 23.437
+    - type: precision_at_100
+      value: 7.829999999999999
+    - type: precision_at_1000
+      value: 2.053
+    - type: precision_at_3
+      value: 35.501
+    - type: precision_at_5
+      value: 30.464000000000002
+    - type: recall_at_1
+      value: 5.526
+    - type: recall_at_10
+      value: 15.445999999999998
+    - type: recall_at_100
+      value: 31.179000000000002
+    - type: recall_at_1000
+      value: 61.578
+    - type: recall_at_3
+      value: 9.71
+    - type: recall_at_5
+      value: 12.026
+  - task:
+      type: Retrieval
+    dataset:
+      type: nq
+      name: MTEB NQ
+    metrics:
+    - type: map_at_1
+      value: 23.467
+    - type: map_at_10
+      value: 36.041000000000004
+    - type: map_at_100
+      value: 37.268
+    - type: map_at_1000
+      value: 37.322
+    - type: map_at_3
+      value: 32.09
+    - type: map_at_5
+      value: 34.414
+    - type: mrr_at_1
+      value: 26.738
+    - type: mrr_at_10
+      value: 38.665
+    - type: mrr_at_100
+      value: 39.64
+    - type: mrr_at_1000
+      value: 39.681
+    - type: mrr_at_3
+      value: 35.207
+    - type: mrr_at_5
+      value: 37.31
+    - type: ndcg_at_1
+      value: 26.709
+    - type: ndcg_at_10
+      value: 42.942
+    - type: ndcg_at_100
+      value: 48.296
+    - type: ndcg_at_1000
+      value: 49.651
+    - type: ndcg_at_3
+      value: 35.413
+    - type: ndcg_at_5
+      value: 39.367999999999995
+    - type: precision_at_1
+      value: 26.709
+    - type: precision_at_10
+      value: 7.306
+    - type: precision_at_100
+      value: 1.0290000000000001
+    - type: precision_at_1000
+      value: 0.116
+    - type: precision_at_3
+      value: 16.348
+    - type: precision_at_5
+      value: 12.068
+    - type: recall_at_1
+      value: 23.467
+    - type: recall_at_10
+      value: 61.492999999999995
+    - type: recall_at_100
+      value: 85.01100000000001
+    - type: recall_at_1000
+      value: 95.261
+    - type: recall_at_3
+      value: 41.952
+    - type: recall_at_5
+      value: 51.105999999999995
+  - task:
+      type: Retrieval
+    dataset:
+      type: quora
+      name: MTEB QuoraRetrieval
+    metrics:
+    - type: map_at_1
+      value: 67.51700000000001
+    - type: map_at_10
+      value: 81.054
+    - type: map_at_100
+      value: 81.727
+    - type: map_at_1000
+      value: 81.75200000000001
+    - type: map_at_3
+      value: 78.018
+    - type: map_at_5
+      value: 79.879
+    - type: mrr_at_1
+      value: 77.52
+    - type: mrr_at_10
+      value: 84.429
+    - type: mrr_at_100
+      value: 84.58200000000001
+    - type: mrr_at_1000
+      value: 84.584
+    - type: mrr_at_3
+      value: 83.268
+    - type: mrr_at_5
+      value: 84.013
+    - type: ndcg_at_1
+      value: 77.53
+    - type: ndcg_at_10
+      value: 85.277
+    - type: ndcg_at_100
+      value: 86.80499999999999
+    - type: ndcg_at_1000
+      value: 87.01
+    - type: ndcg_at_3
+      value: 81.975
+    - type: ndcg_at_5
+      value: 83.723
+    - type: precision_at_1
+      value: 77.53
+    - type: precision_at_10
+      value: 12.961
+    - type: precision_at_100
+      value: 1.502
+    - type: precision_at_1000
+      value: 0.156
+    - type: precision_at_3
+      value: 35.713
+    - type: precision_at_5
+      value: 23.574
+    - type: recall_at_1
+      value: 67.51700000000001
+    - type: recall_at_10
+      value: 93.486
+    - type: recall_at_100
+      value: 98.9
+    - type: recall_at_1000
+      value: 99.92999999999999
+    - type: recall_at_3
+      value: 84.17999999999999
+    - type: recall_at_5
+      value: 88.97500000000001
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering
+      name: MTEB RedditClustering
+    metrics:
+    - type: v_measure
+      value: 48.225994608749915
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering-p2p
+      name: MTEB RedditClusteringP2P
+    metrics:
+    - type: v_measure
+      value: 53.17635557157765
+  - task:
+      type: Retrieval
+    dataset:
+      type: scidocs
+      name: MTEB SCIDOCS
+    metrics:
+    - type: map_at_1
+      value: 3.988
+    - type: map_at_10
+      value: 9.4
+    - type: map_at_100
+      value: 10.968
+    - type: map_at_1000
+      value: 11.257
+    - type: map_at_3
+      value: 7.123
+    - type: map_at_5
+      value: 8.221
+    - type: mrr_at_1
+      value: 19.7
+    - type: mrr_at_10
+      value: 29.098000000000003
+    - type: mrr_at_100
+      value: 30.247
+    - type: mrr_at_1000
+      value: 30.318
+    - type: mrr_at_3
+      value: 26.55
+    - type: mrr_at_5
+      value: 27.915
+    - type: ndcg_at_1
+      value: 19.7
+    - type: ndcg_at_10
+      value: 16.176
+    - type: ndcg_at_100
+      value: 22.931
+    - type: ndcg_at_1000
+      value: 28.301
+    - type: ndcg_at_3
+      value: 16.142
+    - type: ndcg_at_5
+      value: 13.633999999999999
+    - type: precision_at_1
+      value: 19.7
+    - type: precision_at_10
+      value: 8.18
+    - type: precision_at_100
+      value: 1.8010000000000002
+    - type: precision_at_1000
+      value: 0.309
+    - type: precision_at_3
+      value: 15.1
+    - type: precision_at_5
+      value: 11.74
+    - type: recall_at_1
+      value: 3.988
+    - type: recall_at_10
+      value: 16.625
+    - type: recall_at_100
+      value: 36.61
+    - type: recall_at_1000
+      value: 62.805
+    - type: recall_at_3
+      value: 9.168
+    - type: recall_at_5
+      value: 11.902
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sickr-sts
+      name: MTEB SICK-R
+    metrics:
+    - type: cos_sim_pearson
+      value: 77.29330379162072
+    - type: cos_sim_spearman
+      value: 67.22953551111448
+    - type: euclidean_pearson
+      value: 71.44682700059415
+    - type: euclidean_spearman
+      value: 66.33178012153247
+    - type: manhattan_pearson
+      value: 71.46941734657887
+    - type: manhattan_spearman
+      value: 66.43234359835814
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts12-sts
+      name: MTEB STS12
+    metrics:
+    - type: cos_sim_pearson
+      value: 75.40943196466576
+    - type: cos_sim_spearman
+      value: 66.59241013465915
+    - type: euclidean_pearson
+      value: 71.32500540796616
+    - type: euclidean_spearman
+      value: 67.86667467202591
+    - type: manhattan_pearson
+      value: 71.48209832089134
+    - type: manhattan_spearman
+      value: 67.94511626964879
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts13-sts
+      name: MTEB STS13
+    metrics:
+    - type: cos_sim_pearson
+      value: 77.08302398877518
+    - type: cos_sim_spearman
+      value: 77.33151317062642
+    - type: euclidean_pearson
+      value: 76.77020279715008
+    - type: euclidean_spearman
+      value: 77.13893776083225
+    - type: manhattan_pearson
+      value: 76.76732290707477
+    - type: manhattan_spearman
+      value: 77.14500877396631
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts14-sts
+      name: MTEB STS14
+    metrics:
+    - type: cos_sim_pearson
+      value: 77.46886184932168
+    - type: cos_sim_spearman
+      value: 71.82815265534886
+    - type: euclidean_pearson
+      value: 75.19783284299076
+    - type: euclidean_spearman
+      value: 71.36479611710412
+    - type: manhattan_pearson
+      value: 75.30375233959337
+    - type: manhattan_spearman
+      value: 71.46280266488021
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts15-sts
+      name: MTEB STS15
+    metrics:
+    - type: cos_sim_pearson
+      value: 80.093017609484
+    - type: cos_sim_spearman
+      value: 80.65931167868882
+    - type: euclidean_pearson
+      value: 80.36786337117047
+    - type: euclidean_spearman
+      value: 81.30521389642827
+    - type: manhattan_pearson
+      value: 80.37922433220973
+    - type: manhattan_spearman
+      value: 81.30496664496285
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts16-sts
+      name: MTEB STS16
+    metrics:
+    - type: cos_sim_pearson
+      value: 77.98998347238742
+    - type: cos_sim_spearman
+      value: 78.91151365939403
+    - type: euclidean_pearson
+      value: 76.40510899217841
+    - type: euclidean_spearman
+      value: 76.8551459824213
+    - type: manhattan_pearson
+      value: 76.3986079603294
+    - type: manhattan_spearman
+      value: 76.8848053254288
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts17-crosslingual-sts
+      name: MTEB STS17 (en-en)
+    metrics:
+    - type: cos_sim_pearson
+      value: 85.63510653472044
+    - type: cos_sim_spearman
+      value: 86.98674844768605
+    - type: euclidean_pearson
+      value: 85.205080538809
+    - type: euclidean_spearman
+      value: 85.53630494151886
+    - type: manhattan_pearson
+      value: 85.48612469885626
+    - type: manhattan_spearman
+      value: 85.81741413931921
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts22-crosslingual-sts
+      name: MTEB STS22 (en)
+    metrics:
+    - type: cos_sim_pearson
+      value: 66.7257987615171
+    - type: cos_sim_spearman
+      value: 67.30387805090024
+    - type: euclidean_pearson
+      value: 69.46877227885867
+    - type: euclidean_spearman
+      value: 69.33161798704344
+    - type: manhattan_pearson
+      value: 69.82773311626424
+    - type: manhattan_spearman
+      value: 69.57199940498796
+  - task:
+      type: STS
+    dataset:
+      type: mteb/stsbenchmark-sts
+      name: MTEB STSBenchmark
+    metrics:
+    - type: cos_sim_pearson
+      value: 79.37322139418472
+    - type: cos_sim_spearman
+      value: 77.5887175717799
+    - type: euclidean_pearson
+      value: 78.23006410562164
+    - type: euclidean_spearman
+      value: 77.18470385673044
+    - type: manhattan_pearson
+      value: 78.40868369362455
+    - type: manhattan_spearman
+      value: 77.36675823897656
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/scidocs-reranking
+      name: MTEB SciDocsRR
+    metrics:
+    - type: map
+      value: 77.21233007730808
+    - type: mrr
+      value: 93.0502386139641
+  - task:
+      type: Retrieval
+    dataset:
+      type: scifact
+      name: MTEB SciFact
+    metrics:
+    - type: map_at_1
+      value: 54.567
+    - type: map_at_10
+      value: 63.653000000000006
+    - type: map_at_100
+      value: 64.282
+    - type: map_at_1000
+      value: 64.31099999999999
+    - type: map_at_3
+      value: 60.478
+    - type: map_at_5
+      value: 62.322
+    - type: mrr_at_1
+      value: 56.99999999999999
+    - type: mrr_at_10
+      value: 64.759
+    - type: mrr_at_100
+      value: 65.274
+    - type: mrr_at_1000
+      value: 65.301
+    - type: mrr_at_3
+      value: 62.333000000000006
+    - type: mrr_at_5
+      value: 63.817
+    - type: ndcg_at_1
+      value: 56.99999999999999
+    - type: ndcg_at_10
+      value: 68.28699999999999
+    - type: ndcg_at_100
+      value: 70.98400000000001
+    - type: ndcg_at_1000
+      value: 71.695
+    - type: ndcg_at_3
+      value: 62.656
+    - type: ndcg_at_5
+      value: 65.523
+    - type: precision_at_1
+      value: 56.99999999999999
+    - type: precision_at_10
+      value: 9.232999999999999
+    - type: precision_at_100
+      value: 1.0630000000000002
+    - type: precision_at_1000
+      value: 0.11199999999999999
+    - type: precision_at_3
+      value: 24.221999999999998
+    - type: precision_at_5
+      value: 16.333000000000002
+    - type: recall_at_1
+      value: 54.567
+    - type: recall_at_10
+      value: 81.45599999999999
+    - type: recall_at_100
+      value: 93.5
+    - type: recall_at_1000
+      value: 99.0
+    - type: recall_at_3
+      value: 66.228
+    - type: recall_at_5
+      value: 73.489
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/sprintduplicatequestions-pairclassification
+      name: MTEB SprintDuplicateQuestions
+    metrics:
+    - type: cos_sim_accuracy
+      value: 99.74455445544554
+    - type: cos_sim_ap
+      value: 92.57836032673468
+    - type: cos_sim_f1
+      value: 87.0471464019851
+    - type: cos_sim_precision
+      value: 86.4039408866995
+    - type: cos_sim_recall
+      value: 87.7
+    - type: dot_accuracy
+      value: 99.56039603960396
+    - type: dot_ap
+      value: 82.47233353407186
+    - type: dot_f1
+      value: 76.78207739307537
+    - type: dot_precision
+      value: 78.21576763485477
+    - type: dot_recall
+      value: 75.4
+    - type: euclidean_accuracy
+      value: 99.73069306930694
+    - type: euclidean_ap
+      value: 91.70507666665775
+    - type: euclidean_f1
+      value: 86.26262626262626
+    - type: euclidean_precision
+      value: 87.14285714285714
+    - type: euclidean_recall
+      value: 85.39999999999999
+    - type: manhattan_accuracy
+      value: 99.73861386138614
+    - type: manhattan_ap
+      value: 91.96809459281754
+    - type: manhattan_f1
+      value: 86.6
+    - type: manhattan_precision
+      value: 86.6
+    - type: manhattan_recall
+      value: 86.6
+    - type: max_accuracy
+      value: 99.74455445544554
+    - type: max_ap
+      value: 92.57836032673468
+    - type: max_f1
+      value: 87.0471464019851
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering
+      name: MTEB StackExchangeClustering
+    metrics:
+    - type: v_measure
+      value: 60.85593925770172
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering-p2p
+      name: MTEB StackExchangeClusteringP2P
+    metrics:
+    - type: v_measure
+      value: 32.356772998237496
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/stackoverflowdupquestions-reranking
+      name: MTEB StackOverflowDupQuestions
+    metrics:
+    - type: map
+      value: 49.320607035290735
+    - type: mrr
+      value: 50.09196481622952
+  - task:
+      type: Summarization
+    dataset:
+      type: mteb/summeval
+      name: MTEB SummEval
+    metrics:
+    - type: cos_sim_pearson
+      value: 25.57602918901377
+    - type: cos_sim_spearman
+      value: 25.440272876996694
+    - type: dot_pearson
+      value: 24.909680980895065
+    - type: dot_spearman
+      value: 24.032627570006824
+  - task:
+      type: Retrieval
+    dataset:
+      type: trec-covid
+      name: MTEB TRECCOVID
+    metrics:
+    - type: map_at_1
+      value: 0.22100000000000003
+    - type: map_at_10
+      value: 1.7229999999999999
+    - type: map_at_100
+      value: 9.195
+    - type: map_at_1000
+      value: 21.999
+    - type: map_at_3
+      value: 0.6479999999999999
+    - type: map_at_5
+      value: 0.964
+    - type: mrr_at_1
+      value: 86.0
+    - type: mrr_at_10
+      value: 90.667
+    - type: mrr_at_100
+      value: 90.858
+    - type: mrr_at_1000
+      value: 90.858
+    - type: mrr_at_3
+      value: 90.667
+    - type: mrr_at_5
+      value: 90.667
+    - type: ndcg_at_1
+      value: 82.0
+    - type: ndcg_at_10
+      value: 72.98
+    - type: ndcg_at_100
+      value: 52.868
+    - type: ndcg_at_1000
+      value: 46.541
+    - type: ndcg_at_3
+      value: 80.39699999999999
+    - type: ndcg_at_5
+      value: 76.303
+    - type: precision_at_1
+      value: 86.0
+    - type: precision_at_10
+      value: 75.8
+    - type: precision_at_100
+      value: 53.5
+    - type: precision_at_1000
+      value: 20.946
+    - type: precision_at_3
+      value: 85.333
+    - type: precision_at_5
+      value: 79.2
+    - type: recall_at_1
+      value: 0.22100000000000003
+    - type: recall_at_10
+      value: 1.9109999999999998
+    - type: recall_at_100
+      value: 12.437
+    - type: recall_at_1000
+      value: 43.606
+    - type: recall_at_3
+      value: 0.681
+    - type: recall_at_5
+      value: 1.023
+  - task:
+      type: Retrieval
+    dataset:
+      type: webis-touche2020
+      name: MTEB Touche2020
+    metrics:
+    - type: map_at_1
+      value: 2.5
+    - type: map_at_10
+      value: 9.568999999999999
+    - type: map_at_100
+      value: 15.653
+    - type: map_at_1000
+      value: 17.188
+    - type: map_at_3
+      value: 5.335999999999999
+    - type: map_at_5
+      value: 6.522
+    - type: mrr_at_1
+      value: 34.694
+    - type: mrr_at_10
+      value: 49.184
+    - type: mrr_at_100
+      value: 50.512
+    - type: mrr_at_1000
+      value: 50.512
+    - type: mrr_at_3
+      value: 46.259
+    - type: mrr_at_5
+      value: 48.299
+    - type: ndcg_at_1
+      value: 30.612000000000002
+    - type: ndcg_at_10
+      value: 24.45
+    - type: ndcg_at_100
+      value: 35.870999999999995
+    - type: ndcg_at_1000
+      value: 47.272999999999996
+    - type: ndcg_at_3
+      value: 28.528
+    - type: ndcg_at_5
+      value: 25.768
+    - type: precision_at_1
+      value: 34.694
+    - type: precision_at_10
+      value: 21.429000000000002
+    - type: precision_at_100
+      value: 7.265000000000001
+    - type: precision_at_1000
+      value: 1.504
+    - type: precision_at_3
+      value: 29.252
+    - type: precision_at_5
+      value: 24.898
+    - type: recall_at_1
+      value: 2.5
+    - type: recall_at_10
+      value: 15.844
+    - type: recall_at_100
+      value: 45.469
+    - type: recall_at_1000
+      value: 81.148
+    - type: recall_at_3
+      value: 6.496
+    - type: recall_at_5
+      value: 8.790000000000001
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/toxic_conversations_50k
+      name: MTEB ToxicConversationsClassification
+    metrics:
+    - type: accuracy
+      value: 68.7272
+    - type: ap
+      value: 13.156450706152686
+    - type: f1
+      value: 52.814703437064395
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/tweet_sentiment_extraction
+      name: MTEB TweetSentimentExtractionClassification
+    metrics:
+    - type: accuracy
+      value: 55.6677985285795
+    - type: f1
+      value: 55.9373937514999
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/twentynewsgroups-clustering
+      name: MTEB TwentyNewsgroupsClustering
+    metrics:
+    - type: v_measure
+      value: 40.05809562275603
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twittersemeval2015-pairclassification
+      name: MTEB TwitterSemEval2015
+    metrics:
+    - type: cos_sim_accuracy
+      value: 82.76807534124099
+    - type: cos_sim_ap
+      value: 62.37052608803734
+    - type: cos_sim_f1
+      value: 59.077414934916646
+    - type: cos_sim_precision
+      value: 52.07326892109501
+    - type: cos_sim_recall
+      value: 68.25857519788919
+    - type: dot_accuracy
+      value: 80.56267509089825
+    - type: dot_ap
+      value: 54.75349561321037
+    - type: dot_f1
+      value: 54.75483794372552
+    - type: dot_precision
+      value: 49.77336499028707
+    - type: dot_recall
+      value: 60.844327176781
+    - type: euclidean_accuracy
+      value: 82.476008821601
+    - type: euclidean_ap
+      value: 61.17417554210511
+    - type: euclidean_f1
+      value: 57.80318696022382
+    - type: euclidean_precision
+      value: 53.622207176709544
+    - type: euclidean_recall
+      value: 62.69129287598945
+    - type: manhattan_accuracy
+      value: 82.48792990403528
+    - type: manhattan_ap
+      value: 61.044816292966544
+    - type: manhattan_f1
+      value: 58.03033951360462
+    - type: manhattan_precision
+      value: 53.36581045172719
+    - type: manhattan_recall
+      value: 63.58839050131926
+    - type: max_accuracy
+      value: 82.76807534124099
+    - type: max_ap
+      value: 62.37052608803734
+    - type: max_f1
+      value: 59.077414934916646
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twitterurlcorpus-pairclassification
+      name: MTEB TwitterURLCorpus
+    metrics:
+    - type: cos_sim_accuracy
+      value: 87.97881010594946
+    - type: cos_sim_ap
+      value: 83.78748636891035
+    - type: cos_sim_f1
+      value: 75.94113995691386
+    - type: cos_sim_precision
+      value: 72.22029307590805
+    - type: cos_sim_recall
+      value: 80.06621496766245
+    - type: dot_accuracy
+      value: 85.69294058291614
+    - type: dot_ap
+      value: 78.15363722278026
+    - type: dot_f1
+      value: 72.08894926888564
+    - type: dot_precision
+      value: 67.28959487419075
+    - type: dot_recall
+      value: 77.62550046196489
+    - type: euclidean_accuracy
+      value: 87.73625179493149
+    - type: euclidean_ap
+      value: 83.19012184470559
+    - type: euclidean_f1
+      value: 75.5148064623461
+    - type: euclidean_precision
+      value: 72.63352535381551
+    - type: euclidean_recall
+      value: 78.6341238065907
+    - type: manhattan_accuracy
+      value: 87.74013272790779
+    - type: manhattan_ap
+      value: 83.23305405113403
+    - type: manhattan_f1
+      value: 75.63960775639607
+    - type: manhattan_precision
+      value: 72.563304569246
+    - type: manhattan_recall
+      value: 78.9882968894364
+    - type: max_accuracy
+      value: 87.97881010594946
+    - type: max_ap
+      value: 83.78748636891035
+    - type: max_f1
+      value: 75.94113995691386
 ---
 
 # SGPT-1.3B-weightedmean-msmarco-specb-bitfit