thenlper-get-large setup

README.md

@@ -1,3 +1,2702 @@
 ---
+tags:
+- mteb
+- sentence-similarity
+- sentence-transformers
+- Sentence Transformers
+model-index:
+- name: gte-large
+  results:
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_counterfactual
+      name: MTEB AmazonCounterfactualClassification (en)
+      config: en
+      split: test
+      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
+    metrics:
+    - type: accuracy
+      value: 72.62686567164178
+    - type: ap
+      value: 34.46944126809772
+    - type: f1
+      value: 66.23684353950857
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_polarity
+      name: MTEB AmazonPolarityClassification
+      config: default
+      split: test
+      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
+    metrics:
+    - type: accuracy
+      value: 92.51805
+    - type: ap
+      value: 89.49842783330848
+    - type: f1
+      value: 92.51112169431808
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_reviews_multi
+      name: MTEB AmazonReviewsClassification (en)
+      config: en
+      split: test
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+    metrics:
+    - type: accuracy
+      value: 49.074
+    - type: f1
+      value: 48.44785682572955
+  - task:
+      type: Retrieval
+    dataset:
+      type: arguana
+      name: MTEB ArguAna
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 32.077
+    - type: map_at_10
+      value: 48.153
+    - type: map_at_100
+      value: 48.963
+    - type: map_at_1000
+      value: 48.966
+    - type: map_at_3
+      value: 43.184
+    - type: map_at_5
+      value: 46.072
+    - type: mrr_at_1
+      value: 33.073
+    - type: mrr_at_10
+      value: 48.54
+    - type: mrr_at_100
+      value: 49.335
+    - type: mrr_at_1000
+      value: 49.338
+    - type: mrr_at_3
+      value: 43.563
+    - type: mrr_at_5
+      value: 46.383
+    - type: ndcg_at_1
+      value: 32.077
+    - type: ndcg_at_10
+      value: 57.158
+    - type: ndcg_at_100
+      value: 60.324999999999996
+    - type: ndcg_at_1000
+      value: 60.402
+    - type: ndcg_at_3
+      value: 46.934
+    - type: ndcg_at_5
+      value: 52.158
+    - type: precision_at_1
+      value: 32.077
+    - type: precision_at_10
+      value: 8.591999999999999
+    - type: precision_at_100
+      value: 0.991
+    - type: precision_at_1000
+      value: 0.1
+    - type: precision_at_3
+      value: 19.275000000000002
+    - type: precision_at_5
+      value: 14.111
+    - type: recall_at_1
+      value: 32.077
+    - type: recall_at_10
+      value: 85.917
+    - type: recall_at_100
+      value: 99.075
+    - type: recall_at_1000
+      value: 99.644
+    - type: recall_at_3
+      value: 57.824
+    - type: recall_at_5
+      value: 70.555
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-p2p
+      name: MTEB ArxivClusteringP2P
+      config: default
+      split: test
+      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
+    metrics:
+    - type: v_measure
+      value: 48.619246083417295
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-s2s
+      name: MTEB ArxivClusteringS2S
+      config: default
+      split: test
+      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
+    metrics:
+    - type: v_measure
+      value: 43.3574067664688
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/askubuntudupquestions-reranking
+      name: MTEB AskUbuntuDupQuestions
+      config: default
+      split: test
+      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
+    metrics:
+    - type: map
+      value: 63.06359661829253
+    - type: mrr
+      value: 76.15596007562766
+  - task:
+      type: STS
+    dataset:
+      type: mteb/biosses-sts
+      name: MTEB BIOSSES
+      config: default
+      split: test
+      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
+    metrics:
+    - type: cos_sim_pearson
+      value: 90.25407547368691
+    - type: cos_sim_spearman
+      value: 88.65081514968477
+    - type: euclidean_pearson
+      value: 88.14857116664494
+    - type: euclidean_spearman
+      value: 88.50683596540692
+    - type: manhattan_pearson
+      value: 87.9654797992225
+    - type: manhattan_spearman
+      value: 88.21164851646908
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/banking77
+      name: MTEB Banking77Classification
+      config: default
+      split: test
+      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
+    metrics:
+    - type: accuracy
+      value: 86.05844155844157
+    - type: f1
+      value: 86.01555597681825
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-p2p
+      name: MTEB BiorxivClusteringP2P
+      config: default
+      split: test
+      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
+    metrics:
+    - type: v_measure
+      value: 39.10510519739522
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-s2s
+      name: MTEB BiorxivClusteringS2S
+      config: default
+      split: test
+      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
+    metrics:
+    - type: v_measure
+      value: 36.84689960264385
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackAndroidRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 32.800000000000004
+    - type: map_at_10
+      value: 44.857
+    - type: map_at_100
+      value: 46.512
+    - type: map_at_1000
+      value: 46.635
+    - type: map_at_3
+      value: 41.062
+    - type: map_at_5
+      value: 43.126
+    - type: mrr_at_1
+      value: 39.628
+    - type: mrr_at_10
+      value: 50.879
+    - type: mrr_at_100
+      value: 51.605000000000004
+    - type: mrr_at_1000
+      value: 51.641000000000005
+    - type: mrr_at_3
+      value: 48.14
+    - type: mrr_at_5
+      value: 49.835
+    - type: ndcg_at_1
+      value: 39.628
+    - type: ndcg_at_10
+      value: 51.819
+    - type: ndcg_at_100
+      value: 57.318999999999996
+    - type: ndcg_at_1000
+      value: 58.955999999999996
+    - type: ndcg_at_3
+      value: 46.409
+    - type: ndcg_at_5
+      value: 48.825
+    - type: precision_at_1
+      value: 39.628
+    - type: precision_at_10
+      value: 10.072000000000001
+    - type: precision_at_100
+      value: 1.625
+    - type: precision_at_1000
+      value: 0.21
+    - type: precision_at_3
+      value: 22.556
+    - type: precision_at_5
+      value: 16.309
+    - type: recall_at_1
+      value: 32.800000000000004
+    - type: recall_at_10
+      value: 65.078
+    - type: recall_at_100
+      value: 87.491
+    - type: recall_at_1000
+      value: 97.514
+    - type: recall_at_3
+      value: 49.561
+    - type: recall_at_5
+      value: 56.135999999999996
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackEnglishRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 32.614
+    - type: map_at_10
+      value: 43.578
+    - type: map_at_100
+      value: 44.897
+    - type: map_at_1000
+      value: 45.023
+    - type: map_at_3
+      value: 40.282000000000004
+    - type: map_at_5
+      value: 42.117
+    - type: mrr_at_1
+      value: 40.510000000000005
+    - type: mrr_at_10
+      value: 49.428
+    - type: mrr_at_100
+      value: 50.068999999999996
+    - type: mrr_at_1000
+      value: 50.111000000000004
+    - type: mrr_at_3
+      value: 47.176
+    - type: mrr_at_5
+      value: 48.583999999999996
+    - type: ndcg_at_1
+      value: 40.510000000000005
+    - type: ndcg_at_10
+      value: 49.478
+    - type: ndcg_at_100
+      value: 53.852
+    - type: ndcg_at_1000
+      value: 55.782
+    - type: ndcg_at_3
+      value: 45.091
+    - type: ndcg_at_5
+      value: 47.19
+    - type: precision_at_1
+      value: 40.510000000000005
+    - type: precision_at_10
+      value: 9.363000000000001
+    - type: precision_at_100
+      value: 1.51
+    - type: precision_at_1000
+      value: 0.196
+    - type: precision_at_3
+      value: 21.741
+    - type: precision_at_5
+      value: 15.465000000000002
+    - type: recall_at_1
+      value: 32.614
+    - type: recall_at_10
+      value: 59.782000000000004
+    - type: recall_at_100
+      value: 78.012
+    - type: recall_at_1000
+      value: 90.319
+    - type: recall_at_3
+      value: 46.825
+    - type: recall_at_5
+      value: 52.688
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGamingRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 40.266000000000005
+    - type: map_at_10
+      value: 53.756
+    - type: map_at_100
+      value: 54.809
+    - type: map_at_1000
+      value: 54.855
+    - type: map_at_3
+      value: 50.073
+    - type: map_at_5
+      value: 52.293
+    - type: mrr_at_1
+      value: 46.332
+    - type: mrr_at_10
+      value: 57.116
+    - type: mrr_at_100
+      value: 57.767
+    - type: mrr_at_1000
+      value: 57.791000000000004
+    - type: mrr_at_3
+      value: 54.461999999999996
+    - type: mrr_at_5
+      value: 56.092
+    - type: ndcg_at_1
+      value: 46.332
+    - type: ndcg_at_10
+      value: 60.092
+    - type: ndcg_at_100
+      value: 64.034
+    - type: ndcg_at_1000
+      value: 64.937
+    - type: ndcg_at_3
+      value: 54.071000000000005
+    - type: ndcg_at_5
+      value: 57.254000000000005
+    - type: precision_at_1
+      value: 46.332
+    - type: precision_at_10
+      value: 9.799
+    - type: precision_at_100
+      value: 1.278
+    - type: precision_at_1000
+      value: 0.13899999999999998
+    - type: precision_at_3
+      value: 24.368000000000002
+    - type: precision_at_5
+      value: 16.89
+    - type: recall_at_1
+      value: 40.266000000000005
+    - type: recall_at_10
+      value: 75.41499999999999
+    - type: recall_at_100
+      value: 92.01700000000001
+    - type: recall_at_1000
+      value: 98.379
+    - type: recall_at_3
+      value: 59.476
+    - type: recall_at_5
+      value: 67.297
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGisRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 28.589
+    - type: map_at_10
+      value: 37.755
+    - type: map_at_100
+      value: 38.881
+    - type: map_at_1000
+      value: 38.954
+    - type: map_at_3
+      value: 34.759
+    - type: map_at_5
+      value: 36.544
+    - type: mrr_at_1
+      value: 30.734
+    - type: mrr_at_10
+      value: 39.742
+    - type: mrr_at_100
+      value: 40.774
+    - type: mrr_at_1000
+      value: 40.824
+    - type: mrr_at_3
+      value: 37.137
+    - type: mrr_at_5
+      value: 38.719
+    - type: ndcg_at_1
+      value: 30.734
+    - type: ndcg_at_10
+      value: 42.978
+    - type: ndcg_at_100
+      value: 48.309000000000005
+    - type: ndcg_at_1000
+      value: 50.068
+    - type: ndcg_at_3
+      value: 37.361
+    - type: ndcg_at_5
+      value: 40.268
+    - type: precision_at_1
+      value: 30.734
+    - type: precision_at_10
+      value: 6.565
+    - type: precision_at_100
+      value: 0.964
+    - type: precision_at_1000
+      value: 0.11499999999999999
+    - type: precision_at_3
+      value: 15.744
+    - type: precision_at_5
+      value: 11.096
+    - type: recall_at_1
+      value: 28.589
+    - type: recall_at_10
+      value: 57.126999999999995
+    - type: recall_at_100
+      value: 81.051
+    - type: recall_at_1000
+      value: 94.027
+    - type: recall_at_3
+      value: 42.045
+    - type: recall_at_5
+      value: 49.019
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackMathematicaRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 18.5
+    - type: map_at_10
+      value: 27.950999999999997
+    - type: map_at_100
+      value: 29.186
+    - type: map_at_1000
+      value: 29.298000000000002
+    - type: map_at_3
+      value: 25.141000000000002
+    - type: map_at_5
+      value: 26.848
+    - type: mrr_at_1
+      value: 22.637
+    - type: mrr_at_10
+      value: 32.572
+    - type: mrr_at_100
+      value: 33.472
+    - type: mrr_at_1000
+      value: 33.533
+    - type: mrr_at_3
+      value: 29.747
+    - type: mrr_at_5
+      value: 31.482
+    - type: ndcg_at_1
+      value: 22.637
+    - type: ndcg_at_10
+      value: 33.73
+    - type: ndcg_at_100
+      value: 39.568
+    - type: ndcg_at_1000
+      value: 42.201
+    - type: ndcg_at_3
+      value: 28.505999999999997
+    - type: ndcg_at_5
+      value: 31.255
+    - type: precision_at_1
+      value: 22.637
+    - type: precision_at_10
+      value: 6.281000000000001
+    - type: precision_at_100
+      value: 1.073
+    - type: precision_at_1000
+      value: 0.14300000000000002
+    - type: precision_at_3
+      value: 13.847000000000001
+    - type: precision_at_5
+      value: 10.224
+    - type: recall_at_1
+      value: 18.5
+    - type: recall_at_10
+      value: 46.744
+    - type: recall_at_100
+      value: 72.072
+    - type: recall_at_1000
+      value: 91.03999999999999
+    - type: recall_at_3
+      value: 32.551
+    - type: recall_at_5
+      value: 39.533
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackPhysicsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 30.602
+    - type: map_at_10
+      value: 42.18
+    - type: map_at_100
+      value: 43.6
+    - type: map_at_1000
+      value: 43.704
+    - type: map_at_3
+      value: 38.413000000000004
+    - type: map_at_5
+      value: 40.626
+    - type: mrr_at_1
+      value: 37.344
+    - type: mrr_at_10
+      value: 47.638000000000005
+    - type: mrr_at_100
+      value: 48.485
+    - type: mrr_at_1000
+      value: 48.52
+    - type: mrr_at_3
+      value: 44.867000000000004
+    - type: mrr_at_5
+      value: 46.566
+    - type: ndcg_at_1
+      value: 37.344
+    - type: ndcg_at_10
+      value: 48.632
+    - type: ndcg_at_100
+      value: 54.215
+    - type: ndcg_at_1000
+      value: 55.981
+    - type: ndcg_at_3
+      value: 42.681999999999995
+    - type: ndcg_at_5
+      value: 45.732
+    - type: precision_at_1
+      value: 37.344
+    - type: precision_at_10
+      value: 8.932
+    - type: precision_at_100
+      value: 1.376
+    - type: precision_at_1000
+      value: 0.17099999999999999
+    - type: precision_at_3
+      value: 20.276
+    - type: precision_at_5
+      value: 14.726
+    - type: recall_at_1
+      value: 30.602
+    - type: recall_at_10
+      value: 62.273
+    - type: recall_at_100
+      value: 85.12100000000001
+    - type: recall_at_1000
+      value: 96.439
+    - type: recall_at_3
+      value: 45.848
+    - type: recall_at_5
+      value: 53.615
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackProgrammersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 23.952
+    - type: map_at_10
+      value: 35.177
+    - type: map_at_100
+      value: 36.59
+    - type: map_at_1000
+      value: 36.703
+    - type: map_at_3
+      value: 31.261
+    - type: map_at_5
+      value: 33.222
+    - type: mrr_at_1
+      value: 29.337999999999997
+    - type: mrr_at_10
+      value: 40.152
+    - type: mrr_at_100
+      value: 40.963
+    - type: mrr_at_1000
+      value: 41.016999999999996
+    - type: mrr_at_3
+      value: 36.91
+    - type: mrr_at_5
+      value: 38.685
+    - type: ndcg_at_1
+      value: 29.337999999999997
+    - type: ndcg_at_10
+      value: 41.994
+    - type: ndcg_at_100
+      value: 47.587
+    - type: ndcg_at_1000
+      value: 49.791000000000004
+    - type: ndcg_at_3
+      value: 35.27
+    - type: ndcg_at_5
+      value: 38.042
+    - type: precision_at_1
+      value: 29.337999999999997
+    - type: precision_at_10
+      value: 8.276
+    - type: precision_at_100
+      value: 1.276
+    - type: precision_at_1000
+      value: 0.164
+    - type: precision_at_3
+      value: 17.161
+    - type: precision_at_5
+      value: 12.671
+    - type: recall_at_1
+      value: 23.952
+    - type: recall_at_10
+      value: 57.267
+    - type: recall_at_100
+      value: 80.886
+    - type: recall_at_1000
+      value: 95.611
+    - type: recall_at_3
+      value: 38.622
+    - type: recall_at_5
+      value: 45.811
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 27.092083333333335
+    - type: map_at_10
+      value: 37.2925
+    - type: map_at_100
+      value: 38.57041666666666
+    - type: map_at_1000
+      value: 38.68141666666667
+    - type: map_at_3
+      value: 34.080000000000005
+    - type: map_at_5
+      value: 35.89958333333333
+    - type: mrr_at_1
+      value: 31.94758333333333
+    - type: mrr_at_10
+      value: 41.51049999999999
+    - type: mrr_at_100
+      value: 42.36099999999999
+    - type: mrr_at_1000
+      value: 42.4125
+    - type: mrr_at_3
+      value: 38.849583333333335
+    - type: mrr_at_5
+      value: 40.448249999999994
+    - type: ndcg_at_1
+      value: 31.94758333333333
+    - type: ndcg_at_10
+      value: 43.17633333333333
+    - type: ndcg_at_100
+      value: 48.45241666666668
+    - type: ndcg_at_1000
+      value: 50.513999999999996
+    - type: ndcg_at_3
+      value: 37.75216666666667
+    - type: ndcg_at_5
+      value: 40.393833333333326
+    - type: precision_at_1
+      value: 31.94758333333333
+    - type: precision_at_10
+      value: 7.688916666666666
+    - type: precision_at_100
+      value: 1.2250833333333333
+    - type: precision_at_1000
+      value: 0.1595
+    - type: precision_at_3
+      value: 17.465999999999998
+    - type: precision_at_5
+      value: 12.548083333333333
+    - type: recall_at_1
+      value: 27.092083333333335
+    - type: recall_at_10
+      value: 56.286583333333326
+    - type: recall_at_100
+      value: 79.09033333333333
+    - type: recall_at_1000
+      value: 93.27483333333335
+    - type: recall_at_3
+      value: 41.35325
+    - type: recall_at_5
+      value: 48.072750000000006
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackStatsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 25.825
+    - type: map_at_10
+      value: 33.723
+    - type: map_at_100
+      value: 34.74
+    - type: map_at_1000
+      value: 34.824
+    - type: map_at_3
+      value: 31.369000000000003
+    - type: map_at_5
+      value: 32.533
+    - type: mrr_at_1
+      value: 29.293999999999997
+    - type: mrr_at_10
+      value: 36.84
+    - type: mrr_at_100
+      value: 37.681
+    - type: mrr_at_1000
+      value: 37.742
+    - type: mrr_at_3
+      value: 34.79
+    - type: mrr_at_5
+      value: 35.872
+    - type: ndcg_at_1
+      value: 29.293999999999997
+    - type: ndcg_at_10
+      value: 38.385999999999996
+    - type: ndcg_at_100
+      value: 43.327
+    - type: ndcg_at_1000
+      value: 45.53
+    - type: ndcg_at_3
+      value: 33.985
+    - type: ndcg_at_5
+      value: 35.817
+    - type: precision_at_1
+      value: 29.293999999999997
+    - type: precision_at_10
+      value: 6.12
+    - type: precision_at_100
+      value: 0.9329999999999999
+    - type: precision_at_1000
+      value: 0.11900000000000001
+    - type: precision_at_3
+      value: 14.621999999999998
+    - type: precision_at_5
+      value: 10.030999999999999
+    - type: recall_at_1
+      value: 25.825
+    - type: recall_at_10
+      value: 49.647000000000006
+    - type: recall_at_100
+      value: 72.32300000000001
+    - type: recall_at_1000
+      value: 88.62400000000001
+    - type: recall_at_3
+      value: 37.366
+    - type: recall_at_5
+      value: 41.957
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackTexRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 18.139
+    - type: map_at_10
+      value: 26.107000000000003
+    - type: map_at_100
+      value: 27.406999999999996
+    - type: map_at_1000
+      value: 27.535999999999998
+    - type: map_at_3
+      value: 23.445
+    - type: map_at_5
+      value: 24.916
+    - type: mrr_at_1
+      value: 21.817
+    - type: mrr_at_10
+      value: 29.99
+    - type: mrr_at_100
+      value: 31.052000000000003
+    - type: mrr_at_1000
+      value: 31.128
+    - type: mrr_at_3
+      value: 27.627000000000002
+    - type: mrr_at_5
+      value: 29.005
+    - type: ndcg_at_1
+      value: 21.817
+    - type: ndcg_at_10
+      value: 31.135
+    - type: ndcg_at_100
+      value: 37.108000000000004
+    - type: ndcg_at_1000
+      value: 39.965
+    - type: ndcg_at_3
+      value: 26.439
+    - type: ndcg_at_5
+      value: 28.655
+    - type: precision_at_1
+      value: 21.817
+    - type: precision_at_10
+      value: 5.757000000000001
+    - type: precision_at_100
+      value: 1.036
+    - type: precision_at_1000
+      value: 0.147
+    - type: precision_at_3
+      value: 12.537
+    - type: precision_at_5
+      value: 9.229
+    - type: recall_at_1
+      value: 18.139
+    - type: recall_at_10
+      value: 42.272999999999996
+    - type: recall_at_100
+      value: 68.657
+    - type: recall_at_1000
+      value: 88.93799999999999
+    - type: recall_at_3
+      value: 29.266
+    - type: recall_at_5
+      value: 34.892
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackUnixRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 27.755000000000003
+    - type: map_at_10
+      value: 37.384
+    - type: map_at_100
+      value: 38.56
+    - type: map_at_1000
+      value: 38.655
+    - type: map_at_3
+      value: 34.214
+    - type: map_at_5
+      value: 35.96
+    - type: mrr_at_1
+      value: 32.369
+    - type: mrr_at_10
+      value: 41.625
+    - type: mrr_at_100
+      value: 42.449
+    - type: mrr_at_1000
+      value: 42.502
+    - type: mrr_at_3
+      value: 38.899
+    - type: mrr_at_5
+      value: 40.489999999999995
+    - type: ndcg_at_1
+      value: 32.369
+    - type: ndcg_at_10
+      value: 43.287
+    - type: ndcg_at_100
+      value: 48.504999999999995
+    - type: ndcg_at_1000
+      value: 50.552
+    - type: ndcg_at_3
+      value: 37.549
+    - type: ndcg_at_5
+      value: 40.204
+    - type: precision_at_1
+      value: 32.369
+    - type: precision_at_10
+      value: 7.425
+    - type: precision_at_100
+      value: 1.134
+    - type: precision_at_1000
+      value: 0.14200000000000002
+    - type: precision_at_3
+      value: 17.102
+    - type: precision_at_5
+      value: 12.107999999999999
+    - type: recall_at_1
+      value: 27.755000000000003
+    - type: recall_at_10
+      value: 57.071000000000005
+    - type: recall_at_100
+      value: 79.456
+    - type: recall_at_1000
+      value: 93.54299999999999
+    - type: recall_at_3
+      value: 41.298
+    - type: recall_at_5
+      value: 48.037
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWebmastersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 24.855
+    - type: map_at_10
+      value: 34.53
+    - type: map_at_100
+      value: 36.167
+    - type: map_at_1000
+      value: 36.394999999999996
+    - type: map_at_3
+      value: 31.037
+    - type: map_at_5
+      value: 33.119
+    - type: mrr_at_1
+      value: 30.631999999999998
+    - type: mrr_at_10
+      value: 39.763999999999996
+    - type: mrr_at_100
+      value: 40.77
+    - type: mrr_at_1000
+      value: 40.826
+    - type: mrr_at_3
+      value: 36.495
+    - type: mrr_at_5
+      value: 38.561
+    - type: ndcg_at_1
+      value: 30.631999999999998
+    - type: ndcg_at_10
+      value: 40.942
+    - type: ndcg_at_100
+      value: 47.07
+    - type: ndcg_at_1000
+      value: 49.363
+    - type: ndcg_at_3
+      value: 35.038000000000004
+    - type: ndcg_at_5
+      value: 38.161
+    - type: precision_at_1
+      value: 30.631999999999998
+    - type: precision_at_10
+      value: 7.983999999999999
+    - type: precision_at_100
+      value: 1.6070000000000002
+    - type: precision_at_1000
+      value: 0.246
+    - type: precision_at_3
+      value: 16.206
+    - type: precision_at_5
+      value: 12.253
+    - type: recall_at_1
+      value: 24.855
+    - type: recall_at_10
+      value: 53.291999999999994
+    - type: recall_at_100
+      value: 80.283
+    - type: recall_at_1000
+      value: 94.309
+    - type: recall_at_3
+      value: 37.257
+    - type: recall_at_5
+      value: 45.282
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWordpressRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 21.208
+    - type: map_at_10
+      value: 30.512
+    - type: map_at_100
+      value: 31.496000000000002
+    - type: map_at_1000
+      value: 31.595000000000002
+    - type: map_at_3
+      value: 27.904
+    - type: map_at_5
+      value: 29.491
+    - type: mrr_at_1
+      value: 22.736
+    - type: mrr_at_10
+      value: 32.379999999999995
+    - type: mrr_at_100
+      value: 33.245000000000005
+    - type: mrr_at_1000
+      value: 33.315
+    - type: mrr_at_3
+      value: 29.945
+    - type: mrr_at_5
+      value: 31.488
+    - type: ndcg_at_1
+      value: 22.736
+    - type: ndcg_at_10
+      value: 35.643
+    - type: ndcg_at_100
+      value: 40.535
+    - type: ndcg_at_1000
+      value: 43.042
+    - type: ndcg_at_3
+      value: 30.625000000000004
+    - type: ndcg_at_5
+      value: 33.323
+    - type: precision_at_1
+      value: 22.736
+    - type: precision_at_10
+      value: 5.6930000000000005
+    - type: precision_at_100
+      value: 0.889
+    - type: precision_at_1000
+      value: 0.122
+    - type: precision_at_3
+      value: 13.431999999999999
+    - type: precision_at_5
+      value: 9.575
+    - type: recall_at_1
+      value: 21.208
+    - type: recall_at_10
+      value: 49.47
+    - type: recall_at_100
+      value: 71.71499999999999
+    - type: recall_at_1000
+      value: 90.55499999999999
+    - type: recall_at_3
+      value: 36.124
+    - type: recall_at_5
+      value: 42.606
+  - task:
+      type: Retrieval
+    dataset:
+      type: climate-fever
+      name: MTEB ClimateFEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 11.363
+    - type: map_at_10
+      value: 20.312
+    - type: map_at_100
+      value: 22.225
+    - type: map_at_1000
+      value: 22.411
+    - type: map_at_3
+      value: 16.68
+    - type: map_at_5
+      value: 18.608
+    - type: mrr_at_1
+      value: 25.537
+    - type: mrr_at_10
+      value: 37.933
+    - type: mrr_at_100
+      value: 38.875
+    - type: mrr_at_1000
+      value: 38.911
+    - type: mrr_at_3
+      value: 34.387
+    - type: mrr_at_5
+      value: 36.51
+    - type: ndcg_at_1
+      value: 25.537
+    - type: ndcg_at_10
+      value: 28.82
+    - type: ndcg_at_100
+      value: 36.341
+    - type: ndcg_at_1000
+      value: 39.615
+    - type: ndcg_at_3
+      value: 23.01
+    - type: ndcg_at_5
+      value: 25.269000000000002
+    - type: precision_at_1
+      value: 25.537
+    - type: precision_at_10
+      value: 9.153
+    - type: precision_at_100
+      value: 1.7319999999999998
+    - type: precision_at_1000
+      value: 0.234
+    - type: precision_at_3
+      value: 17.22
+    - type: precision_at_5
+      value: 13.629
+    - type: recall_at_1
+      value: 11.363
+    - type: recall_at_10
+      value: 35.382999999999996
+    - type: recall_at_100
+      value: 61.367000000000004
+    - type: recall_at_1000
+      value: 79.699
+    - type: recall_at_3
+      value: 21.495
+    - type: recall_at_5
+      value: 27.42
+  - task:
+      type: Retrieval
+    dataset:
+      type: dbpedia-entity
+      name: MTEB DBPedia
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 9.65
+    - type: map_at_10
+      value: 20.742
+    - type: map_at_100
+      value: 29.614
+    - type: map_at_1000
+      value: 31.373
+    - type: map_at_3
+      value: 14.667
+    - type: map_at_5
+      value: 17.186
+    - type: mrr_at_1
+      value: 69.75
+    - type: mrr_at_10
+      value: 76.762
+    - type: mrr_at_100
+      value: 77.171
+    - type: mrr_at_1000
+      value: 77.179
+    - type: mrr_at_3
+      value: 75.125
+    - type: mrr_at_5
+      value: 76.287
+    - type: ndcg_at_1
+      value: 57.62500000000001
+    - type: ndcg_at_10
+      value: 42.370999999999995
+    - type: ndcg_at_100
+      value: 47.897
+    - type: ndcg_at_1000
+      value: 55.393
+    - type: ndcg_at_3
+      value: 46.317
+    - type: ndcg_at_5
+      value: 43.906
+    - type: precision_at_1
+      value: 69.75
+    - type: precision_at_10
+      value: 33.95
+    - type: precision_at_100
+      value: 10.885
+    - type: precision_at_1000
+      value: 2.2239999999999998
+    - type: precision_at_3
+      value: 49.75
+    - type: precision_at_5
+      value: 42.3
+    - type: recall_at_1
+      value: 9.65
+    - type: recall_at_10
+      value: 26.117
+    - type: recall_at_100
+      value: 55.084
+    - type: recall_at_1000
+      value: 78.62400000000001
+    - type: recall_at_3
+      value: 15.823
+    - type: recall_at_5
+      value: 19.652
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/emotion
+      name: MTEB EmotionClassification
+      config: default
+      split: test
+      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
+    metrics:
+    - type: accuracy
+      value: 47.885
+    - type: f1
+      value: 42.99567641346983
+  - task:
+      type: Retrieval
+    dataset:
+      type: fever
+      name: MTEB FEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 70.97
+    - type: map_at_10
+      value: 80.34599999999999
+    - type: map_at_100
+      value: 80.571
+    - type: map_at_1000
+      value: 80.584
+    - type: map_at_3
+      value: 79.279
+    - type: map_at_5
+      value: 79.94
+    - type: mrr_at_1
+      value: 76.613
+    - type: mrr_at_10
+      value: 85.15700000000001
+    - type: mrr_at_100
+      value: 85.249
+    - type: mrr_at_1000
+      value: 85.252
+    - type: mrr_at_3
+      value: 84.33800000000001
+    - type: mrr_at_5
+      value: 84.89
+    - type: ndcg_at_1
+      value: 76.613
+    - type: ndcg_at_10
+      value: 84.53399999999999
+    - type: ndcg_at_100
+      value: 85.359
+    - type: ndcg_at_1000
+      value: 85.607
+    - type: ndcg_at_3
+      value: 82.76599999999999
+    - type: ndcg_at_5
+      value: 83.736
+    - type: precision_at_1
+      value: 76.613
+    - type: precision_at_10
+      value: 10.206
+    - type: precision_at_100
+      value: 1.083
+    - type: precision_at_1000
+      value: 0.11199999999999999
+    - type: precision_at_3
+      value: 31.913000000000004
+    - type: precision_at_5
+      value: 19.769000000000002
+    - type: recall_at_1
+      value: 70.97
+    - type: recall_at_10
+      value: 92.674
+    - type: recall_at_100
+      value: 95.985
+    - type: recall_at_1000
+      value: 97.57000000000001
+    - type: recall_at_3
+      value: 87.742
+    - type: recall_at_5
+      value: 90.28
+  - task:
+      type: Retrieval
+    dataset:
+      type: fiqa
+      name: MTEB FiQA2018
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 22.494
+    - type: map_at_10
+      value: 36.491
+    - type: map_at_100
+      value: 38.550000000000004
+    - type: map_at_1000
+      value: 38.726
+    - type: map_at_3
+      value: 31.807000000000002
+    - type: map_at_5
+      value: 34.299
+    - type: mrr_at_1
+      value: 44.907000000000004
+    - type: mrr_at_10
+      value: 53.146
+    - type: mrr_at_100
+      value: 54.013999999999996
+    - type: mrr_at_1000
+      value: 54.044000000000004
+    - type: mrr_at_3
+      value: 50.952
+    - type: mrr_at_5
+      value: 52.124
+    - type: ndcg_at_1
+      value: 44.907000000000004
+    - type: ndcg_at_10
+      value: 44.499
+    - type: ndcg_at_100
+      value: 51.629000000000005
+    - type: ndcg_at_1000
+      value: 54.367
+    - type: ndcg_at_3
+      value: 40.900999999999996
+    - type: ndcg_at_5
+      value: 41.737
+    - type: precision_at_1
+      value: 44.907000000000004
+    - type: precision_at_10
+      value: 12.346
+    - type: precision_at_100
+      value: 1.974
+    - type: precision_at_1000
+      value: 0.246
+    - type: precision_at_3
+      value: 27.366
+    - type: precision_at_5
+      value: 19.846
+    - type: recall_at_1
+      value: 22.494
+    - type: recall_at_10
+      value: 51.156
+    - type: recall_at_100
+      value: 77.11200000000001
+    - type: recall_at_1000
+      value: 93.44
+    - type: recall_at_3
+      value: 36.574
+    - type: recall_at_5
+      value: 42.361
+  - task:
+      type: Retrieval
+    dataset:
+      type: hotpotqa
+      name: MTEB HotpotQA
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 38.568999999999996
+    - type: map_at_10
+      value: 58.485
+    - type: map_at_100
+      value: 59.358999999999995
+    - type: map_at_1000
+      value: 59.429
+    - type: map_at_3
+      value: 55.217000000000006
+    - type: map_at_5
+      value: 57.236
+    - type: mrr_at_1
+      value: 77.137
+    - type: mrr_at_10
+      value: 82.829
+    - type: mrr_at_100
+      value: 83.04599999999999
+    - type: mrr_at_1000
+      value: 83.05399999999999
+    - type: mrr_at_3
+      value: 81.904
+    - type: mrr_at_5
+      value: 82.50800000000001
+    - type: ndcg_at_1
+      value: 77.137
+    - type: ndcg_at_10
+      value: 67.156
+    - type: ndcg_at_100
+      value: 70.298
+    - type: ndcg_at_1000
+      value: 71.65700000000001
+    - type: ndcg_at_3
+      value: 62.535
+    - type: ndcg_at_5
+      value: 65.095
+    - type: precision_at_1
+      value: 77.137
+    - type: precision_at_10
+      value: 13.911999999999999
+    - type: precision_at_100
+      value: 1.6389999999999998
+    - type: precision_at_1000
+      value: 0.182
+    - type: precision_at_3
+      value: 39.572
+    - type: precision_at_5
+      value: 25.766
+    - type: recall_at_1
+      value: 38.568999999999996
+    - type: recall_at_10
+      value: 69.56099999999999
+    - type: recall_at_100
+      value: 81.931
+    - type: recall_at_1000
+      value: 90.91799999999999
+    - type: recall_at_3
+      value: 59.358999999999995
+    - type: recall_at_5
+      value: 64.416
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/imdb
+      name: MTEB ImdbClassification
+      config: default
+      split: test
+      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
+    metrics:
+    - type: accuracy
+      value: 88.45600000000002
+    - type: ap
+      value: 84.09725115338568
+    - type: f1
+      value: 88.41874909080512
+  - task:
+      type: Retrieval
+    dataset:
+      type: msmarco
+      name: MTEB MSMARCO
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 21.404999999999998
+    - type: map_at_10
+      value: 33.921
+    - type: map_at_100
+      value: 35.116
+    - type: map_at_1000
+      value: 35.164
+    - type: map_at_3
+      value: 30.043999999999997
+    - type: map_at_5
+      value: 32.327
+    - type: mrr_at_1
+      value: 21.977
+    - type: mrr_at_10
+      value: 34.505
+    - type: mrr_at_100
+      value: 35.638999999999996
+    - type: mrr_at_1000
+      value: 35.68
+    - type: mrr_at_3
+      value: 30.703999999999997
+    - type: mrr_at_5
+      value: 32.96
+    - type: ndcg_at_1
+      value: 21.963
+    - type: ndcg_at_10
+      value: 40.859
+    - type: ndcg_at_100
+      value: 46.614
+    - type: ndcg_at_1000
+      value: 47.789
+    - type: ndcg_at_3
+      value: 33.007999999999996
+    - type: ndcg_at_5
+      value: 37.084
+    - type: precision_at_1
+      value: 21.963
+    - type: precision_at_10
+      value: 6.493
+    - type: precision_at_100
+      value: 0.938
+    - type: precision_at_1000
+      value: 0.104
+    - type: precision_at_3
+      value: 14.155000000000001
+    - type: precision_at_5
+      value: 10.544
+    - type: recall_at_1
+      value: 21.404999999999998
+    - type: recall_at_10
+      value: 62.175000000000004
+    - type: recall_at_100
+      value: 88.786
+    - type: recall_at_1000
+      value: 97.738
+    - type: recall_at_3
+      value: 40.925
+    - type: recall_at_5
+      value: 50.722
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_domain
+      name: MTEB MTOPDomainClassification (en)
+      config: en
+      split: test
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+    metrics:
+    - type: accuracy
+      value: 93.50661194710442
+    - type: f1
+      value: 93.30311193153668
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_intent
+      name: MTEB MTOPIntentClassification (en)
+      config: en
+      split: test
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+    metrics:
+    - type: accuracy
+      value: 73.24669402644778
+    - type: f1
+      value: 54.23122108002977
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (en)
+      config: en
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 72.61936785474109
+    - type: f1
+      value: 70.52644941025565
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (en)
+      config: en
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 76.76529926025555
+    - type: f1
+      value: 77.26872729322514
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-p2p
+      name: MTEB MedrxivClusteringP2P
+      config: default
+      split: test
+      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
+    metrics:
+    - type: v_measure
+      value: 33.39450293021839
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-s2s
+      name: MTEB MedrxivClusteringS2S
+      config: default
+      split: test
+      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
+    metrics:
+    - type: v_measure
+      value: 31.757796879839294
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/mind_small
+      name: MTEB MindSmallReranking
+      config: default
+      split: test
+      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
+    metrics:
+    - type: map
+      value: 32.62512146657428
+    - type: mrr
+      value: 33.84624322066173
+  - task:
+      type: Retrieval
+    dataset:
+      type: nfcorpus
+      name: MTEB NFCorpus
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 6.462
+    - type: map_at_10
+      value: 14.947
+    - type: map_at_100
+      value: 19.344
+    - type: map_at_1000
+      value: 20.933
+    - type: map_at_3
+      value: 10.761999999999999
+    - type: map_at_5
+      value: 12.744
+    - type: mrr_at_1
+      value: 47.988
+    - type: mrr_at_10
+      value: 57.365
+    - type: mrr_at_100
+      value: 57.931
+    - type: mrr_at_1000
+      value: 57.96
+    - type: mrr_at_3
+      value: 54.85
+    - type: mrr_at_5
+      value: 56.569
+    - type: ndcg_at_1
+      value: 46.129999999999995
+    - type: ndcg_at_10
+      value: 38.173
+    - type: ndcg_at_100
+      value: 35.983
+    - type: ndcg_at_1000
+      value: 44.507000000000005
+    - type: ndcg_at_3
+      value: 42.495
+    - type: ndcg_at_5
+      value: 41.019
+    - type: precision_at_1
+      value: 47.678
+    - type: precision_at_10
+      value: 28.731
+    - type: precision_at_100
+      value: 9.232
+    - type: precision_at_1000
+      value: 2.202
+    - type: precision_at_3
+      value: 39.628
+    - type: precision_at_5
+      value: 35.851
+    - type: recall_at_1
+      value: 6.462
+    - type: recall_at_10
+      value: 18.968
+    - type: recall_at_100
+      value: 37.131
+    - type: recall_at_1000
+      value: 67.956
+    - type: recall_at_3
+      value: 11.905000000000001
+    - type: recall_at_5
+      value: 15.097
+  - task:
+      type: Retrieval
+    dataset:
+      type: nq
+      name: MTEB NQ
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 30.335
+    - type: map_at_10
+      value: 46.611999999999995
+    - type: map_at_100
+      value: 47.632000000000005
+    - type: map_at_1000
+      value: 47.661
+    - type: map_at_3
+      value: 41.876999999999995
+    - type: map_at_5
+      value: 44.799
+    - type: mrr_at_1
+      value: 34.125
+    - type: mrr_at_10
+      value: 49.01
+    - type: mrr_at_100
+      value: 49.75
+    - type: mrr_at_1000
+      value: 49.768
+    - type: mrr_at_3
+      value: 45.153
+    - type: mrr_at_5
+      value: 47.589999999999996
+    - type: ndcg_at_1
+      value: 34.125
+    - type: ndcg_at_10
+      value: 54.777
+    - type: ndcg_at_100
+      value: 58.914
+    - type: ndcg_at_1000
+      value: 59.521
+    - type: ndcg_at_3
+      value: 46.015
+    - type: ndcg_at_5
+      value: 50.861000000000004
+    - type: precision_at_1
+      value: 34.125
+    - type: precision_at_10
+      value: 9.166
+    - type: precision_at_100
+      value: 1.149
+    - type: precision_at_1000
+      value: 0.121
+    - type: precision_at_3
+      value: 21.147
+    - type: precision_at_5
+      value: 15.469
+    - type: recall_at_1
+      value: 30.335
+    - type: recall_at_10
+      value: 77.194
+    - type: recall_at_100
+      value: 94.812
+    - type: recall_at_1000
+      value: 99.247
+    - type: recall_at_3
+      value: 54.681000000000004
+    - type: recall_at_5
+      value: 65.86800000000001
+  - task:
+      type: Retrieval
+    dataset:
+      type: quora
+      name: MTEB QuoraRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 70.62
+    - type: map_at_10
+      value: 84.536
+    - type: map_at_100
+      value: 85.167
+    - type: map_at_1000
+      value: 85.184
+    - type: map_at_3
+      value: 81.607
+    - type: map_at_5
+      value: 83.423
+    - type: mrr_at_1
+      value: 81.36
+    - type: mrr_at_10
+      value: 87.506
+    - type: mrr_at_100
+      value: 87.601
+    - type: mrr_at_1000
+      value: 87.601
+    - type: mrr_at_3
+      value: 86.503
+    - type: mrr_at_5
+      value: 87.179
+    - type: ndcg_at_1
+      value: 81.36
+    - type: ndcg_at_10
+      value: 88.319
+    - type: ndcg_at_100
+      value: 89.517
+    - type: ndcg_at_1000
+      value: 89.60900000000001
+    - type: ndcg_at_3
+      value: 85.423
+    - type: ndcg_at_5
+      value: 86.976
+    - type: precision_at_1
+      value: 81.36
+    - type: precision_at_10
+      value: 13.415
+    - type: precision_at_100
+      value: 1.529
+    - type: precision_at_1000
+      value: 0.157
+    - type: precision_at_3
+      value: 37.342999999999996
+    - type: precision_at_5
+      value: 24.534
+    - type: recall_at_1
+      value: 70.62
+    - type: recall_at_10
+      value: 95.57600000000001
+    - type: recall_at_100
+      value: 99.624
+    - type: recall_at_1000
+      value: 99.991
+    - type: recall_at_3
+      value: 87.22
+    - type: recall_at_5
+      value: 91.654
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering
+      name: MTEB RedditClustering
+      config: default
+      split: test
+      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
+    metrics:
+    - type: v_measure
+      value: 60.826438478212744
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering-p2p
+      name: MTEB RedditClusteringP2P
+      config: default
+      split: test
+      revision: 282350215ef01743dc01b456c7f5241fa8937f16
+    metrics:
+    - type: v_measure
+      value: 64.24027467551447
+  - task:
+      type: Retrieval
+    dataset:
+      type: scidocs
+      name: MTEB SCIDOCS
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 4.997999999999999
+    - type: map_at_10
+      value: 14.267
+    - type: map_at_100
+      value: 16.843
+    - type: map_at_1000
+      value: 17.229
+    - type: map_at_3
+      value: 9.834
+    - type: map_at_5
+      value: 11.92
+    - type: mrr_at_1
+      value: 24.7
+    - type: mrr_at_10
+      value: 37.685
+    - type: mrr_at_100
+      value: 38.704
+    - type: mrr_at_1000
+      value: 38.747
+    - type: mrr_at_3
+      value: 34.150000000000006
+    - type: mrr_at_5
+      value: 36.075
+    - type: ndcg_at_1
+      value: 24.7
+    - type: ndcg_at_10
+      value: 23.44
+    - type: ndcg_at_100
+      value: 32.617000000000004
+    - type: ndcg_at_1000
+      value: 38.628
+    - type: ndcg_at_3
+      value: 21.747
+    - type: ndcg_at_5
+      value: 19.076
+    - type: precision_at_1
+      value: 24.7
+    - type: precision_at_10
+      value: 12.47
+    - type: precision_at_100
+      value: 2.564
+    - type: precision_at_1000
+      value: 0.4
+    - type: precision_at_3
+      value: 20.767
+    - type: precision_at_5
+      value: 17.06
+    - type: recall_at_1
+      value: 4.997999999999999
+    - type: recall_at_10
+      value: 25.3
+    - type: recall_at_100
+      value: 52.048
+    - type: recall_at_1000
+      value: 81.093
+    - type: recall_at_3
+      value: 12.642999999999999
+    - type: recall_at_5
+      value: 17.312
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sickr-sts
+      name: MTEB SICK-R
+      config: default
+      split: test
+      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
+    metrics:
+    - type: cos_sim_pearson
+      value: 85.44942006292234
+    - type: cos_sim_spearman
+      value: 79.80930790660699
+    - type: euclidean_pearson
+      value: 82.93400777494863
+    - type: euclidean_spearman
+      value: 80.04664991110705
+    - type: manhattan_pearson
+      value: 82.93551681854949
+    - type: manhattan_spearman
+      value: 80.03156736837379
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts12-sts
+      name: MTEB STS12
+      config: default
+      split: test
+      revision: a0d554a64d88156834ff5ae9920b964011b16384
+    metrics:
+    - type: cos_sim_pearson
+      value: 85.63574059135726
+    - type: cos_sim_spearman
+      value: 76.80552915288186
+    - type: euclidean_pearson
+      value: 82.46368529820518
+    - type: euclidean_spearman
+      value: 76.60338474719275
+    - type: manhattan_pearson
+      value: 82.4558617035968
+    - type: manhattan_spearman
+      value: 76.57936082895705
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts13-sts
+      name: MTEB STS13
+      config: default
+      split: test
+      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
+    metrics:
+    - type: cos_sim_pearson
+      value: 86.24116811084211
+    - type: cos_sim_spearman
+      value: 88.10998662068769
+    - type: euclidean_pearson
+      value: 87.04961732352689
+    - type: euclidean_spearman
+      value: 88.12543945864087
+    - type: manhattan_pearson
+      value: 86.9905224528854
+    - type: manhattan_spearman
+      value: 88.07827944705546
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts14-sts
+      name: MTEB STS14
+      config: default
+      split: test
+      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
+    metrics:
+    - type: cos_sim_pearson
+      value: 84.74847296555048
+    - type: cos_sim_spearman
+      value: 82.66200957916445
+    - type: euclidean_pearson
+      value: 84.48132256004965
+    - type: euclidean_spearman
+      value: 82.67915286000596
+    - type: manhattan_pearson
+      value: 84.44950477268334
+    - type: manhattan_spearman
+      value: 82.63327639173352
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts15-sts
+      name: MTEB STS15
+      config: default
+      split: test
+      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
+    metrics:
+    - type: cos_sim_pearson
+      value: 87.23056258027053
+    - type: cos_sim_spearman
+      value: 88.92791680286955
+    - type: euclidean_pearson
+      value: 88.13819235461933
+    - type: euclidean_spearman
+      value: 88.87294661361716
+    - type: manhattan_pearson
+      value: 88.14212133687899
+    - type: manhattan_spearman
+      value: 88.88551854529777
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts16-sts
+      name: MTEB STS16
+      config: default
+      split: test
+      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
+    metrics:
+    - type: cos_sim_pearson
+      value: 82.64179522732887
+    - type: cos_sim_spearman
+      value: 84.25028809903114
+    - type: euclidean_pearson
+      value: 83.40175015236979
+    - type: euclidean_spearman
+      value: 84.23369296429406
+    - type: manhattan_pearson
+      value: 83.43768174261321
+    - type: manhattan_spearman
+      value: 84.27855229214734
+  - 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: 88.20378955494732
+    - type: cos_sim_spearman
+      value: 88.46863559173111
+    - type: euclidean_pearson
+      value: 88.8249295811663
+    - type: euclidean_spearman
+      value: 88.6312737724905
+    - type: manhattan_pearson
+      value: 88.87744466378827
+    - type: manhattan_spearman
+      value: 88.82908423767314
+  - 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: 69.91342028796086
+    - type: cos_sim_spearman
+      value: 69.71495021867864
+    - type: euclidean_pearson
+      value: 70.65334330405646
+    - type: euclidean_spearman
+      value: 69.4321253472211
+    - type: manhattan_pearson
+      value: 70.59743494727465
+    - type: manhattan_spearman
+      value: 69.11695509297482
+  - task:
+      type: STS
+    dataset:
+      type: mteb/stsbenchmark-sts
+      name: MTEB STSBenchmark
+      config: default
+      split: test
+      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
+    metrics:
+    - type: cos_sim_pearson
+      value: 85.42451709766952
+    - type: cos_sim_spearman
+      value: 86.07166710670508
+    - type: euclidean_pearson
+      value: 86.12711421258899
+    - type: euclidean_spearman
+      value: 86.05232086925126
+    - type: manhattan_pearson
+      value: 86.15591089932126
+    - type: manhattan_spearman
+      value: 86.0890128623439
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/scidocs-reranking
+      name: MTEB SciDocsRR
+      config: default
+      split: test
+      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
+    metrics:
+    - type: map
+      value: 87.1976344717285
+    - type: mrr
+      value: 96.3703145075694
+  - task:
+      type: Retrieval
+    dataset:
+      type: scifact
+      name: MTEB SciFact
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 59.511
+    - type: map_at_10
+      value: 69.724
+    - type: map_at_100
+      value: 70.208
+    - type: map_at_1000
+      value: 70.22800000000001
+    - type: map_at_3
+      value: 66.986
+    - type: map_at_5
+      value: 68.529
+    - type: mrr_at_1
+      value: 62.333000000000006
+    - type: mrr_at_10
+      value: 70.55
+    - type: mrr_at_100
+      value: 70.985
+    - type: mrr_at_1000
+      value: 71.004
+    - type: mrr_at_3
+      value: 68.611
+    - type: mrr_at_5
+      value: 69.728
+    - type: ndcg_at_1
+      value: 62.333000000000006
+    - type: ndcg_at_10
+      value: 74.265
+    - type: ndcg_at_100
+      value: 76.361
+    - type: ndcg_at_1000
+      value: 76.82900000000001
+    - type: ndcg_at_3
+      value: 69.772
+    - type: ndcg_at_5
+      value: 71.94800000000001
+    - type: precision_at_1
+      value: 62.333000000000006
+    - type: precision_at_10
+      value: 9.9
+    - type: precision_at_100
+      value: 1.093
+    - type: precision_at_1000
+      value: 0.11299999999999999
+    - type: precision_at_3
+      value: 27.444000000000003
+    - type: precision_at_5
+      value: 18
+    - type: recall_at_1
+      value: 59.511
+    - type: recall_at_10
+      value: 87.156
+    - type: recall_at_100
+      value: 96.5
+    - type: recall_at_1000
+      value: 100
+    - type: recall_at_3
+      value: 75.2
+    - type: recall_at_5
+      value: 80.661
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/sprintduplicatequestions-pairclassification
+      name: MTEB SprintDuplicateQuestions
+      config: default
+      split: test
+      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
+    metrics:
+    - type: cos_sim_accuracy
+      value: 99.81683168316832
+    - type: cos_sim_ap
+      value: 95.74716566563774
+    - type: cos_sim_f1
+      value: 90.64238745574103
+    - type: cos_sim_precision
+      value: 91.7093142272262
+    - type: cos_sim_recall
+      value: 89.60000000000001
+    - type: dot_accuracy
+      value: 99.69405940594059
+    - type: dot_ap
+      value: 91.09013507754594
+    - type: dot_f1
+      value: 84.54227113556779
+    - type: dot_precision
+      value: 84.58458458458459
+    - type: dot_recall
+      value: 84.5
+    - type: euclidean_accuracy
+      value: 99.81782178217821
+    - type: euclidean_ap
+      value: 95.6324301072609
+    - type: euclidean_f1
+      value: 90.58341862845445
+    - type: euclidean_precision
+      value: 92.76729559748428
+    - type: euclidean_recall
+      value: 88.5
+    - type: manhattan_accuracy
+      value: 99.81980198019802
+    - type: manhattan_ap
+      value: 95.68510494437183
+    - type: manhattan_f1
+      value: 90.58945191313342
+    - type: manhattan_precision
+      value: 93.79014989293361
+    - type: manhattan_recall
+      value: 87.6
+    - type: max_accuracy
+      value: 99.81980198019802
+    - type: max_ap
+      value: 95.74716566563774
+    - type: max_f1
+      value: 90.64238745574103
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering
+      name: MTEB StackExchangeClustering
+      config: default
+      split: test
+      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
+    metrics:
+    - type: v_measure
+      value: 67.63761899427078
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering-p2p
+      name: MTEB StackExchangeClusteringP2P
+      config: default
+      split: test
+      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
+    metrics:
+    - type: v_measure
+      value: 36.572473369697235
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/stackoverflowdupquestions-reranking
+      name: MTEB StackOverflowDupQuestions
+      config: default
+      split: test
+      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
+    metrics:
+    - type: map
+      value: 53.63000245208579
+    - type: mrr
+      value: 54.504193722943725
+  - task:
+      type: Summarization
+    dataset:
+      type: mteb/summeval
+      name: MTEB SummEval
+      config: default
+      split: test
+      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
+    metrics:
+    - type: cos_sim_pearson
+      value: 30.300791939416545
+    - type: cos_sim_spearman
+      value: 31.662904057924123
+    - type: dot_pearson
+      value: 26.21198530758316
+    - type: dot_spearman
+      value: 27.006921548904263
+  - task:
+      type: Retrieval
+    dataset:
+      type: trec-covid
+      name: MTEB TRECCOVID
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 0.197
+    - type: map_at_10
+      value: 1.752
+    - type: map_at_100
+      value: 10.795
+    - type: map_at_1000
+      value: 27.18
+    - type: map_at_3
+      value: 0.5890000000000001
+    - type: map_at_5
+      value: 0.938
+    - type: mrr_at_1
+      value: 74
+    - type: mrr_at_10
+      value: 85.833
+    - type: mrr_at_100
+      value: 85.833
+    - type: mrr_at_1000
+      value: 85.833
+    - type: mrr_at_3
+      value: 85.333
+    - type: mrr_at_5
+      value: 85.833
+    - type: ndcg_at_1
+      value: 69
+    - type: ndcg_at_10
+      value: 70.22
+    - type: ndcg_at_100
+      value: 55.785
+    - type: ndcg_at_1000
+      value: 52.93600000000001
+    - type: ndcg_at_3
+      value: 72.084
+    - type: ndcg_at_5
+      value: 71.184
+    - type: precision_at_1
+      value: 74
+    - type: precision_at_10
+      value: 75.2
+    - type: precision_at_100
+      value: 57.3
+    - type: precision_at_1000
+      value: 23.302
+    - type: precision_at_3
+      value: 77.333
+    - type: precision_at_5
+      value: 75.6
+    - type: recall_at_1
+      value: 0.197
+    - type: recall_at_10
+      value: 2.019
+    - type: recall_at_100
+      value: 14.257
+    - type: recall_at_1000
+      value: 50.922
+    - type: recall_at_3
+      value: 0.642
+    - type: recall_at_5
+      value: 1.043
+  - task:
+      type: Retrieval
+    dataset:
+      type: webis-touche2020
+      name: MTEB Touche2020
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 2.803
+    - type: map_at_10
+      value: 10.407
+    - type: map_at_100
+      value: 16.948
+    - type: map_at_1000
+      value: 18.424
+    - type: map_at_3
+      value: 5.405
+    - type: map_at_5
+      value: 6.908
+    - type: mrr_at_1
+      value: 36.735
+    - type: mrr_at_10
+      value: 50.221000000000004
+    - type: mrr_at_100
+      value: 51.388
+    - type: mrr_at_1000
+      value: 51.402
+    - type: mrr_at_3
+      value: 47.278999999999996
+    - type: mrr_at_5
+      value: 49.626
+    - type: ndcg_at_1
+      value: 34.694
+    - type: ndcg_at_10
+      value: 25.507
+    - type: ndcg_at_100
+      value: 38.296
+    - type: ndcg_at_1000
+      value: 49.492000000000004
+    - type: ndcg_at_3
+      value: 29.006999999999998
+    - type: ndcg_at_5
+      value: 25.979000000000003
+    - type: precision_at_1
+      value: 36.735
+    - type: precision_at_10
+      value: 22.041
+    - type: precision_at_100
+      value: 8.02
+    - type: precision_at_1000
+      value: 1.567
+    - type: precision_at_3
+      value: 28.571
+    - type: precision_at_5
+      value: 24.490000000000002
+    - type: recall_at_1
+      value: 2.803
+    - type: recall_at_10
+      value: 16.378
+    - type: recall_at_100
+      value: 50.489
+    - type: recall_at_1000
+      value: 85.013
+    - type: recall_at_3
+      value: 6.505
+    - type: recall_at_5
+      value: 9.243
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/toxic_conversations_50k
+      name: MTEB ToxicConversationsClassification
+      config: default
+      split: test
+      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
+    metrics:
+    - type: accuracy
+      value: 70.55579999999999
+    - type: ap
+      value: 14.206982753316227
+    - type: f1
+      value: 54.372142814964285
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/tweet_sentiment_extraction
+      name: MTEB TweetSentimentExtractionClassification
+      config: default
+      split: test
+      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
+    metrics:
+    - type: accuracy
+      value: 56.57611771363893
+    - type: f1
+      value: 56.924172639063144
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/twentynewsgroups-clustering
+      name: MTEB TwentyNewsgroupsClustering
+      config: default
+      split: test
+      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
+    metrics:
+    - type: v_measure
+      value: 52.82304915719759
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twittersemeval2015-pairclassification
+      name: MTEB TwitterSemEval2015
+      config: default
+      split: test
+      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
+    metrics:
+    - type: cos_sim_accuracy
+      value: 85.92716218632653
+    - type: cos_sim_ap
+      value: 73.73359122546046
+    - type: cos_sim_f1
+      value: 68.42559487116262
+    - type: cos_sim_precision
+      value: 64.22124508215691
+    - type: cos_sim_recall
+      value: 73.21899736147758
+    - type: dot_accuracy
+      value: 80.38981939560112
+    - type: dot_ap
+      value: 54.61060862444974
+    - type: dot_f1
+      value: 53.45710627400769
+    - type: dot_precision
+      value: 44.87638839125761
+    - type: dot_recall
+      value: 66.09498680738787
+    - type: euclidean_accuracy
+      value: 86.02849138701794
+    - type: euclidean_ap
+      value: 73.95673761922404
+    - type: euclidean_f1
+      value: 68.6783042394015
+    - type: euclidean_precision
+      value: 65.1063829787234
+    - type: euclidean_recall
+      value: 72.66490765171504
+    - type: manhattan_accuracy
+      value: 85.9808070572808
+    - type: manhattan_ap
+      value: 73.9050720058029
+    - type: manhattan_f1
+      value: 68.57560618983794
+    - type: manhattan_precision
+      value: 63.70839936608558
+    - type: manhattan_recall
+      value: 74.24802110817942
+    - type: max_accuracy
+      value: 86.02849138701794
+    - type: max_ap
+      value: 73.95673761922404
+    - type: max_f1
+      value: 68.6783042394015
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twitterurlcorpus-pairclassification
+      name: MTEB TwitterURLCorpus
+      config: default
+      split: test
+      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
+    metrics:
+    - type: cos_sim_accuracy
+      value: 88.72783017037295
+    - type: cos_sim_ap
+      value: 85.52705223340233
+    - type: cos_sim_f1
+      value: 77.91659078492079
+    - type: cos_sim_precision
+      value: 73.93378032764221
+    - type: cos_sim_recall
+      value: 82.35294117647058
+    - type: dot_accuracy
+      value: 85.41739434159972
+    - type: dot_ap
+      value: 77.17734818118443
+    - type: dot_f1
+      value: 71.63473589973144
+    - type: dot_precision
+      value: 66.96123719622415
+    - type: dot_recall
+      value: 77.00954727440714
+    - type: euclidean_accuracy
+      value: 88.68125897465751
+    - type: euclidean_ap
+      value: 85.47712213906692
+    - type: euclidean_f1
+      value: 77.81419950830664
+    - type: euclidean_precision
+      value: 75.37162649733006
+    - type: euclidean_recall
+      value: 80.42038805050817
+    - type: manhattan_accuracy
+      value: 88.67349710870494
+    - type: manhattan_ap
+      value: 85.46506475241955
+    - type: manhattan_f1
+      value: 77.87259084890393
+    - type: manhattan_precision
+      value: 74.54929577464789
+    - type: manhattan_recall
+      value: 81.50600554357868
+    - type: max_accuracy
+      value: 88.72783017037295
+    - type: max_ap
+      value: 85.52705223340233
+    - type: max_f1
+      value: 77.91659078492079
+language:
+- en
 license: mit
 ---
+
+# gte-large
+
+General Text Embeddings (GTE) model. [Towards General Text Embeddings with Multi-stage Contrastive Learning](https://arxiv.org/abs/2308.03281)
+
+The GTE models are trained by Alibaba DAMO Academy. They are mainly based on the BERT framework and currently offer three different sizes of models, including [GTE-large](https://huggingface.co/thenlper/gte-large), [GTE-base](https://huggingface.co/thenlper/gte-base), and [GTE-small](https://huggingface.co/thenlper/gte-small). The GTE models are trained on a large-scale corpus of relevance text pairs, covering a wide range of domains and scenarios. This enables the GTE models to be applied to various downstream tasks of text embeddings, including **information retrieval**, **semantic textual similarity**, **text reranking**, etc.
+
+## Metrics
+
+We compared the performance of the GTE models with other popular text embedding models on the MTEB benchmark. For more detailed comparison results, please refer to the [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard).
+
+
+
+| Model Name | Model Size (GB) | Dimension | Sequence Length | Average (56) | Clustering (11) | Pair Classification (3) | Reranking (4) | Retrieval (15) | STS (10) | Summarization (1) | Classification (12) |
+|:----:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
+| [**gte-large**](https://huggingface.co/thenlper/gte-large) | 0.67 | 1024 | 512 | **63.13** | 46.84 | 85.00 | 59.13 | 52.22 | 83.35 | 31.66 | 73.33 |
+| [**gte-base**](https://huggingface.co/thenlper/gte-base) 	| 0.22 | 768 | 512 | **62.39** | 46.2 | 84.57 | 58.61 | 51.14 | 82.3 | 31.17 | 73.01 |
+| [e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) | 1.34 | 1024| 512 | 62.25 | 44.49 | 86.03 | 56.61 | 50.56 | 82.05 | 30.19 | 75.24 |
+| [e5-base-v2](https://huggingface.co/intfloat/e5-base-v2) | 0.44 | 768 | 512 | 61.5 | 43.80 | 85.73 | 55.91 | 50.29 | 81.05 | 30.28 | 73.84 |
+| [**gte-small**](https://huggingface.co/thenlper/gte-small) | 0.07 | 384 | 512 | **61.36** | 44.89 | 83.54 | 57.7 | 49.46 | 82.07 | 30.42 | 72.31 |
+| [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings) | - | 1536 | 8192 | 60.99 | 45.9 | 84.89 | 56.32 | 49.25 | 80.97 | 30.8 | 70.93 |
+| [e5-small-v2](https://huggingface.co/intfloat/e5-base-v2) | 0.13 | 384 | 512 | 59.93 | 39.92 | 84.67 | 54.32 | 49.04 | 80.39 | 31.16 | 72.94 |
+| [sentence-t5-xxl](https://huggingface.co/sentence-transformers/sentence-t5-xxl) | 9.73 | 768 | 512 | 59.51 | 43.72 | 85.06 | 56.42 | 42.24 | 82.63 | 30.08 | 73.42 |
+| [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) 	| 0.44 | 768 | 514 	| 57.78 | 43.69 | 83.04 | 59.36 | 43.81 | 80.28 | 27.49 | 65.07 |
+| [sgpt-bloom-7b1-msmarco](https://huggingface.co/bigscience/sgpt-bloom-7b1-msmarco) 	| 28.27 | 4096 | 2048 | 57.59 | 38.93 | 81.9 | 55.65 | 48.22 | 77.74 | 33.6 | 66.19 |
+| [all-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L12-v2) 	| 0.13 | 384 | 512 	| 56.53 | 41.81 | 82.41 | 58.44 | 42.69 | 79.8 | 27.9 | 63.21 |
+| [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) 	| 0.09 | 384 | 512 	| 56.26 | 42.35 | 82.37 | 58.04 | 41.95 | 78.9 | 30.81 | 63.05 |
+| [contriever-base-msmarco](https://huggingface.co/nthakur/contriever-base-msmarco) 	| 0.44 | 768 | 512 	| 56.00 | 41.1 	| 82.54 | 53.14 | 41.88 | 76.51 | 30.36 | 66.68 |
+| [sentence-t5-base](https://huggingface.co/sentence-transformers/sentence-t5-base) 	| 0.22 | 768 | 512 	| 55.27 | 40.21 | 85.18 | 53.09 | 33.63 | 81.14 | 31.39 | 69.81 |
+
+
+## Usage
+
+Code example
+
+```python
+import torch.nn.functional as F
+from torch import Tensor
+from transformers import AutoTokenizer, AutoModel
+
+def average_pool(last_hidden_states: Tensor,
+                 attention_mask: Tensor) -> Tensor:
+    last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0)
+    return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None]
+
+input_texts = [
+    "what is the capital of China?",
+    "how to implement quick sort in python?",
+    "Beijing",
+    "sorting algorithms"
+]
+
+tokenizer = AutoTokenizer.from_pretrained("thenlper/gte-large")
+model = AutoModel.from_pretrained("thenlper/gte-large")
+
+# Tokenize the input texts
+batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt')
+
+outputs = model(**batch_dict)
+embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
+
+# (Optionally) normalize embeddings
+embeddings = F.normalize(embeddings, p=2, dim=1)
+scores = (embeddings[:1] @ embeddings[1:].T) * 100
+print(scores.tolist())
+```
+
+Use with sentence-transformers:
+```python
+from sentence_transformers import SentenceTransformer
+from sentence_transformers.util import cos_sim
+
+sentences = ['That is a happy person', 'That is a very happy person']
+
+model = SentenceTransformer('thenlper/gte-large')
+embeddings = model.encode(sentences)
+print(cos_sim(embeddings[0], embeddings[1]))
+```
+
+### Limitation
+
+This model exclusively caters to English texts, and any lengthy texts will be truncated to a maximum of 512 tokens.
+
+### Citation
+
+If you find our paper or models helpful, please consider citing them as follows:
+
+```
+@misc{li2023general,
+      title={Towards General Text Embeddings with Multi-stage Contrastive Learning}, 
+      author={Zehan Li and Xin Zhang and Yanzhao Zhang and Dingkun Long and Pengjun Xie and Meishan Zhang},
+      year={2023},
+      eprint={2308.03281},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```

config.json

@@ -0,0 +1,25 @@
+{
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 1024,
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float16",
+  "transformers_version": "4.28.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

modules.json

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+[
+    {
+      "idx": 0,
+      "name": "0",
+      "path": "",
+      "type": "sentence_transformers.models.Transformer"
+    },
+    {
+      "idx": 1,
+      "name": "1",
+      "path": "1_Pooling",
+      "type": "sentence_transformers.models.Pooling"
+    },
+    {
+      "idx": 2,
+      "name": "2",
+      "path": "2_Normalize",
+      "type": "sentence_transformers.models.Normalize"
+    }
+]

pytorch_model.bin

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+version https://git-lfs.github.com/spec/v1
+oid sha256:65a6da76608a92fb42fbee99ffe472357416caf215be2d4cfe4c3fdf6f040f4a
+size 670341183

sentence_bert_config.json

@@ -0,0 +1,4 @@
+{
+    "max_seq_length": 512,
+    "do_lower_case": false
+}

special_tokens_map.json

@@ -0,0 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

@@ -0,0 +1,13 @@
+{
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "mask_token": "[MASK]",
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}