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README.md

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+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- mteb
+model-index:
+- name: stella-base-en-v2
+  results:
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_counterfactual
+      name: MTEB AmazonCounterfactualClassification (en)
+      config: en
+      split: test
+      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
+    metrics:
+    - type: accuracy
+      value: 77.19402985074628
+    - type: ap
+      value: 40.43267503017359
+    - type: f1
+      value: 71.15585210518594
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_polarity
+      name: MTEB AmazonPolarityClassification
+      config: default
+      split: test
+      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
+    metrics:
+    - type: accuracy
+      value: 93.256675
+    - type: ap
+      value: 90.00824833079179
+    - type: f1
+      value: 93.2473146151734
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_reviews_multi
+      name: MTEB AmazonReviewsClassification (en)
+      config: en
+      split: test
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+    metrics:
+    - type: accuracy
+      value: 49.612
+    - type: f1
+      value: 48.530785631574304
+  - task:
+      type: Retrieval
+    dataset:
+      type: arguana
+      name: MTEB ArguAna
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 37.411
+    - type: map_at_10
+      value: 52.673
+    - type: map_at_100
+      value: 53.410999999999994
+    - type: map_at_1000
+      value: 53.415
+    - type: map_at_3
+      value: 48.495
+    - type: map_at_5
+      value: 51.183
+    - type: mrr_at_1
+      value: 37.838
+    - type: mrr_at_10
+      value: 52.844
+    - type: mrr_at_100
+      value: 53.581999999999994
+    - type: mrr_at_1000
+      value: 53.586
+    - type: mrr_at_3
+      value: 48.672
+    - type: mrr_at_5
+      value: 51.272
+    - type: ndcg_at_1
+      value: 37.411
+    - type: ndcg_at_10
+      value: 60.626999999999995
+    - type: ndcg_at_100
+      value: 63.675000000000004
+    - type: ndcg_at_1000
+      value: 63.776999999999994
+    - type: ndcg_at_3
+      value: 52.148
+    - type: ndcg_at_5
+      value: 57.001999999999995
+    - type: precision_at_1
+      value: 37.411
+    - type: precision_at_10
+      value: 8.578
+    - type: precision_at_100
+      value: 0.989
+    - type: precision_at_1000
+      value: 0.1
+    - type: precision_at_3
+      value: 20.91
+    - type: precision_at_5
+      value: 14.908
+    - type: recall_at_1
+      value: 37.411
+    - type: recall_at_10
+      value: 85.775
+    - type: recall_at_100
+      value: 98.86200000000001
+    - type: recall_at_1000
+      value: 99.644
+    - type: recall_at_3
+      value: 62.731
+    - type: recall_at_5
+      value: 74.53800000000001
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-p2p
+      name: MTEB ArxivClusteringP2P
+      config: default
+      split: test
+      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
+    metrics:
+    - type: v_measure
+      value: 47.24219029437865
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-s2s
+      name: MTEB ArxivClusteringS2S
+      config: default
+      split: test
+      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
+    metrics:
+    - type: v_measure
+      value: 40.474604844291726
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/askubuntudupquestions-reranking
+      name: MTEB AskUbuntuDupQuestions
+      config: default
+      split: test
+      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
+    metrics:
+    - type: map
+      value: 62.720542706366054
+    - type: mrr
+      value: 75.59633733456448
+  - task:
+      type: STS
+    dataset:
+      type: mteb/biosses-sts
+      name: MTEB BIOSSES
+      config: default
+      split: test
+      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
+    metrics:
+    - type: cos_sim_pearson
+      value: 86.31345008397868
+    - type: cos_sim_spearman
+      value: 85.94292212320399
+    - type: euclidean_pearson
+      value: 85.03974302774525
+    - type: euclidean_spearman
+      value: 85.88087251659051
+    - type: manhattan_pearson
+      value: 84.91900996712951
+    - type: manhattan_spearman
+      value: 85.96701905781116
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/banking77
+      name: MTEB Banking77Classification
+      config: default
+      split: test
+      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
+    metrics:
+    - type: accuracy
+      value: 84.72727272727273
+    - type: f1
+      value: 84.29572512364581
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-p2p
+      name: MTEB BiorxivClusteringP2P
+      config: default
+      split: test
+      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
+    metrics:
+    - type: v_measure
+      value: 39.55532460397536
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-s2s
+      name: MTEB BiorxivClusteringS2S
+      config: default
+      split: test
+      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
+    metrics:
+    - type: v_measure
+      value: 35.91195973591251
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackAndroidRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 32.822
+    - type: map_at_10
+      value: 44.139
+    - type: map_at_100
+      value: 45.786
+    - type: map_at_1000
+      value: 45.906000000000006
+    - type: map_at_3
+      value: 40.637
+    - type: map_at_5
+      value: 42.575
+    - type: mrr_at_1
+      value: 41.059
+    - type: mrr_at_10
+      value: 50.751000000000005
+    - type: mrr_at_100
+      value: 51.548
+    - type: mrr_at_1000
+      value: 51.583999999999996
+    - type: mrr_at_3
+      value: 48.236000000000004
+    - type: mrr_at_5
+      value: 49.838
+    - type: ndcg_at_1
+      value: 41.059
+    - type: ndcg_at_10
+      value: 50.573
+    - type: ndcg_at_100
+      value: 56.25
+    - type: ndcg_at_1000
+      value: 58.004
+    - type: ndcg_at_3
+      value: 45.995000000000005
+    - type: ndcg_at_5
+      value: 48.18
+    - type: precision_at_1
+      value: 41.059
+    - type: precision_at_10
+      value: 9.757
+    - type: precision_at_100
+      value: 1.609
+    - type: precision_at_1000
+      value: 0.20600000000000002
+    - type: precision_at_3
+      value: 22.222
+    - type: precision_at_5
+      value: 16.023
+    - type: recall_at_1
+      value: 32.822
+    - type: recall_at_10
+      value: 61.794000000000004
+    - type: recall_at_100
+      value: 85.64699999999999
+    - type: recall_at_1000
+      value: 96.836
+    - type: recall_at_3
+      value: 47.999
+    - type: recall_at_5
+      value: 54.376999999999995
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackEnglishRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 29.579
+    - type: map_at_10
+      value: 39.787
+    - type: map_at_100
+      value: 40.976
+    - type: map_at_1000
+      value: 41.108
+    - type: map_at_3
+      value: 36.819
+    - type: map_at_5
+      value: 38.437
+    - type: mrr_at_1
+      value: 37.516
+    - type: mrr_at_10
+      value: 45.822
+    - type: mrr_at_100
+      value: 46.454
+    - type: mrr_at_1000
+      value: 46.495999999999995
+    - type: mrr_at_3
+      value: 43.556
+    - type: mrr_at_5
+      value: 44.814
+    - type: ndcg_at_1
+      value: 37.516
+    - type: ndcg_at_10
+      value: 45.5
+    - type: ndcg_at_100
+      value: 49.707
+    - type: ndcg_at_1000
+      value: 51.842
+    - type: ndcg_at_3
+      value: 41.369
+    - type: ndcg_at_5
+      value: 43.161
+    - type: precision_at_1
+      value: 37.516
+    - type: precision_at_10
+      value: 8.713
+    - type: precision_at_100
+      value: 1.38
+    - type: precision_at_1000
+      value: 0.188
+    - type: precision_at_3
+      value: 20.233999999999998
+    - type: precision_at_5
+      value: 14.280000000000001
+    - type: recall_at_1
+      value: 29.579
+    - type: recall_at_10
+      value: 55.458
+    - type: recall_at_100
+      value: 73.49799999999999
+    - type: recall_at_1000
+      value: 87.08200000000001
+    - type: recall_at_3
+      value: 42.858000000000004
+    - type: recall_at_5
+      value: 48.215
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGamingRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 40.489999999999995
+    - type: map_at_10
+      value: 53.313
+    - type: map_at_100
+      value: 54.290000000000006
+    - type: map_at_1000
+      value: 54.346000000000004
+    - type: map_at_3
+      value: 49.983
+    - type: map_at_5
+      value: 51.867
+    - type: mrr_at_1
+      value: 46.27
+    - type: mrr_at_10
+      value: 56.660999999999994
+    - type: mrr_at_100
+      value: 57.274
+    - type: mrr_at_1000
+      value: 57.301
+    - type: mrr_at_3
+      value: 54.138
+    - type: mrr_at_5
+      value: 55.623999999999995
+    - type: ndcg_at_1
+      value: 46.27
+    - type: ndcg_at_10
+      value: 59.192
+    - type: ndcg_at_100
+      value: 63.026
+    - type: ndcg_at_1000
+      value: 64.079
+    - type: ndcg_at_3
+      value: 53.656000000000006
+    - type: ndcg_at_5
+      value: 56.387
+    - type: precision_at_1
+      value: 46.27
+    - type: precision_at_10
+      value: 9.511
+    - type: precision_at_100
+      value: 1.23
+    - type: precision_at_1000
+      value: 0.136
+    - type: precision_at_3
+      value: 24.096
+    - type: precision_at_5
+      value: 16.476
+    - type: recall_at_1
+      value: 40.489999999999995
+    - type: recall_at_10
+      value: 73.148
+    - type: recall_at_100
+      value: 89.723
+    - type: recall_at_1000
+      value: 97.073
+    - type: recall_at_3
+      value: 58.363
+    - type: recall_at_5
+      value: 65.083
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGisRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 26.197
+    - type: map_at_10
+      value: 35.135
+    - type: map_at_100
+      value: 36.14
+    - type: map_at_1000
+      value: 36.216
+    - type: map_at_3
+      value: 32.358
+    - type: map_at_5
+      value: 33.814
+    - type: mrr_at_1
+      value: 28.475
+    - type: mrr_at_10
+      value: 37.096000000000004
+    - type: mrr_at_100
+      value: 38.006
+    - type: mrr_at_1000
+      value: 38.06
+    - type: mrr_at_3
+      value: 34.52
+    - type: mrr_at_5
+      value: 35.994
+    - type: ndcg_at_1
+      value: 28.475
+    - type: ndcg_at_10
+      value: 40.263
+    - type: ndcg_at_100
+      value: 45.327
+    - type: ndcg_at_1000
+      value: 47.225
+    - type: ndcg_at_3
+      value: 34.882000000000005
+    - type: ndcg_at_5
+      value: 37.347
+    - type: precision_at_1
+      value: 28.475
+    - type: precision_at_10
+      value: 6.249
+    - type: precision_at_100
+      value: 0.919
+    - type: precision_at_1000
+      value: 0.11199999999999999
+    - type: precision_at_3
+      value: 14.689
+    - type: precision_at_5
+      value: 10.237
+    - type: recall_at_1
+      value: 26.197
+    - type: recall_at_10
+      value: 54.17999999999999
+    - type: recall_at_100
+      value: 77.768
+    - type: recall_at_1000
+      value: 91.932
+    - type: recall_at_3
+      value: 39.804
+    - type: recall_at_5
+      value: 45.660000000000004
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackMathematicaRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 16.683
+    - type: map_at_10
+      value: 25.013999999999996
+    - type: map_at_100
+      value: 26.411
+    - type: map_at_1000
+      value: 26.531
+    - type: map_at_3
+      value: 22.357
+    - type: map_at_5
+      value: 23.982999999999997
+    - type: mrr_at_1
+      value: 20.896
+    - type: mrr_at_10
+      value: 29.758000000000003
+    - type: mrr_at_100
+      value: 30.895
+    - type: mrr_at_1000
+      value: 30.964999999999996
+    - type: mrr_at_3
+      value: 27.177
+    - type: mrr_at_5
+      value: 28.799999999999997
+    - type: ndcg_at_1
+      value: 20.896
+    - type: ndcg_at_10
+      value: 30.294999999999998
+    - type: ndcg_at_100
+      value: 36.68
+    - type: ndcg_at_1000
+      value: 39.519
+    - type: ndcg_at_3
+      value: 25.480999999999998
+    - type: ndcg_at_5
+      value: 28.027
+    - type: precision_at_1
+      value: 20.896
+    - type: precision_at_10
+      value: 5.56
+    - type: precision_at_100
+      value: 1.006
+    - type: precision_at_1000
+      value: 0.13899999999999998
+    - type: precision_at_3
+      value: 12.231
+    - type: precision_at_5
+      value: 9.104
+    - type: recall_at_1
+      value: 16.683
+    - type: recall_at_10
+      value: 41.807
+    - type: recall_at_100
+      value: 69.219
+    - type: recall_at_1000
+      value: 89.178
+    - type: recall_at_3
+      value: 28.772
+    - type: recall_at_5
+      value: 35.167
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackPhysicsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 30.653000000000002
+    - type: map_at_10
+      value: 41.21
+    - type: map_at_100
+      value: 42.543
+    - type: map_at_1000
+      value: 42.657000000000004
+    - type: map_at_3
+      value: 38.094
+    - type: map_at_5
+      value: 39.966
+    - type: mrr_at_1
+      value: 37.824999999999996
+    - type: mrr_at_10
+      value: 47.087
+    - type: mrr_at_100
+      value: 47.959
+    - type: mrr_at_1000
+      value: 48.003
+    - type: mrr_at_3
+      value: 45.043
+    - type: mrr_at_5
+      value: 46.352
+    - type: ndcg_at_1
+      value: 37.824999999999996
+    - type: ndcg_at_10
+      value: 47.158
+    - type: ndcg_at_100
+      value: 52.65
+    - type: ndcg_at_1000
+      value: 54.644999999999996
+    - type: ndcg_at_3
+      value: 42.632999999999996
+    - type: ndcg_at_5
+      value: 44.994
+    - type: precision_at_1
+      value: 37.824999999999996
+    - type: precision_at_10
+      value: 8.498999999999999
+    - type: precision_at_100
+      value: 1.308
+    - type: precision_at_1000
+      value: 0.166
+    - type: precision_at_3
+      value: 20.308
+    - type: precision_at_5
+      value: 14.283000000000001
+    - type: recall_at_1
+      value: 30.653000000000002
+    - type: recall_at_10
+      value: 58.826
+    - type: recall_at_100
+      value: 81.94
+    - type: recall_at_1000
+      value: 94.71000000000001
+    - type: recall_at_3
+      value: 45.965
+    - type: recall_at_5
+      value: 52.294
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackProgrammersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 26.71
+    - type: map_at_10
+      value: 36.001
+    - type: map_at_100
+      value: 37.416
+    - type: map_at_1000
+      value: 37.522
+    - type: map_at_3
+      value: 32.841
+    - type: map_at_5
+      value: 34.515
+    - type: mrr_at_1
+      value: 32.647999999999996
+    - type: mrr_at_10
+      value: 41.43
+    - type: mrr_at_100
+      value: 42.433
+    - type: mrr_at_1000
+      value: 42.482
+    - type: mrr_at_3
+      value: 39.117000000000004
+    - type: mrr_at_5
+      value: 40.35
+    - type: ndcg_at_1
+      value: 32.647999999999996
+    - type: ndcg_at_10
+      value: 41.629
+    - type: ndcg_at_100
+      value: 47.707
+    - type: ndcg_at_1000
+      value: 49.913000000000004
+    - type: ndcg_at_3
+      value: 36.598000000000006
+    - type: ndcg_at_5
+      value: 38.696000000000005
+    - type: precision_at_1
+      value: 32.647999999999996
+    - type: precision_at_10
+      value: 7.704999999999999
+    - type: precision_at_100
+      value: 1.242
+    - type: precision_at_1000
+      value: 0.16
+    - type: precision_at_3
+      value: 17.314
+    - type: precision_at_5
+      value: 12.374
+    - type: recall_at_1
+      value: 26.71
+    - type: recall_at_10
+      value: 52.898
+    - type: recall_at_100
+      value: 79.08
+    - type: recall_at_1000
+      value: 93.94
+    - type: recall_at_3
+      value: 38.731
+    - type: recall_at_5
+      value: 44.433
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 26.510999999999996
+    - type: map_at_10
+      value: 35.755333333333326
+    - type: map_at_100
+      value: 36.97525
+    - type: map_at_1000
+      value: 37.08741666666667
+    - type: map_at_3
+      value: 32.921
+    - type: map_at_5
+      value: 34.45041666666667
+    - type: mrr_at_1
+      value: 31.578416666666666
+    - type: mrr_at_10
+      value: 40.06066666666667
+    - type: mrr_at_100
+      value: 40.93350000000001
+    - type: mrr_at_1000
+      value: 40.98716666666667
+    - type: mrr_at_3
+      value: 37.710499999999996
+    - type: mrr_at_5
+      value: 39.033249999999995
+    - type: ndcg_at_1
+      value: 31.578416666666666
+    - type: ndcg_at_10
+      value: 41.138666666666666
+    - type: ndcg_at_100
+      value: 46.37291666666666
+    - type: ndcg_at_1000
+      value: 48.587500000000006
+    - type: ndcg_at_3
+      value: 36.397083333333335
+    - type: ndcg_at_5
+      value: 38.539
+    - type: precision_at_1
+      value: 31.578416666666666
+    - type: precision_at_10
+      value: 7.221583333333332
+    - type: precision_at_100
+      value: 1.1581666666666668
+    - type: precision_at_1000
+      value: 0.15416666666666667
+    - type: precision_at_3
+      value: 16.758
+    - type: precision_at_5
+      value: 11.830916666666665
+    - type: recall_at_1
+      value: 26.510999999999996
+    - type: recall_at_10
+      value: 52.7825
+    - type: recall_at_100
+      value: 75.79675
+    - type: recall_at_1000
+      value: 91.10483333333335
+    - type: recall_at_3
+      value: 39.48233333333334
+    - type: recall_at_5
+      value: 45.07116666666667
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackStatsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 24.564
+    - type: map_at_10
+      value: 31.235000000000003
+    - type: map_at_100
+      value: 32.124
+    - type: map_at_1000
+      value: 32.216
+    - type: map_at_3
+      value: 29.330000000000002
+    - type: map_at_5
+      value: 30.379
+    - type: mrr_at_1
+      value: 27.761000000000003
+    - type: mrr_at_10
+      value: 34.093
+    - type: mrr_at_100
+      value: 34.885
+    - type: mrr_at_1000
+      value: 34.957
+    - type: mrr_at_3
+      value: 32.388
+    - type: mrr_at_5
+      value: 33.269
+    - type: ndcg_at_1
+      value: 27.761000000000003
+    - type: ndcg_at_10
+      value: 35.146
+    - type: ndcg_at_100
+      value: 39.597
+    - type: ndcg_at_1000
+      value: 42.163000000000004
+    - type: ndcg_at_3
+      value: 31.674000000000003
+    - type: ndcg_at_5
+      value: 33.224
+    - type: precision_at_1
+      value: 27.761000000000003
+    - type: precision_at_10
+      value: 5.383
+    - type: precision_at_100
+      value: 0.836
+    - type: precision_at_1000
+      value: 0.11199999999999999
+    - type: precision_at_3
+      value: 13.599
+    - type: precision_at_5
+      value: 9.202
+    - type: recall_at_1
+      value: 24.564
+    - type: recall_at_10
+      value: 44.36
+    - type: recall_at_100
+      value: 64.408
+    - type: recall_at_1000
+      value: 83.892
+    - type: recall_at_3
+      value: 34.653
+    - type: recall_at_5
+      value: 38.589
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackTexRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 17.01
+    - type: map_at_10
+      value: 24.485
+    - type: map_at_100
+      value: 25.573
+    - type: map_at_1000
+      value: 25.703
+    - type: map_at_3
+      value: 21.953
+    - type: map_at_5
+      value: 23.294999999999998
+    - type: mrr_at_1
+      value: 20.544
+    - type: mrr_at_10
+      value: 28.238000000000003
+    - type: mrr_at_100
+      value: 29.142000000000003
+    - type: mrr_at_1000
+      value: 29.219
+    - type: mrr_at_3
+      value: 25.802999999999997
+    - type: mrr_at_5
+      value: 27.105
+    - type: ndcg_at_1
+      value: 20.544
+    - type: ndcg_at_10
+      value: 29.387999999999998
+    - type: ndcg_at_100
+      value: 34.603
+    - type: ndcg_at_1000
+      value: 37.564
+    - type: ndcg_at_3
+      value: 24.731
+    - type: ndcg_at_5
+      value: 26.773000000000003
+    - type: precision_at_1
+      value: 20.544
+    - type: precision_at_10
+      value: 5.509
+    - type: precision_at_100
+      value: 0.9450000000000001
+    - type: precision_at_1000
+      value: 0.13799999999999998
+    - type: precision_at_3
+      value: 11.757
+    - type: precision_at_5
+      value: 8.596
+    - type: recall_at_1
+      value: 17.01
+    - type: recall_at_10
+      value: 40.392
+    - type: recall_at_100
+      value: 64.043
+    - type: recall_at_1000
+      value: 85.031
+    - type: recall_at_3
+      value: 27.293
+    - type: recall_at_5
+      value: 32.586999999999996
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackUnixRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 27.155
+    - type: map_at_10
+      value: 35.92
+    - type: map_at_100
+      value: 37.034
+    - type: map_at_1000
+      value: 37.139
+    - type: map_at_3
+      value: 33.263999999999996
+    - type: map_at_5
+      value: 34.61
+    - type: mrr_at_1
+      value: 32.183
+    - type: mrr_at_10
+      value: 40.099000000000004
+    - type: mrr_at_100
+      value: 41.001
+    - type: mrr_at_1000
+      value: 41.059
+    - type: mrr_at_3
+      value: 37.889
+    - type: mrr_at_5
+      value: 39.007999999999996
+    - type: ndcg_at_1
+      value: 32.183
+    - type: ndcg_at_10
+      value: 41.127
+    - type: ndcg_at_100
+      value: 46.464
+    - type: ndcg_at_1000
+      value: 48.67
+    - type: ndcg_at_3
+      value: 36.396
+    - type: ndcg_at_5
+      value: 38.313
+    - type: precision_at_1
+      value: 32.183
+    - type: precision_at_10
+      value: 6.847
+    - type: precision_at_100
+      value: 1.0739999999999998
+    - type: precision_at_1000
+      value: 0.13699999999999998
+    - type: precision_at_3
+      value: 16.356
+    - type: precision_at_5
+      value: 11.362
+    - type: recall_at_1
+      value: 27.155
+    - type: recall_at_10
+      value: 52.922000000000004
+    - type: recall_at_100
+      value: 76.39
+    - type: recall_at_1000
+      value: 91.553
+    - type: recall_at_3
+      value: 39.745999999999995
+    - type: recall_at_5
+      value: 44.637
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWebmastersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 25.523
+    - type: map_at_10
+      value: 34.268
+    - type: map_at_100
+      value: 35.835
+    - type: map_at_1000
+      value: 36.046
+    - type: map_at_3
+      value: 31.662000000000003
+    - type: map_at_5
+      value: 32.71
+    - type: mrr_at_1
+      value: 31.028
+    - type: mrr_at_10
+      value: 38.924
+    - type: mrr_at_100
+      value: 39.95
+    - type: mrr_at_1000
+      value: 40.003
+    - type: mrr_at_3
+      value: 36.594
+    - type: mrr_at_5
+      value: 37.701
+    - type: ndcg_at_1
+      value: 31.028
+    - type: ndcg_at_10
+      value: 39.848
+    - type: ndcg_at_100
+      value: 45.721000000000004
+    - type: ndcg_at_1000
+      value: 48.424
+    - type: ndcg_at_3
+      value: 35.329
+    - type: ndcg_at_5
+      value: 36.779
+    - type: precision_at_1
+      value: 31.028
+    - type: precision_at_10
+      value: 7.51
+    - type: precision_at_100
+      value: 1.478
+    - type: precision_at_1000
+      value: 0.24
+    - type: precision_at_3
+      value: 16.337
+    - type: precision_at_5
+      value: 11.383000000000001
+    - type: recall_at_1
+      value: 25.523
+    - type: recall_at_10
+      value: 50.735
+    - type: recall_at_100
+      value: 76.593
+    - type: recall_at_1000
+      value: 93.771
+    - type: recall_at_3
+      value: 37.574000000000005
+    - type: recall_at_5
+      value: 41.602
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWordpressRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 20.746000000000002
+    - type: map_at_10
+      value: 28.557
+    - type: map_at_100
+      value: 29.575000000000003
+    - type: map_at_1000
+      value: 29.659000000000002
+    - type: map_at_3
+      value: 25.753999999999998
+    - type: map_at_5
+      value: 27.254
+    - type: mrr_at_1
+      value: 22.736
+    - type: mrr_at_10
+      value: 30.769000000000002
+    - type: mrr_at_100
+      value: 31.655
+    - type: mrr_at_1000
+      value: 31.717000000000002
+    - type: mrr_at_3
+      value: 28.065
+    - type: mrr_at_5
+      value: 29.543999999999997
+    - type: ndcg_at_1
+      value: 22.736
+    - type: ndcg_at_10
+      value: 33.545
+    - type: ndcg_at_100
+      value: 38.743
+    - type: ndcg_at_1000
+      value: 41.002
+    - type: ndcg_at_3
+      value: 28.021
+    - type: ndcg_at_5
+      value: 30.586999999999996
+    - type: precision_at_1
+      value: 22.736
+    - type: precision_at_10
+      value: 5.416
+    - type: precision_at_100
+      value: 0.8710000000000001
+    - type: precision_at_1000
+      value: 0.116
+    - type: precision_at_3
+      value: 11.953
+    - type: precision_at_5
+      value: 8.651
+    - type: recall_at_1
+      value: 20.746000000000002
+    - type: recall_at_10
+      value: 46.87
+    - type: recall_at_100
+      value: 71.25200000000001
+    - type: recall_at_1000
+      value: 88.26
+    - type: recall_at_3
+      value: 32.029999999999994
+    - type: recall_at_5
+      value: 38.21
+  - task:
+      type: Retrieval
+    dataset:
+      type: climate-fever
+      name: MTEB ClimateFEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 12.105
+    - type: map_at_10
+      value: 20.577
+    - type: map_at_100
+      value: 22.686999999999998
+    - type: map_at_1000
+      value: 22.889
+    - type: map_at_3
+      value: 17.174
+    - type: map_at_5
+      value: 18.807
+    - type: mrr_at_1
+      value: 27.101
+    - type: mrr_at_10
+      value: 38.475
+    - type: mrr_at_100
+      value: 39.491
+    - type: mrr_at_1000
+      value: 39.525
+    - type: mrr_at_3
+      value: 34.886
+    - type: mrr_at_5
+      value: 36.922
+    - type: ndcg_at_1
+      value: 27.101
+    - type: ndcg_at_10
+      value: 29.002
+    - type: ndcg_at_100
+      value: 37.218
+    - type: ndcg_at_1000
+      value: 40.644000000000005
+    - type: ndcg_at_3
+      value: 23.464
+    - type: ndcg_at_5
+      value: 25.262
+    - type: precision_at_1
+      value: 27.101
+    - type: precision_at_10
+      value: 9.179
+    - type: precision_at_100
+      value: 1.806
+    - type: precision_at_1000
+      value: 0.244
+    - type: precision_at_3
+      value: 17.394000000000002
+    - type: precision_at_5
+      value: 13.342
+    - type: recall_at_1
+      value: 12.105
+    - type: recall_at_10
+      value: 35.143
+    - type: recall_at_100
+      value: 63.44499999999999
+    - type: recall_at_1000
+      value: 82.49499999999999
+    - type: recall_at_3
+      value: 21.489
+    - type: recall_at_5
+      value: 26.82
+  - task:
+      type: Retrieval
+    dataset:
+      type: dbpedia-entity
+      name: MTEB DBPedia
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 8.769
+    - type: map_at_10
+      value: 18.619
+    - type: map_at_100
+      value: 26.3
+    - type: map_at_1000
+      value: 28.063
+    - type: map_at_3
+      value: 13.746
+    - type: map_at_5
+      value: 16.035
+    - type: mrr_at_1
+      value: 65.25
+    - type: mrr_at_10
+      value: 73.678
+    - type: mrr_at_100
+      value: 73.993
+    - type: mrr_at_1000
+      value: 74.003
+    - type: mrr_at_3
+      value: 72.042
+    - type: mrr_at_5
+      value: 72.992
+    - type: ndcg_at_1
+      value: 53.625
+    - type: ndcg_at_10
+      value: 39.638
+    - type: ndcg_at_100
+      value: 44.601
+    - type: ndcg_at_1000
+      value: 52.80200000000001
+    - type: ndcg_at_3
+      value: 44.727
+    - type: ndcg_at_5
+      value: 42.199
+    - type: precision_at_1
+      value: 65.25
+    - type: precision_at_10
+      value: 31.025000000000002
+    - type: precision_at_100
+      value: 10.174999999999999
+    - type: precision_at_1000
+      value: 2.0740000000000003
+    - type: precision_at_3
+      value: 48.083
+    - type: precision_at_5
+      value: 40.6
+    - type: recall_at_1
+      value: 8.769
+    - type: recall_at_10
+      value: 23.910999999999998
+    - type: recall_at_100
+      value: 51.202999999999996
+    - type: recall_at_1000
+      value: 77.031
+    - type: recall_at_3
+      value: 15.387999999999998
+    - type: recall_at_5
+      value: 18.919
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/emotion
+      name: MTEB EmotionClassification
+      config: default
+      split: test
+      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
+    metrics:
+    - type: accuracy
+      value: 54.47
+    - type: f1
+      value: 48.21839043361556
+  - task:
+      type: Retrieval
+    dataset:
+      type: fever
+      name: MTEB FEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 63.564
+    - type: map_at_10
+      value: 74.236
+    - type: map_at_100
+      value: 74.53699999999999
+    - type: map_at_1000
+      value: 74.557
+    - type: map_at_3
+      value: 72.556
+    - type: map_at_5
+      value: 73.656
+    - type: mrr_at_1
+      value: 68.497
+    - type: mrr_at_10
+      value: 78.373
+    - type: mrr_at_100
+      value: 78.54299999999999
+    - type: mrr_at_1000
+      value: 78.549
+    - type: mrr_at_3
+      value: 77.03
+    - type: mrr_at_5
+      value: 77.938
+    - type: ndcg_at_1
+      value: 68.497
+    - type: ndcg_at_10
+      value: 79.12599999999999
+    - type: ndcg_at_100
+      value: 80.319
+    - type: ndcg_at_1000
+      value: 80.71199999999999
+    - type: ndcg_at_3
+      value: 76.209
+    - type: ndcg_at_5
+      value: 77.90700000000001
+    - type: precision_at_1
+      value: 68.497
+    - type: precision_at_10
+      value: 9.958
+    - type: precision_at_100
+      value: 1.077
+    - type: precision_at_1000
+      value: 0.11299999999999999
+    - type: precision_at_3
+      value: 29.908
+    - type: precision_at_5
+      value: 18.971
+    - type: recall_at_1
+      value: 63.564
+    - type: recall_at_10
+      value: 90.05199999999999
+    - type: recall_at_100
+      value: 95.028
+    - type: recall_at_1000
+      value: 97.667
+    - type: recall_at_3
+      value: 82.17999999999999
+    - type: recall_at_5
+      value: 86.388
+  - task:
+      type: Retrieval
+    dataset:
+      type: fiqa
+      name: MTEB FiQA2018
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 19.042
+    - type: map_at_10
+      value: 30.764999999999997
+    - type: map_at_100
+      value: 32.678000000000004
+    - type: map_at_1000
+      value: 32.881
+    - type: map_at_3
+      value: 26.525
+    - type: map_at_5
+      value: 28.932000000000002
+    - type: mrr_at_1
+      value: 37.653999999999996
+    - type: mrr_at_10
+      value: 46.597
+    - type: mrr_at_100
+      value: 47.413
+    - type: mrr_at_1000
+      value: 47.453
+    - type: mrr_at_3
+      value: 43.775999999999996
+    - type: mrr_at_5
+      value: 45.489000000000004
+    - type: ndcg_at_1
+      value: 37.653999999999996
+    - type: ndcg_at_10
+      value: 38.615
+    - type: ndcg_at_100
+      value: 45.513999999999996
+    - type: ndcg_at_1000
+      value: 48.815999999999995
+    - type: ndcg_at_3
+      value: 34.427
+    - type: ndcg_at_5
+      value: 35.954
+    - type: precision_at_1
+      value: 37.653999999999996
+    - type: precision_at_10
+      value: 10.864
+    - type: precision_at_100
+      value: 1.7850000000000001
+    - type: precision_at_1000
+      value: 0.23800000000000002
+    - type: precision_at_3
+      value: 22.788
+    - type: precision_at_5
+      value: 17.346
+    - type: recall_at_1
+      value: 19.042
+    - type: recall_at_10
+      value: 45.707
+    - type: recall_at_100
+      value: 71.152
+    - type: recall_at_1000
+      value: 90.7
+    - type: recall_at_3
+      value: 30.814000000000004
+    - type: recall_at_5
+      value: 37.478
+  - task:
+      type: Retrieval
+    dataset:
+      type: hotpotqa
+      name: MTEB HotpotQA
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 38.001000000000005
+    - type: map_at_10
+      value: 59.611000000000004
+    - type: map_at_100
+      value: 60.582
+    - type: map_at_1000
+      value: 60.646
+    - type: map_at_3
+      value: 56.031
+    - type: map_at_5
+      value: 58.243
+    - type: mrr_at_1
+      value: 76.003
+    - type: mrr_at_10
+      value: 82.15400000000001
+    - type: mrr_at_100
+      value: 82.377
+    - type: mrr_at_1000
+      value: 82.383
+    - type: mrr_at_3
+      value: 81.092
+    - type: mrr_at_5
+      value: 81.742
+    - type: ndcg_at_1
+      value: 76.003
+    - type: ndcg_at_10
+      value: 68.216
+    - type: ndcg_at_100
+      value: 71.601
+    - type: ndcg_at_1000
+      value: 72.821
+    - type: ndcg_at_3
+      value: 63.109
+    - type: ndcg_at_5
+      value: 65.902
+    - type: precision_at_1
+      value: 76.003
+    - type: precision_at_10
+      value: 14.379
+    - type: precision_at_100
+      value: 1.702
+    - type: precision_at_1000
+      value: 0.186
+    - type: precision_at_3
+      value: 40.396
+    - type: precision_at_5
+      value: 26.442
+    - type: recall_at_1
+      value: 38.001000000000005
+    - type: recall_at_10
+      value: 71.897
+    - type: recall_at_100
+      value: 85.105
+    - type: recall_at_1000
+      value: 93.133
+    - type: recall_at_3
+      value: 60.594
+    - type: recall_at_5
+      value: 66.104
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/imdb
+      name: MTEB ImdbClassification
+      config: default
+      split: test
+      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
+    metrics:
+    - type: accuracy
+      value: 91.31280000000001
+    - type: ap
+      value: 87.53723467501632
+    - type: f1
+      value: 91.30282906596291
+  - task:
+      type: Retrieval
+    dataset:
+      type: msmarco
+      name: MTEB MSMARCO
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 21.917
+    - type: map_at_10
+      value: 34.117999999999995
+    - type: map_at_100
+      value: 35.283
+    - type: map_at_1000
+      value: 35.333999999999996
+    - type: map_at_3
+      value: 30.330000000000002
+    - type: map_at_5
+      value: 32.461
+    - type: mrr_at_1
+      value: 22.579
+    - type: mrr_at_10
+      value: 34.794000000000004
+    - type: mrr_at_100
+      value: 35.893
+    - type: mrr_at_1000
+      value: 35.937000000000005
+    - type: mrr_at_3
+      value: 31.091
+    - type: mrr_at_5
+      value: 33.173
+    - type: ndcg_at_1
+      value: 22.579
+    - type: ndcg_at_10
+      value: 40.951
+    - type: ndcg_at_100
+      value: 46.558
+    - type: ndcg_at_1000
+      value: 47.803000000000004
+    - type: ndcg_at_3
+      value: 33.262
+    - type: ndcg_at_5
+      value: 37.036
+    - type: precision_at_1
+      value: 22.579
+    - type: precision_at_10
+      value: 6.463000000000001
+    - type: precision_at_100
+      value: 0.928
+    - type: precision_at_1000
+      value: 0.104
+    - type: precision_at_3
+      value: 14.174000000000001
+    - type: precision_at_5
+      value: 10.421
+    - type: recall_at_1
+      value: 21.917
+    - type: recall_at_10
+      value: 61.885
+    - type: recall_at_100
+      value: 87.847
+    - type: recall_at_1000
+      value: 97.322
+    - type: recall_at_3
+      value: 41.010000000000005
+    - type: recall_at_5
+      value: 50.031000000000006
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_domain
+      name: MTEB MTOPDomainClassification (en)
+      config: en
+      split: test
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+    metrics:
+    - type: accuracy
+      value: 93.49521203830369
+    - type: f1
+      value: 93.30882341740241
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_intent
+      name: MTEB MTOPIntentClassification (en)
+      config: en
+      split: test
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+    metrics:
+    - type: accuracy
+      value: 71.0579115367077
+    - type: f1
+      value: 51.2368258319339
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (en)
+      config: en
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 73.88029589778077
+    - type: f1
+      value: 72.34422048584663
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (en)
+      config: en
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 78.2817753866846
+    - type: f1
+      value: 77.87746050004304
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-p2p
+      name: MTEB MedrxivClusteringP2P
+      config: default
+      split: test
+      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
+    metrics:
+    - type: v_measure
+      value: 33.247341454119216
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-s2s
+      name: MTEB MedrxivClusteringS2S
+      config: default
+      split: test
+      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
+    metrics:
+    - type: v_measure
+      value: 31.9647477166234
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/mind_small
+      name: MTEB MindSmallReranking
+      config: default
+      split: test
+      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
+    metrics:
+    - type: map
+      value: 31.90698374676892
+    - type: mrr
+      value: 33.07523683771251
+  - task:
+      type: Retrieval
+    dataset:
+      type: nfcorpus
+      name: MTEB NFCorpus
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 6.717
+    - type: map_at_10
+      value: 14.566
+    - type: map_at_100
+      value: 18.465999999999998
+    - type: map_at_1000
+      value: 20.033
+    - type: map_at_3
+      value: 10.863
+    - type: map_at_5
+      value: 12.589
+    - type: mrr_at_1
+      value: 49.845
+    - type: mrr_at_10
+      value: 58.385
+    - type: mrr_at_100
+      value: 58.989999999999995
+    - type: mrr_at_1000
+      value: 59.028999999999996
+    - type: mrr_at_3
+      value: 56.76
+    - type: mrr_at_5
+      value: 57.766
+    - type: ndcg_at_1
+      value: 47.678
+    - type: ndcg_at_10
+      value: 37.511
+    - type: ndcg_at_100
+      value: 34.537
+    - type: ndcg_at_1000
+      value: 43.612
+    - type: ndcg_at_3
+      value: 43.713
+    - type: ndcg_at_5
+      value: 41.303
+    - type: precision_at_1
+      value: 49.845
+    - type: precision_at_10
+      value: 27.307
+    - type: precision_at_100
+      value: 8.746
+    - type: precision_at_1000
+      value: 2.182
+    - type: precision_at_3
+      value: 40.764
+    - type: precision_at_5
+      value: 35.232
+    - type: recall_at_1
+      value: 6.717
+    - type: recall_at_10
+      value: 18.107
+    - type: recall_at_100
+      value: 33.759
+    - type: recall_at_1000
+      value: 67.31
+    - type: recall_at_3
+      value: 11.68
+    - type: recall_at_5
+      value: 14.557999999999998
+  - task:
+      type: Retrieval
+    dataset:
+      type: nq
+      name: MTEB NQ
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 27.633999999999997
+    - type: map_at_10
+      value: 42.400999999999996
+    - type: map_at_100
+      value: 43.561
+    - type: map_at_1000
+      value: 43.592
+    - type: map_at_3
+      value: 37.865
+    - type: map_at_5
+      value: 40.650999999999996
+    - type: mrr_at_1
+      value: 31.286
+    - type: mrr_at_10
+      value: 44.996
+    - type: mrr_at_100
+      value: 45.889
+    - type: mrr_at_1000
+      value: 45.911
+    - type: mrr_at_3
+      value: 41.126000000000005
+    - type: mrr_at_5
+      value: 43.536
+    - type: ndcg_at_1
+      value: 31.257
+    - type: ndcg_at_10
+      value: 50.197
+    - type: ndcg_at_100
+      value: 55.062
+    - type: ndcg_at_1000
+      value: 55.81700000000001
+    - type: ndcg_at_3
+      value: 41.650999999999996
+    - type: ndcg_at_5
+      value: 46.324
+    - type: precision_at_1
+      value: 31.257
+    - type: precision_at_10
+      value: 8.508000000000001
+    - type: precision_at_100
+      value: 1.121
+    - type: precision_at_1000
+      value: 0.11900000000000001
+    - type: precision_at_3
+      value: 19.1
+    - type: precision_at_5
+      value: 14.16
+    - type: recall_at_1
+      value: 27.633999999999997
+    - type: recall_at_10
+      value: 71.40100000000001
+    - type: recall_at_100
+      value: 92.463
+    - type: recall_at_1000
+      value: 98.13199999999999
+    - type: recall_at_3
+      value: 49.382
+    - type: recall_at_5
+      value: 60.144
+  - task:
+      type: Retrieval
+    dataset:
+      type: quora
+      name: MTEB QuoraRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 71.17099999999999
+    - type: map_at_10
+      value: 85.036
+    - type: map_at_100
+      value: 85.67099999999999
+    - type: map_at_1000
+      value: 85.68599999999999
+    - type: map_at_3
+      value: 82.086
+    - type: map_at_5
+      value: 83.956
+    - type: mrr_at_1
+      value: 82.04
+    - type: mrr_at_10
+      value: 88.018
+    - type: mrr_at_100
+      value: 88.114
+    - type: mrr_at_1000
+      value: 88.115
+    - type: mrr_at_3
+      value: 87.047
+    - type: mrr_at_5
+      value: 87.73100000000001
+    - type: ndcg_at_1
+      value: 82.03
+    - type: ndcg_at_10
+      value: 88.717
+    - type: ndcg_at_100
+      value: 89.904
+    - type: ndcg_at_1000
+      value: 89.991
+    - type: ndcg_at_3
+      value: 85.89099999999999
+    - type: ndcg_at_5
+      value: 87.485
+    - type: precision_at_1
+      value: 82.03
+    - type: precision_at_10
+      value: 13.444999999999999
+    - type: precision_at_100
+      value: 1.533
+    - type: precision_at_1000
+      value: 0.157
+    - type: precision_at_3
+      value: 37.537
+    - type: precision_at_5
+      value: 24.692
+    - type: recall_at_1
+      value: 71.17099999999999
+    - type: recall_at_10
+      value: 95.634
+    - type: recall_at_100
+      value: 99.614
+    - type: recall_at_1000
+      value: 99.99
+    - type: recall_at_3
+      value: 87.48
+    - type: recall_at_5
+      value: 91.996
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering
+      name: MTEB RedditClustering
+      config: default
+      split: test
+      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
+    metrics:
+    - type: v_measure
+      value: 55.067219624685315
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering-p2p
+      name: MTEB RedditClusteringP2P
+      config: default
+      split: test
+      revision: 282350215ef01743dc01b456c7f5241fa8937f16
+    metrics:
+    - type: v_measure
+      value: 62.121822992300444
+  - task:
+      type: Retrieval
+    dataset:
+      type: scidocs
+      name: MTEB SCIDOCS
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 4.153
+    - type: map_at_10
+      value: 11.024000000000001
+    - type: map_at_100
+      value: 13.233
+    - type: map_at_1000
+      value: 13.62
+    - type: map_at_3
+      value: 7.779999999999999
+    - type: map_at_5
+      value: 9.529
+    - type: mrr_at_1
+      value: 20.599999999999998
+    - type: mrr_at_10
+      value: 31.361
+    - type: mrr_at_100
+      value: 32.738
+    - type: mrr_at_1000
+      value: 32.792
+    - type: mrr_at_3
+      value: 28.15
+    - type: mrr_at_5
+      value: 30.085
+    - type: ndcg_at_1
+      value: 20.599999999999998
+    - type: ndcg_at_10
+      value: 18.583
+    - type: ndcg_at_100
+      value: 27.590999999999998
+    - type: ndcg_at_1000
+      value: 34.001
+    - type: ndcg_at_3
+      value: 17.455000000000002
+    - type: ndcg_at_5
+      value: 15.588
+    - type: precision_at_1
+      value: 20.599999999999998
+    - type: precision_at_10
+      value: 9.74
+    - type: precision_at_100
+      value: 2.284
+    - type: precision_at_1000
+      value: 0.381
+    - type: precision_at_3
+      value: 16.533
+    - type: precision_at_5
+      value: 14.02
+    - type: recall_at_1
+      value: 4.153
+    - type: recall_at_10
+      value: 19.738
+    - type: recall_at_100
+      value: 46.322
+    - type: recall_at_1000
+      value: 77.378
+    - type: recall_at_3
+      value: 10.048
+    - type: recall_at_5
+      value: 14.233
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sickr-sts
+      name: MTEB SICK-R
+      config: default
+      split: test
+      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
+    metrics:
+    - type: cos_sim_pearson
+      value: 85.07097501003639
+    - type: cos_sim_spearman
+      value: 81.05827848407056
+    - type: euclidean_pearson
+      value: 82.6279003372546
+    - type: euclidean_spearman
+      value: 81.00031515279802
+    - type: manhattan_pearson
+      value: 82.59338284959495
+    - type: manhattan_spearman
+      value: 80.97432711064945
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts12-sts
+      name: MTEB STS12
+      config: default
+      split: test
+      revision: a0d554a64d88156834ff5ae9920b964011b16384
+    metrics:
+    - type: cos_sim_pearson
+      value: 86.28991993621685
+    - type: cos_sim_spearman
+      value: 78.71828082424351
+    - type: euclidean_pearson
+      value: 83.4881331520832
+    - type: euclidean_spearman
+      value: 78.51746826842316
+    - type: manhattan_pearson
+      value: 83.4109223774324
+    - type: manhattan_spearman
+      value: 78.431544382179
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts13-sts
+      name: MTEB STS13
+      config: default
+      split: test
+      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
+    metrics:
+    - type: cos_sim_pearson
+      value: 83.16651661072123
+    - type: cos_sim_spearman
+      value: 84.88094386637867
+    - type: euclidean_pearson
+      value: 84.3547603585416
+    - type: euclidean_spearman
+      value: 84.85148665860193
+    - type: manhattan_pearson
+      value: 84.29648369879266
+    - type: manhattan_spearman
+      value: 84.76074870571124
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts14-sts
+      name: MTEB STS14
+      config: default
+      split: test
+      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
+    metrics:
+    - type: cos_sim_pearson
+      value: 83.40596254292149
+    - type: cos_sim_spearman
+      value: 83.10699573133829
+    - type: euclidean_pearson
+      value: 83.22794776876958
+    - type: euclidean_spearman
+      value: 83.22583316084712
+    - type: manhattan_pearson
+      value: 83.15899233935681
+    - type: manhattan_spearman
+      value: 83.17668293648019
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts15-sts
+      name: MTEB STS15
+      config: default
+      split: test
+      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
+    metrics:
+    - type: cos_sim_pearson
+      value: 87.27977121352563
+    - type: cos_sim_spearman
+      value: 88.73903130248591
+    - type: euclidean_pearson
+      value: 88.30685958438735
+    - type: euclidean_spearman
+      value: 88.79755484280406
+    - type: manhattan_pearson
+      value: 88.30305607758652
+    - type: manhattan_spearman
+      value: 88.80096577072784
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts16-sts
+      name: MTEB STS16
+      config: default
+      split: test
+      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
+    metrics:
+    - type: cos_sim_pearson
+      value: 84.08819031430218
+    - type: cos_sim_spearman
+      value: 86.35414445951125
+    - type: euclidean_pearson
+      value: 85.4683192388315
+    - type: euclidean_spearman
+      value: 86.2079674669473
+    - type: manhattan_pearson
+      value: 85.35835702257341
+    - type: manhattan_spearman
+      value: 86.08483380002187
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts17-crosslingual-sts
+      name: MTEB STS17 (en-en)
+      config: en-en
+      split: test
+      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
+    metrics:
+    - type: cos_sim_pearson
+      value: 87.36149449801478
+    - type: cos_sim_spearman
+      value: 87.7102980757725
+    - type: euclidean_pearson
+      value: 88.16457177837161
+    - type: euclidean_spearman
+      value: 87.6598652482716
+    - type: manhattan_pearson
+      value: 88.23894728971618
+    - type: manhattan_spearman
+      value: 87.74470156709361
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts22-crosslingual-sts
+      name: MTEB STS22 (en)
+      config: en
+      split: test
+      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
+    metrics:
+    - type: cos_sim_pearson
+      value: 64.54023758394433
+    - type: cos_sim_spearman
+      value: 66.28491960187773
+    - type: euclidean_pearson
+      value: 67.0853128483472
+    - type: euclidean_spearman
+      value: 66.10307543766307
+    - type: manhattan_pearson
+      value: 66.7635365592556
+    - type: manhattan_spearman
+      value: 65.76408004780167
+  - task:
+      type: STS
+    dataset:
+      type: mteb/stsbenchmark-sts
+      name: MTEB STSBenchmark
+      config: default
+      split: test
+      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
+    metrics:
+    - type: cos_sim_pearson
+      value: 85.15858398195317
+    - type: cos_sim_spearman
+      value: 87.44850004752102
+    - type: euclidean_pearson
+      value: 86.60737082550408
+    - type: euclidean_spearman
+      value: 87.31591549824242
+    - type: manhattan_pearson
+      value: 86.56187011429977
+    - type: manhattan_spearman
+      value: 87.23854795795319
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/scidocs-reranking
+      name: MTEB SciDocsRR
+      config: default
+      split: test
+      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
+    metrics:
+    - type: map
+      value: 86.66210488769109
+    - type: mrr
+      value: 96.23100664767331
+  - task:
+      type: Retrieval
+    dataset:
+      type: scifact
+      name: MTEB SciFact
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 56.094
+    - type: map_at_10
+      value: 67.486
+    - type: map_at_100
+      value: 67.925
+    - type: map_at_1000
+      value: 67.949
+    - type: map_at_3
+      value: 64.857
+    - type: map_at_5
+      value: 66.31
+    - type: mrr_at_1
+      value: 58.667
+    - type: mrr_at_10
+      value: 68.438
+    - type: mrr_at_100
+      value: 68.733
+    - type: mrr_at_1000
+      value: 68.757
+    - type: mrr_at_3
+      value: 66.389
+    - type: mrr_at_5
+      value: 67.456
+    - type: ndcg_at_1
+      value: 58.667
+    - type: ndcg_at_10
+      value: 72.506
+    - type: ndcg_at_100
+      value: 74.27
+    - type: ndcg_at_1000
+      value: 74.94800000000001
+    - type: ndcg_at_3
+      value: 67.977
+    - type: ndcg_at_5
+      value: 70.028
+    - type: precision_at_1
+      value: 58.667
+    - type: precision_at_10
+      value: 9.767000000000001
+    - type: precision_at_100
+      value: 1.073
+    - type: precision_at_1000
+      value: 0.11299999999999999
+    - type: precision_at_3
+      value: 27.0
+    - type: precision_at_5
+      value: 17.666999999999998
+    - type: recall_at_1
+      value: 56.094
+    - type: recall_at_10
+      value: 86.68900000000001
+    - type: recall_at_100
+      value: 94.333
+    - type: recall_at_1000
+      value: 99.667
+    - type: recall_at_3
+      value: 74.522
+    - type: recall_at_5
+      value: 79.611
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/sprintduplicatequestions-pairclassification
+      name: MTEB SprintDuplicateQuestions
+      config: default
+      split: test
+      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
+    metrics:
+    - type: cos_sim_accuracy
+      value: 99.83069306930693
+    - type: cos_sim_ap
+      value: 95.69184662911199
+    - type: cos_sim_f1
+      value: 91.4027149321267
+    - type: cos_sim_precision
+      value: 91.91102123356926
+    - type: cos_sim_recall
+      value: 90.9
+    - type: dot_accuracy
+      value: 99.69405940594059
+    - type: dot_ap
+      value: 90.21674151456216
+    - type: dot_f1
+      value: 84.4489179667841
+    - type: dot_precision
+      value: 85.00506585612969
+    - type: dot_recall
+      value: 83.89999999999999
+    - type: euclidean_accuracy
+      value: 99.83069306930693
+    - type: euclidean_ap
+      value: 95.67760109671087
+    - type: euclidean_f1
+      value: 91.19754350051177
+    - type: euclidean_precision
+      value: 93.39622641509435
+    - type: euclidean_recall
+      value: 89.1
+    - type: manhattan_accuracy
+      value: 99.83267326732673
+    - type: manhattan_ap
+      value: 95.69771347732625
+    - type: manhattan_f1
+      value: 91.32420091324201
+    - type: manhattan_precision
+      value: 92.68795056642637
+    - type: manhattan_recall
+      value: 90.0
+    - type: max_accuracy
+      value: 99.83267326732673
+    - type: max_ap
+      value: 95.69771347732625
+    - type: max_f1
+      value: 91.4027149321267
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering
+      name: MTEB StackExchangeClustering
+      config: default
+      split: test
+      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
+    metrics:
+    - type: v_measure
+      value: 64.47378332953092
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering-p2p
+      name: MTEB StackExchangeClusteringP2P
+      config: default
+      split: test
+      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
+    metrics:
+    - type: v_measure
+      value: 33.79602531604151
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/stackoverflowdupquestions-reranking
+      name: MTEB StackOverflowDupQuestions
+      config: default
+      split: test
+      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
+    metrics:
+    - type: map
+      value: 53.80707639107175
+    - type: mrr
+      value: 54.64886522790935
+  - task:
+      type: Summarization
+    dataset:
+      type: mteb/summeval
+      name: MTEB SummEval
+      config: default
+      split: test
+      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
+    metrics:
+    - type: cos_sim_pearson
+      value: 30.852448373051395
+    - type: cos_sim_spearman
+      value: 32.51821499493775
+    - type: dot_pearson
+      value: 30.390650062190456
+    - type: dot_spearman
+      value: 30.588836159667636
+  - task:
+      type: Retrieval
+    dataset:
+      type: trec-covid
+      name: MTEB TRECCOVID
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 0.198
+    - type: map_at_10
+      value: 1.51
+    - type: map_at_100
+      value: 8.882
+    - type: map_at_1000
+      value: 22.181
+    - type: map_at_3
+      value: 0.553
+    - type: map_at_5
+      value: 0.843
+    - type: mrr_at_1
+      value: 74.0
+    - type: mrr_at_10
+      value: 84.89999999999999
+    - type: mrr_at_100
+      value: 84.89999999999999
+    - type: mrr_at_1000
+      value: 84.89999999999999
+    - type: mrr_at_3
+      value: 84.0
+    - type: mrr_at_5
+      value: 84.89999999999999
+    - type: ndcg_at_1
+      value: 68.0
+    - type: ndcg_at_10
+      value: 64.792
+    - type: ndcg_at_100
+      value: 51.37199999999999
+    - type: ndcg_at_1000
+      value: 47.392
+    - type: ndcg_at_3
+      value: 68.46900000000001
+    - type: ndcg_at_5
+      value: 67.084
+    - type: precision_at_1
+      value: 74.0
+    - type: precision_at_10
+      value: 69.39999999999999
+    - type: precision_at_100
+      value: 53.080000000000005
+    - type: precision_at_1000
+      value: 21.258
+    - type: precision_at_3
+      value: 76.0
+    - type: precision_at_5
+      value: 73.2
+    - type: recall_at_1
+      value: 0.198
+    - type: recall_at_10
+      value: 1.7950000000000002
+    - type: recall_at_100
+      value: 12.626999999999999
+    - type: recall_at_1000
+      value: 44.84
+    - type: recall_at_3
+      value: 0.611
+    - type: recall_at_5
+      value: 0.959
+  - task:
+      type: Retrieval
+    dataset:
+      type: webis-touche2020
+      name: MTEB Touche2020
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 1.4949999999999999
+    - type: map_at_10
+      value: 8.797
+    - type: map_at_100
+      value: 14.889
+    - type: map_at_1000
+      value: 16.309
+    - type: map_at_3
+      value: 4.389
+    - type: map_at_5
+      value: 6.776
+    - type: mrr_at_1
+      value: 18.367
+    - type: mrr_at_10
+      value: 35.844
+    - type: mrr_at_100
+      value: 37.119
+    - type: mrr_at_1000
+      value: 37.119
+    - type: mrr_at_3
+      value: 30.612000000000002
+    - type: mrr_at_5
+      value: 33.163
+    - type: ndcg_at_1
+      value: 16.326999999999998
+    - type: ndcg_at_10
+      value: 21.9
+    - type: ndcg_at_100
+      value: 34.705000000000005
+    - type: ndcg_at_1000
+      value: 45.709
+    - type: ndcg_at_3
+      value: 22.7
+    - type: ndcg_at_5
+      value: 23.197000000000003
+    - type: precision_at_1
+      value: 18.367
+    - type: precision_at_10
+      value: 21.02
+    - type: precision_at_100
+      value: 7.714
+    - type: precision_at_1000
+      value: 1.504
+    - type: precision_at_3
+      value: 26.531
+    - type: precision_at_5
+      value: 26.122
+    - type: recall_at_1
+      value: 1.4949999999999999
+    - type: recall_at_10
+      value: 15.504000000000001
+    - type: recall_at_100
+      value: 47.978
+    - type: recall_at_1000
+      value: 81.56
+    - type: recall_at_3
+      value: 5.569
+    - type: recall_at_5
+      value: 9.821
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/toxic_conversations_50k
+      name: MTEB ToxicConversationsClassification
+      config: default
+      split: test
+      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
+    metrics:
+    - type: accuracy
+      value: 72.99279999999999
+    - type: ap
+      value: 15.459189680101492
+    - type: f1
+      value: 56.33023271441895
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/tweet_sentiment_extraction
+      name: MTEB TweetSentimentExtractionClassification
+      config: default
+      split: test
+      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
+    metrics:
+    - type: accuracy
+      value: 63.070175438596486
+    - type: f1
+      value: 63.28070758709465
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/twentynewsgroups-clustering
+      name: MTEB TwentyNewsgroupsClustering
+      config: default
+      split: test
+      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
+    metrics:
+    - type: v_measure
+      value: 50.076231309703054
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twittersemeval2015-pairclassification
+      name: MTEB TwitterSemEval2015
+      config: default
+      split: test
+      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
+    metrics:
+    - type: cos_sim_accuracy
+      value: 87.21463908922931
+    - type: cos_sim_ap
+      value: 77.67287017966282
+    - type: cos_sim_f1
+      value: 70.34412955465588
+    - type: cos_sim_precision
+      value: 67.57413709285368
+    - type: cos_sim_recall
+      value: 73.35092348284961
+    - type: dot_accuracy
+      value: 85.04500208618943
+    - type: dot_ap
+      value: 70.4075203869744
+    - type: dot_f1
+      value: 66.18172537008678
+    - type: dot_precision
+      value: 64.08798813643104
+    - type: dot_recall
+      value: 68.41688654353561
+    - type: euclidean_accuracy
+      value: 87.17887584192646
+    - type: euclidean_ap
+      value: 77.5774128274464
+    - type: euclidean_f1
+      value: 70.09307972480777
+    - type: euclidean_precision
+      value: 71.70852884349986
+    - type: euclidean_recall
+      value: 68.54881266490766
+    - type: manhattan_accuracy
+      value: 87.28020504261787
+    - type: manhattan_ap
+      value: 77.57835820297892
+    - type: manhattan_f1
+      value: 70.23063591521131
+    - type: manhattan_precision
+      value: 70.97817299919159
+    - type: manhattan_recall
+      value: 69.49868073878628
+    - type: max_accuracy
+      value: 87.28020504261787
+    - type: max_ap
+      value: 77.67287017966282
+    - type: max_f1
+      value: 70.34412955465588
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twitterurlcorpus-pairclassification
+      name: MTEB TwitterURLCorpus
+      config: default
+      split: test
+      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
+    metrics:
+    - type: cos_sim_accuracy
+      value: 88.96650754841464
+    - type: cos_sim_ap
+      value: 86.00185968965064
+    - type: cos_sim_f1
+      value: 77.95861256351718
+    - type: cos_sim_precision
+      value: 74.70712773465067
+    - type: cos_sim_recall
+      value: 81.50600554357868
+    - type: dot_accuracy
+      value: 87.36950362867233
+    - type: dot_ap
+      value: 82.22071181147555
+    - type: dot_f1
+      value: 74.85680716698488
+    - type: dot_precision
+      value: 71.54688377316114
+    - type: dot_recall
+      value: 78.48783492454572
+    - type: euclidean_accuracy
+      value: 88.99561454573679
+    - type: euclidean_ap
+      value: 86.15882097229648
+    - type: euclidean_f1
+      value: 78.18463125322332
+    - type: euclidean_precision
+      value: 74.95408956067241
+    - type: euclidean_recall
+      value: 81.70619032953496
+    - type: manhattan_accuracy
+      value: 88.96650754841464
+    - type: manhattan_ap
+      value: 86.13133111232099
+    - type: manhattan_f1
+      value: 78.10771470160115
+    - type: manhattan_precision
+      value: 74.05465084184377
+    - type: manhattan_recall
+      value: 82.63012011087157
+    - type: max_accuracy
+      value: 88.99561454573679
+    - type: max_ap
+      value: 86.15882097229648
+    - type: max_f1
+      value: 78.18463125322332
+language:
+- en
 license: mit
 ---
+
+## stella model
+
+**新闻 | News**
+
+**[2023-10-19]** 开源stella-base-en-v2 使用简单，**不需要任何前缀文本**。
+Release stella-base-en-v2. This model **does not need any prefix text**.\
+**[2023-10-12]** 开源stella-base-zh-v2和stella-large-zh-v2, 效果更好且使用简单，**不需要任何前缀文本**。
+Release stella-base-zh-v2 and stella-large-zh-v2. The 2 models have better performance
+and **do not need any prefix text**.\
+**[2023-09-11]** 开源stella-base-zh和stella-large-zh
+
+stella是一个通用的文本编码模型，主要有以下模型：
+
+|     Model Name     | Model Size (GB) | Dimension | Sequence Length | Language | Need instruction for retrieval? |
+|:------------------:|:---------------:|:---------:|:---------------:|:--------:|:-------------------------------:|
+| stella-base-en-v2  |       0.2       |    768    |       512       | English  |               No                |
+| stella-large-zh-v2 |      0.65       |   1024    |      1024       | Chinese  |               No                |
+| stella-base-zh-v2  |       0.2       |    768    |      1024       | Chinese  |               No                |
+|  stella-large-zh   |      0.65       |   1024    |      1024       | Chinese  |               Yes               |
+|   stella-base-zh   |       0.2       |    768    |      1024       | Chinese  |               Yes               |
+
+完整的训练思路和训练过程已记录在[博客](https://zhuanlan.zhihu.com/p/655322183)，欢迎阅读讨论。
+
+**训练数据：**
+
+1. 开源数据(wudao_base_200GB[1]、m3e[2]和simclue[3])，着重挑选了长度大于512的文本
+2. 在通用语料库上使用LLM构造一批(question, paragraph)和(sentence, paragraph)数据
+
+**训练方法：**
+
+1. 对比学习损失函数
+2. 带有难负例的对比学习损失函数(分别基于bm25和vector构造了难负例)
+3. EWC(Elastic Weights Consolidation)[4]
+4. cosent loss[5]
+5. 每一种类型的数据一个迭代器，分别计算loss进行更新
+
+stella-v2在stella模型的基础上，使用了更多的训练数据，同时知识蒸馏等方法去除了前置的instruction(
+比如piccolo的`查询:`, `结果:`, e5的`query:`和`passage:`)。
+
+**初始权重：**\
+stella-base-zh和stella-large-zh分别以piccolo-base-zh[6]和piccolo-large-zh作为基础模型，512-1024的position
+embedding使用层次分解位置编码[7]进行初始化。\
+感谢商汤科技研究院开源的[piccolo系列模型](https://huggingface.co/sensenova)。
+
+stella is a general-purpose text encoder, which mainly includes the following models:
+
+|     Model Name     | Model Size (GB) | Dimension | Sequence Length | Language | Need instruction for retrieval? |
+|:------------------:|:---------------:|:---------:|:---------------:|:--------:|:-------------------------------:|
+| stella-base-en-v2  |       0.2       |    768    |       512       | English  |               No                |
+| stella-large-zh-v2 |      0.65       |   1024    |      1024       | Chinese  |               No                |
+| stella-base-zh-v2  |       0.2       |    768    |      1024       | Chinese  |               No                |
+|  stella-large-zh   |      0.65       |   1024    |      1024       | Chinese  |               Yes               |
+|   stella-base-zh   |       0.2       |    768    |      1024       | Chinese  |               Yes               |
+
+The training data mainly includes:
+
+1. Open-source training data (wudao_base_200GB, m3e, and simclue), with a focus on selecting texts with lengths greater
+   than 512.
+2. A batch of (question, paragraph) and (sentence, paragraph) data constructed on a general corpus using LLM.
+
+The loss functions mainly include:
+
+1. Contrastive learning loss function
+2. Contrastive learning loss function with hard negative examples (based on bm25 and vector hard negatives)
+3. EWC (Elastic Weights Consolidation)
+4. cosent loss
+
+Model weight initialization:\
+stella-base-zh and stella-large-zh use piccolo-base-zh and piccolo-large-zh as the base models, respectively, and the
+512-1024 position embedding uses the initialization strategy of hierarchical decomposed position encoding.
+
+Training strategy:\
+One iterator for each type of data, separately calculating the loss.
+
+Based on stella models, stella-v2 use more training data and remove instruction by Knowledge Distillation.
+
+## Metric
+
+#### C-MTEB leaderboard (Chinese)
+
+|     Model Name     | Model Size (GB) | Dimension | Sequence Length | Average (35) | Classification (9) | Clustering (4) | Pair Classification (2) | Reranking (4) | Retrieval (8) | STS (8) |
+|:------------------:|:---------------:|:---------:|:---------------:|:------------:|:------------------:|:--------------:|:-----------------------:|:-------------:|:-------------:|:-------:|
+| stella-large-zh-v2 |      0.65       |   1024    |      1024       |    65.13     |       69.05        |     49.16      |          82.68          |     66.41     |     70.14     |  58.66  |
+| stella-base-zh-v2  |       0.2       |    768    |      1024       |    64.36     |       68.29        |      49.4      |          79.95          |     66.1      |     70.08     |  56.92  |
+|  stella-large-zh   |      0.65       |   1024    |      1024       |    64.54     |       67.62        |     48.65      |          78.72          |     65.98     |     71.02     |  58.3   |
+|   stella-base-zh   |       0.2       |    768    |      1024       |    64.16     |       67.77        |      48.7      |          76.09          |     66.95     |     71.07     |  56.54  |
+
+#### MTEB leaderboard (English)
+
+|    Model Name     | Model Size (GB) | Dimension | Sequence Length | Average (56) | Classification (12) | Clustering (11) | Pair Classification (3) | Reranking (4) | Retrieval (15) | STS (10) | Summarization  (1) |
+|:-----------------:|:---------------:|:---------:|:---------------:|:------------:|:-------------------:|:---------------:|:-----------------------:|:-------------:|:--------------:|:--------:|:------------------:|
+| stella-base-en-v2 |       0.2       |    768    |       512       |    62.61     |        75.28        |      44.9       |          86.45          |     58.77     |      50.1      |  83.02   |       32.52        |
+
+#### Reproduce our results
+
+**C-MTEB:** 
+
+```python
+import torch
+import numpy as np
+from typing import List
+from mteb import MTEB
+from sentence_transformers import SentenceTransformer
+
+
+class FastTextEncoder():
+    def __init__(self, model_name):
+        self.model = SentenceTransformer(model_name).cuda().half().eval()
+        self.model.max_seq_length = 512
+
+    def encode(
+            self,
+            input_texts: List[str],
+            *args,
+            **kwargs
+    ):
+        new_sens = list(set(input_texts))
+        new_sens.sort(key=lambda x: len(x), reverse=True)
+        vecs = self.model.encode(
+            new_sens, normalize_embeddings=True, convert_to_numpy=True, batch_size=256
+        ).astype(np.float32)
+        sen2arrid = {sen: idx for idx, sen in enumerate(new_sens)}
+        vecs = vecs[[sen2arrid[sen] for sen in input_texts]]
+        torch.cuda.empty_cache()
+        return vecs
+
+
+if __name__ == '__main__':
+    model_name = "infgrad/stella-base-zh-v2"
+    output_folder = "zh_mteb_results/stella-base-zh-v2"
+    task_names = [t.description["name"] for t in MTEB(task_langs=['zh', 'zh-CN']).tasks]
+    model = FastTextEncoder(model_name)
+    for task in task_names:
+        MTEB(tasks=[task], task_langs=['zh', 'zh-CN']).run(model, output_folder=output_folder)
+
+```
+
+**MTEB:**
+
+You can use official script to reproduce our result. [scripts/run_mteb_english.py](https://github.com/embeddings-benchmark/mteb/blob/main/scripts/run_mteb_english.py)
+
+#### Evaluation for long text
+
+经过实际观察发现，C-MTEB的评测数据长度基本都是小于512的，
+更致命的是那些长度大于512的文本，其重点都在前半部分
+这里以CMRC2018的数据为例说明这个问题：
+
+```
+question: 《无双大蛇z》是谁旗下ω-force开发的动作游戏？
+
+passage：《无双大蛇z》是光荣旗下ω-force开发的动作游戏，于2009年3月12日登陆索尼playstation3，并于2009年11月27日推......
+```
+
+passage长度为800多，大于512，但是对于这个question而言只需要前面40个字就足以检索，多的内容对于模型而言是一种噪声，反而降低了效果。\
+简言之，现有数据集的2个问题：\
+1）长度大于512的过少\
+2）即便大于512，对于检索而言也只需要前512的文本内容\
+导致**无法准确评估模型的长文本编码能力。**
+
+为了解决这个问题，搜集了相关开源数据并使用规则进行过滤，最终整理了6份长文本测试集,他们分别是：
+
+- CMRC2018，通用百科
+- CAIL，法律阅读理解
+- DRCD，繁体百科，已转简体
+- Military，军工问答
+- Squad，英文阅读理解，已转中文
+- Multifieldqa_zh，清华的大模型长文本理解能力评测数据[9]
+
+处理规则是选取答案在512长度之后的文本，短的测试数据会欠采样一下，长短文本占比约为1:2，所以模型既得理解短文本也得理解长文本。
+除了Military数据集，我们提供了其他5个测试数据的下载地址：https://drive.google.com/file/d/1WC6EWaCbVgz-vPMDFH4TwAMkLyh5WNcN/view?usp=sharing
+
+评测指标为Recall@5, 结果如下：
+
+|     Dataset     | piccolo-base-zh | piccolo-large-zh | bge-base-zh | bge-large-zh | stella-base-zh | stella-large-zh | 
+|:---------------:|:---------------:|:----------------:|:-----------:|:------------:|:--------------:|:---------------:|
+|    CMRC2018     |      94.34      |      93.82       |    91.56    |    93.12     |     96.08      |      95.56      | 
+|      CAIL       |      28.04      |      33.64       |    31.22    |    33.94     |     34.62      |      37.18      | 
+|      DRCD       |      78.25      |       77.9       |    78.34    |    80.26     |     86.14      |      84.58      | 
+|    Military     |      76.61      |      73.06       |    75.65    |    75.81     |     83.71      |      80.48      | 
+|      Squad      |      91.21      |      86.61       |    87.87    |    90.38     |     93.31      |      91.21      | 
+| Multifieldqa_zh |      81.41      |      83.92       |    83.92    |    83.42     |      79.9      |      80.4       | 
+|   **Average**   |      74.98      |      74.83       |    74.76    |    76.15     |   **78.96**    |    **78.24**    | 
+
+**注意：** 因为长文本评测数据数量稀少，所以构造时也使用了train部分，如果自行评测，请注意模型的训练数据以免数据泄露。
+
+## Usage
+
+#### stella 中文系列模型
+
+stella-base-zh 和 stella-large-zh: 本模型是在piccolo基础上训练的，因此**用法和piccolo完全一致**
+，即在检索重排任务上给query和passage加上`查询: `和`结果: `。对于短短匹配不需要做任何操作。
+
+stella-base-zh-v2 和 stella-large-zh-v2: 本模型使用简单，**任何使用场景中都不需要加前缀文本**。
+
+stella中文系列模型均使用mean pooling做为文本向量。
+
+在sentence-transformer库中的使用方法：
+
+```python
+from sentence_transformers import SentenceTransformer
+
+sentences = ["数据1", "数据2"]
+model = SentenceTransformer('infgrad/stella-base-zh-v2')
+print(model.max_seq_length)
+embeddings_1 = model.encode(sentences, normalize_embeddings=True)
+embeddings_2 = model.encode(sentences, normalize_embeddings=True)
+similarity = embeddings_1 @ embeddings_2.T
+print(similarity)
+```
+
+直接使用transformers库：
+
+```python
+from transformers import AutoModel, AutoTokenizer
+from sklearn.preprocessing import normalize
+
+model = AutoModel.from_pretrained('infgrad/stella-base-zh-v2')
+tokenizer = AutoTokenizer.from_pretrained('infgrad/stella-base-zh-v2')
+sentences = ["数据1", "数据ABCDEFGH"]
+batch_data = tokenizer(
+    batch_text_or_text_pairs=sentences,
+    padding="longest",
+    return_tensors="pt",
+    max_length=1024,
+    truncation=True,
+)
+attention_mask = batch_data["attention_mask"]
+model_output = model(**batch_data)
+last_hidden = model_output.last_hidden_state.masked_fill(~attention_mask[..., None].bool(), 0.0)
+vectors = last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
+vectors = normalize(vectors, norm="l2", axis=1, )
+print(vectors.shape)  # 2,768
+```
+
+#### stella models for English
+
+**Using Sentence-Transformers:**
+
+```python
+from sentence_transformers import SentenceTransformer
+
+sentences = ["one car come", "one car go"]
+model = SentenceTransformer('infgrad/stella-base-en-v2')
+print(model.max_seq_length)
+embeddings_1 = model.encode(sentences, normalize_embeddings=True)
+embeddings_2 = model.encode(sentences, normalize_embeddings=True)
+similarity = embeddings_1 @ embeddings_2.T
+print(similarity)
+```
+
+**Using HuggingFace Transformers:**
+
+```python
+from transformers import AutoModel, AutoTokenizer
+from sklearn.preprocessing import normalize
+
+model = AutoModel.from_pretrained('infgrad/stella-base-en-v2')
+tokenizer = AutoTokenizer.from_pretrained('infgrad/stella-base-en-v2')
+sentences = ["one car come", "one car go"]
+batch_data = tokenizer(
+    batch_text_or_text_pairs=sentences,
+    padding="longest",
+    return_tensors="pt",
+    max_length=512,
+    truncation=True,
+)
+attention_mask = batch_data["attention_mask"]
+model_output = model(**batch_data)
+last_hidden = model_output.last_hidden_state.masked_fill(~attention_mask[..., None].bool(), 0.0)
+vectors = last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
+vectors = normalize(vectors, norm="l2", axis=1, )
+print(vectors.shape)  # 2,768
+```
+
+## Training Detail
+
+**硬件：** 单卡A100-80GB
+
+**环境：** torch1.13.*; transformers-trainer + deepspeed + gradient-checkpointing
+
+**学习率：** 1e-6
+
+**batch_size：** base模型为1024，额外增加20%的难负例；large模型为768，额外增加20%的难负例
+
+**数据量：** 第一版模型约100万，其中用LLM构造的数据约有200K. LLM模型大小为13b。v2系列模型到了2000万训练数据。
+
+## ToDoList
+
+**评测的稳定性：**
+评测过程中发现Clustering任务会和官方的结果不一致，大约有±0.0x的小差距，原因是聚类代码没有设置random_seed，差距可以忽略不计，不影响评测结论。
+
+**更高质量的长文本训练和测试数据：** 训练数据多是用13b模型构造的，肯定会存在噪声。
+测试数据基本都是从mrc数据整理来的，所以问题都是factoid类型，不符合真实分布。
+
+**OOD的性能：** 虽然近期出现了很多向量编码模型，但是对于不是那么通用的domain，这一众模型包括stella、openai和cohere,
+它们的效果均比不上BM25。
+
+## Reference
+
+1. https://www.scidb.cn/en/detail?dataSetId=c6a3fe684227415a9db8e21bac4a15ab
+2. https://github.com/wangyuxinwhy/uniem
+3. https://github.com/CLUEbenchmark/SimCLUE
+4. https://arxiv.org/abs/1612.00796
+5. https://kexue.fm/archives/8847
+6. https://huggingface.co/sensenova/piccolo-base-zh
+7. https://kexue.fm/archives/7947
+8. https://github.com/FlagOpen/FlagEmbedding
+9. https://github.com/THUDM/LongBench
+
+

config.json

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+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float16",
+  "transformers_version": "4.30.2",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

pytorch_model.bin

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+size 219035693

special_tokens_map.json

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+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

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+{
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}