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README.md

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+---
+language:
+- en
+library_name: sentence-transformers
+license: mit
+pipeline_tag: sentence-similarity
+tags:
+  - feature-extraction
+  - mteb
+  - sentence-similarity
+  - sentence-transformers
+
+model-index:
+- name: GIST-small-Embedding-v0
+  results:
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_counterfactual
+      name: MTEB AmazonCounterfactualClassification (en)
+      config: en
+      split: test
+      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
+    metrics:
+    - type: accuracy
+      value: 75.26865671641791
+    - type: ap
+      value: 38.25623793370476
+    - type: f1
+      value: 69.26434651320257
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_polarity
+      name: MTEB AmazonPolarityClassification
+      config: default
+      split: test
+      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
+    metrics:
+    - type: accuracy
+      value: 93.232225
+    - type: ap
+      value: 89.97936072879344
+    - type: f1
+      value: 93.22122653806187
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_reviews_multi
+      name: MTEB AmazonReviewsClassification (en)
+      config: en
+      split: test
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+    metrics:
+    - type: accuracy
+      value: 49.715999999999994
+    - type: f1
+      value: 49.169789920136076
+  - task:
+      type: Retrieval
+    dataset:
+      type: arguana
+      name: MTEB ArguAna
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 34.922
+    - type: map_at_10
+      value: 50.524
+    - type: map_at_100
+      value: 51.247
+    - type: map_at_1000
+      value: 51.249
+    - type: map_at_3
+      value: 45.887
+    - type: map_at_5
+      value: 48.592999999999996
+    - type: mrr_at_1
+      value: 34.922
+    - type: mrr_at_10
+      value: 50.382000000000005
+    - type: mrr_at_100
+      value: 51.104000000000006
+    - type: mrr_at_1000
+      value: 51.105999999999995
+    - type: mrr_at_3
+      value: 45.733000000000004
+    - type: mrr_at_5
+      value: 48.428
+    - type: ndcg_at_1
+      value: 34.922
+    - type: ndcg_at_10
+      value: 59.12
+    - type: ndcg_at_100
+      value: 62.083999999999996
+    - type: ndcg_at_1000
+      value: 62.137
+    - type: ndcg_at_3
+      value: 49.616
+    - type: ndcg_at_5
+      value: 54.501
+    - type: precision_at_1
+      value: 34.922
+    - type: precision_at_10
+      value: 8.649
+    - type: precision_at_100
+      value: 0.991
+    - type: precision_at_1000
+      value: 0.1
+    - type: precision_at_3
+      value: 20.152
+    - type: precision_at_5
+      value: 14.466999999999999
+    - type: recall_at_1
+      value: 34.922
+    - type: recall_at_10
+      value: 86.48599999999999
+    - type: recall_at_100
+      value: 99.14699999999999
+    - type: recall_at_1000
+      value: 99.57300000000001
+    - type: recall_at_3
+      value: 60.455000000000005
+    - type: recall_at_5
+      value: 72.333
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-p2p
+      name: MTEB ArxivClusteringP2P
+      config: default
+      split: test
+      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
+    metrics:
+    - type: v_measure
+      value: 47.623282347623714
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-s2s
+      name: MTEB ArxivClusteringS2S
+      config: default
+      split: test
+      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
+    metrics:
+    - type: v_measure
+      value: 39.86487843524932
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/askubuntudupquestions-reranking
+      name: MTEB AskUbuntuDupQuestions
+      config: default
+      split: test
+      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
+    metrics:
+    - type: map
+      value: 62.3290291318171
+    - type: mrr
+      value: 75.2379853141626
+  - task:
+      type: STS
+    dataset:
+      type: mteb/biosses-sts
+      name: MTEB BIOSSES
+      config: default
+      split: test
+      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
+    metrics:
+    - type: cos_sim_pearson
+      value: 88.52002953574285
+    - type: cos_sim_spearman
+      value: 86.98752423842483
+    - type: euclidean_pearson
+      value: 86.89442688314197
+    - type: euclidean_spearman
+      value: 86.88631711307471
+    - type: manhattan_pearson
+      value: 87.03723618507175
+    - type: manhattan_spearman
+      value: 86.76041062975224
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/banking77
+      name: MTEB Banking77Classification
+      config: default
+      split: test
+      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
+    metrics:
+    - type: accuracy
+      value: 86.64935064935065
+    - type: f1
+      value: 86.61903824934998
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-p2p
+      name: MTEB BiorxivClusteringP2P
+      config: default
+      split: test
+      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
+    metrics:
+    - type: v_measure
+      value: 39.21904455377494
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-s2s
+      name: MTEB BiorxivClusteringS2S
+      config: default
+      split: test
+      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
+    metrics:
+    - type: v_measure
+      value: 35.43342755570654
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackAndroidRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 31.843
+    - type: map_at_10
+      value: 43.379
+    - type: map_at_100
+      value: 44.946999999999996
+    - type: map_at_1000
+      value: 45.078
+    - type: map_at_3
+      value: 39.598
+    - type: map_at_5
+      value: 41.746
+    - type: mrr_at_1
+      value: 39.199
+    - type: mrr_at_10
+      value: 49.672
+    - type: mrr_at_100
+      value: 50.321000000000005
+    - type: mrr_at_1000
+      value: 50.365
+    - type: mrr_at_3
+      value: 46.805
+    - type: mrr_at_5
+      value: 48.579
+    - type: ndcg_at_1
+      value: 39.199
+    - type: ndcg_at_10
+      value: 50.163999999999994
+    - type: ndcg_at_100
+      value: 55.418
+    - type: ndcg_at_1000
+      value: 57.353
+    - type: ndcg_at_3
+      value: 44.716
+    - type: ndcg_at_5
+      value: 47.268
+    - type: precision_at_1
+      value: 39.199
+    - type: precision_at_10
+      value: 9.757
+    - type: precision_at_100
+      value: 1.552
+    - type: precision_at_1000
+      value: 0.20500000000000002
+    - type: precision_at_3
+      value: 21.602
+    - type: precision_at_5
+      value: 15.479000000000001
+    - type: recall_at_1
+      value: 31.843
+    - type: recall_at_10
+      value: 62.743
+    - type: recall_at_100
+      value: 84.78099999999999
+    - type: recall_at_1000
+      value: 96.86099999999999
+    - type: recall_at_3
+      value: 46.927
+    - type: recall_at_5
+      value: 54.355
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackEnglishRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 29.321
+    - type: map_at_10
+      value: 39.062999999999995
+    - type: map_at_100
+      value: 40.403
+    - type: map_at_1000
+      value: 40.534
+    - type: map_at_3
+      value: 36.367
+    - type: map_at_5
+      value: 37.756
+    - type: mrr_at_1
+      value: 35.987
+    - type: mrr_at_10
+      value: 44.708999999999996
+    - type: mrr_at_100
+      value: 45.394
+    - type: mrr_at_1000
+      value: 45.436
+    - type: mrr_at_3
+      value: 42.463
+    - type: mrr_at_5
+      value: 43.663000000000004
+    - type: ndcg_at_1
+      value: 35.987
+    - type: ndcg_at_10
+      value: 44.585
+    - type: ndcg_at_100
+      value: 49.297999999999995
+    - type: ndcg_at_1000
+      value: 51.315
+    - type: ndcg_at_3
+      value: 40.569
+    - type: ndcg_at_5
+      value: 42.197
+    - type: precision_at_1
+      value: 35.987
+    - type: precision_at_10
+      value: 8.369
+    - type: precision_at_100
+      value: 1.366
+    - type: precision_at_1000
+      value: 0.184
+    - type: precision_at_3
+      value: 19.427
+    - type: precision_at_5
+      value: 13.58
+    - type: recall_at_1
+      value: 29.321
+    - type: recall_at_10
+      value: 54.333
+    - type: recall_at_100
+      value: 74.178
+    - type: recall_at_1000
+      value: 86.732
+    - type: recall_at_3
+      value: 42.46
+    - type: recall_at_5
+      value: 47.089999999999996
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGamingRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 38.811
+    - type: map_at_10
+      value: 51.114000000000004
+    - type: map_at_100
+      value: 52.22
+    - type: map_at_1000
+      value: 52.275000000000006
+    - type: map_at_3
+      value: 47.644999999999996
+    - type: map_at_5
+      value: 49.675000000000004
+    - type: mrr_at_1
+      value: 44.389
+    - type: mrr_at_10
+      value: 54.459
+    - type: mrr_at_100
+      value: 55.208999999999996
+    - type: mrr_at_1000
+      value: 55.239000000000004
+    - type: mrr_at_3
+      value: 51.954
+    - type: mrr_at_5
+      value: 53.571999999999996
+    - type: ndcg_at_1
+      value: 44.389
+    - type: ndcg_at_10
+      value: 56.979
+    - type: ndcg_at_100
+      value: 61.266
+    - type: ndcg_at_1000
+      value: 62.315
+    - type: ndcg_at_3
+      value: 51.342
+    - type: ndcg_at_5
+      value: 54.33
+    - type: precision_at_1
+      value: 44.389
+    - type: precision_at_10
+      value: 9.26
+    - type: precision_at_100
+      value: 1.226
+    - type: precision_at_1000
+      value: 0.136
+    - type: precision_at_3
+      value: 22.926
+    - type: precision_at_5
+      value: 15.987000000000002
+    - type: recall_at_1
+      value: 38.811
+    - type: recall_at_10
+      value: 70.841
+    - type: recall_at_100
+      value: 89.218
+    - type: recall_at_1000
+      value: 96.482
+    - type: recall_at_3
+      value: 56.123999999999995
+    - type: recall_at_5
+      value: 63.322
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGisRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 25.378
+    - type: map_at_10
+      value: 34.311
+    - type: map_at_100
+      value: 35.399
+    - type: map_at_1000
+      value: 35.482
+    - type: map_at_3
+      value: 31.917
+    - type: map_at_5
+      value: 33.275
+    - type: mrr_at_1
+      value: 27.683999999999997
+    - type: mrr_at_10
+      value: 36.575
+    - type: mrr_at_100
+      value: 37.492
+    - type: mrr_at_1000
+      value: 37.556
+    - type: mrr_at_3
+      value: 34.35
+    - type: mrr_at_5
+      value: 35.525
+    - type: ndcg_at_1
+      value: 27.683999999999997
+    - type: ndcg_at_10
+      value: 39.247
+    - type: ndcg_at_100
+      value: 44.424
+    - type: ndcg_at_1000
+      value: 46.478
+    - type: ndcg_at_3
+      value: 34.684
+    - type: ndcg_at_5
+      value: 36.886
+    - type: precision_at_1
+      value: 27.683999999999997
+    - type: precision_at_10
+      value: 5.989
+    - type: precision_at_100
+      value: 0.899
+    - type: precision_at_1000
+      value: 0.11199999999999999
+    - type: precision_at_3
+      value: 14.84
+    - type: precision_at_5
+      value: 10.215
+    - type: recall_at_1
+      value: 25.378
+    - type: recall_at_10
+      value: 52.195
+    - type: recall_at_100
+      value: 75.764
+    - type: recall_at_1000
+      value: 91.012
+    - type: recall_at_3
+      value: 39.885999999999996
+    - type: recall_at_5
+      value: 45.279
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackMathematicaRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 17.326
+    - type: map_at_10
+      value: 25.247000000000003
+    - type: map_at_100
+      value: 26.473000000000003
+    - type: map_at_1000
+      value: 26.579000000000004
+    - type: map_at_3
+      value: 22.466
+    - type: map_at_5
+      value: 24.113
+    - type: mrr_at_1
+      value: 21.393
+    - type: mrr_at_10
+      value: 30.187
+    - type: mrr_at_100
+      value: 31.089
+    - type: mrr_at_1000
+      value: 31.15
+    - type: mrr_at_3
+      value: 27.279999999999998
+    - type: mrr_at_5
+      value: 29.127
+    - type: ndcg_at_1
+      value: 21.393
+    - type: ndcg_at_10
+      value: 30.668
+    - type: ndcg_at_100
+      value: 36.543
+    - type: ndcg_at_1000
+      value: 39.181
+    - type: ndcg_at_3
+      value: 25.552000000000003
+    - type: ndcg_at_5
+      value: 28.176000000000002
+    - type: precision_at_1
+      value: 21.393
+    - type: precision_at_10
+      value: 5.784000000000001
+    - type: precision_at_100
+      value: 1.001
+    - type: precision_at_1000
+      value: 0.136
+    - type: precision_at_3
+      value: 12.231
+    - type: precision_at_5
+      value: 9.179
+    - type: recall_at_1
+      value: 17.326
+    - type: recall_at_10
+      value: 42.415000000000006
+    - type: recall_at_100
+      value: 68.605
+    - type: recall_at_1000
+      value: 87.694
+    - type: recall_at_3
+      value: 28.343
+    - type: recall_at_5
+      value: 35.086
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackPhysicsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 29.069
+    - type: map_at_10
+      value: 40.027
+    - type: map_at_100
+      value: 41.308
+    - type: map_at_1000
+      value: 41.412
+    - type: map_at_3
+      value: 36.864000000000004
+    - type: map_at_5
+      value: 38.641999999999996
+    - type: mrr_at_1
+      value: 35.707
+    - type: mrr_at_10
+      value: 45.527
+    - type: mrr_at_100
+      value: 46.348
+    - type: mrr_at_1000
+      value: 46.392
+    - type: mrr_at_3
+      value: 43.086
+    - type: mrr_at_5
+      value: 44.645
+    - type: ndcg_at_1
+      value: 35.707
+    - type: ndcg_at_10
+      value: 46.117000000000004
+    - type: ndcg_at_100
+      value: 51.468
+    - type: ndcg_at_1000
+      value: 53.412000000000006
+    - type: ndcg_at_3
+      value: 41.224
+    - type: ndcg_at_5
+      value: 43.637
+    - type: precision_at_1
+      value: 35.707
+    - type: precision_at_10
+      value: 8.459999999999999
+    - type: precision_at_100
+      value: 1.2970000000000002
+    - type: precision_at_1000
+      value: 0.165
+    - type: precision_at_3
+      value: 19.731
+    - type: precision_at_5
+      value: 14.013
+    - type: recall_at_1
+      value: 29.069
+    - type: recall_at_10
+      value: 58.343999999999994
+    - type: recall_at_100
+      value: 81.296
+    - type: recall_at_1000
+      value: 93.974
+    - type: recall_at_3
+      value: 44.7
+    - type: recall_at_5
+      value: 50.88700000000001
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackProgrammersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 23.905
+    - type: map_at_10
+      value: 33.983000000000004
+    - type: map_at_100
+      value: 35.372
+    - type: map_at_1000
+      value: 35.487
+    - type: map_at_3
+      value: 30.902
+    - type: map_at_5
+      value: 32.505
+    - type: mrr_at_1
+      value: 29.794999999999998
+    - type: mrr_at_10
+      value: 39.28
+    - type: mrr_at_100
+      value: 40.215
+    - type: mrr_at_1000
+      value: 40.276
+    - type: mrr_at_3
+      value: 36.701
+    - type: mrr_at_5
+      value: 38.105
+    - type: ndcg_at_1
+      value: 29.794999999999998
+    - type: ndcg_at_10
+      value: 40.041
+    - type: ndcg_at_100
+      value: 45.884
+    - type: ndcg_at_1000
+      value: 48.271
+    - type: ndcg_at_3
+      value: 34.931
+    - type: ndcg_at_5
+      value: 37.044
+    - type: precision_at_1
+      value: 29.794999999999998
+    - type: precision_at_10
+      value: 7.546
+    - type: precision_at_100
+      value: 1.216
+    - type: precision_at_1000
+      value: 0.158
+    - type: precision_at_3
+      value: 16.933
+    - type: precision_at_5
+      value: 12.1
+    - type: recall_at_1
+      value: 23.905
+    - type: recall_at_10
+      value: 52.945
+    - type: recall_at_100
+      value: 77.551
+    - type: recall_at_1000
+      value: 93.793
+    - type: recall_at_3
+      value: 38.364
+    - type: recall_at_5
+      value: 44.044
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 25.24441666666667
+    - type: map_at_10
+      value: 34.4595
+    - type: map_at_100
+      value: 35.699999999999996
+    - type: map_at_1000
+      value: 35.8155
+    - type: map_at_3
+      value: 31.608333333333338
+    - type: map_at_5
+      value: 33.189416666666666
+    - type: mrr_at_1
+      value: 29.825250000000004
+    - type: mrr_at_10
+      value: 38.60875
+    - type: mrr_at_100
+      value: 39.46575
+    - type: mrr_at_1000
+      value: 39.52458333333333
+    - type: mrr_at_3
+      value: 36.145166666666675
+    - type: mrr_at_5
+      value: 37.57625
+    - type: ndcg_at_1
+      value: 29.825250000000004
+    - type: ndcg_at_10
+      value: 39.88741666666667
+    - type: ndcg_at_100
+      value: 45.17966666666667
+    - type: ndcg_at_1000
+      value: 47.440583333333336
+    - type: ndcg_at_3
+      value: 35.04591666666666
+    - type: ndcg_at_5
+      value: 37.32025
+    - type: precision_at_1
+      value: 29.825250000000004
+    - type: precision_at_10
+      value: 7.07225
+    - type: precision_at_100
+      value: 1.1462499999999998
+    - type: precision_at_1000
+      value: 0.15325
+    - type: precision_at_3
+      value: 16.18375
+    - type: precision_at_5
+      value: 11.526833333333334
+    - type: recall_at_1
+      value: 25.24441666666667
+    - type: recall_at_10
+      value: 51.744916666666676
+    - type: recall_at_100
+      value: 75.04574999999998
+    - type: recall_at_1000
+      value: 90.65558333333334
+    - type: recall_at_3
+      value: 38.28349999999999
+    - type: recall_at_5
+      value: 44.16591666666667
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackStatsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 24.237000000000002
+    - type: map_at_10
+      value: 30.667
+    - type: map_at_100
+      value: 31.592
+    - type: map_at_1000
+      value: 31.688
+    - type: map_at_3
+      value: 28.810999999999996
+    - type: map_at_5
+      value: 29.788999999999998
+    - type: mrr_at_1
+      value: 26.840000000000003
+    - type: mrr_at_10
+      value: 33.305
+    - type: mrr_at_100
+      value: 34.089000000000006
+    - type: mrr_at_1000
+      value: 34.159
+    - type: mrr_at_3
+      value: 31.518
+    - type: mrr_at_5
+      value: 32.469
+    - type: ndcg_at_1
+      value: 26.840000000000003
+    - type: ndcg_at_10
+      value: 34.541
+    - type: ndcg_at_100
+      value: 39.206
+    - type: ndcg_at_1000
+      value: 41.592
+    - type: ndcg_at_3
+      value: 31.005
+    - type: ndcg_at_5
+      value: 32.554
+    - type: precision_at_1
+      value: 26.840000000000003
+    - type: precision_at_10
+      value: 5.3069999999999995
+    - type: precision_at_100
+      value: 0.8340000000000001
+    - type: precision_at_1000
+      value: 0.11199999999999999
+    - type: precision_at_3
+      value: 13.292000000000002
+    - type: precision_at_5
+      value: 9.049
+    - type: recall_at_1
+      value: 24.237000000000002
+    - type: recall_at_10
+      value: 43.862
+    - type: recall_at_100
+      value: 65.352
+    - type: recall_at_1000
+      value: 82.704
+    - type: recall_at_3
+      value: 34.009
+    - type: recall_at_5
+      value: 37.878
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackTexRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 16.482
+    - type: map_at_10
+      value: 23.249
+    - type: map_at_100
+      value: 24.388
+    - type: map_at_1000
+      value: 24.519
+    - type: map_at_3
+      value: 20.971
+    - type: map_at_5
+      value: 22.192
+    - type: mrr_at_1
+      value: 19.993
+    - type: mrr_at_10
+      value: 26.985
+    - type: mrr_at_100
+      value: 27.975
+    - type: mrr_at_1000
+      value: 28.052
+    - type: mrr_at_3
+      value: 24.954
+    - type: mrr_at_5
+      value: 26.070999999999998
+    - type: ndcg_at_1
+      value: 19.993
+    - type: ndcg_at_10
+      value: 27.656
+    - type: ndcg_at_100
+      value: 33.256
+    - type: ndcg_at_1000
+      value: 36.275
+    - type: ndcg_at_3
+      value: 23.644000000000002
+    - type: ndcg_at_5
+      value: 25.466
+    - type: precision_at_1
+      value: 19.993
+    - type: precision_at_10
+      value: 5.093
+    - type: precision_at_100
+      value: 0.932
+    - type: precision_at_1000
+      value: 0.13699999999999998
+    - type: precision_at_3
+      value: 11.149000000000001
+    - type: precision_at_5
+      value: 8.149000000000001
+    - type: recall_at_1
+      value: 16.482
+    - type: recall_at_10
+      value: 37.141999999999996
+    - type: recall_at_100
+      value: 62.696
+    - type: recall_at_1000
+      value: 84.333
+    - type: recall_at_3
+      value: 26.031
+    - type: recall_at_5
+      value: 30.660999999999998
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackUnixRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 24.887999999999998
+    - type: map_at_10
+      value: 34.101
+    - type: map_at_100
+      value: 35.27
+    - type: map_at_1000
+      value: 35.370000000000005
+    - type: map_at_3
+      value: 31.283
+    - type: map_at_5
+      value: 32.72
+    - type: mrr_at_1
+      value: 29.011
+    - type: mrr_at_10
+      value: 38.004
+    - type: mrr_at_100
+      value: 38.879000000000005
+    - type: mrr_at_1000
+      value: 38.938
+    - type: mrr_at_3
+      value: 35.571999999999996
+    - type: mrr_at_5
+      value: 36.789
+    - type: ndcg_at_1
+      value: 29.011
+    - type: ndcg_at_10
+      value: 39.586
+    - type: ndcg_at_100
+      value: 44.939
+    - type: ndcg_at_1000
+      value: 47.236
+    - type: ndcg_at_3
+      value: 34.4
+    - type: ndcg_at_5
+      value: 36.519
+    - type: precision_at_1
+      value: 29.011
+    - type: precision_at_10
+      value: 6.763
+    - type: precision_at_100
+      value: 1.059
+    - type: precision_at_1000
+      value: 0.13699999999999998
+    - type: precision_at_3
+      value: 15.609
+    - type: precision_at_5
+      value: 10.896
+    - type: recall_at_1
+      value: 24.887999999999998
+    - type: recall_at_10
+      value: 52.42
+    - type: recall_at_100
+      value: 75.803
+    - type: recall_at_1000
+      value: 91.725
+    - type: recall_at_3
+      value: 38.080999999999996
+    - type: recall_at_5
+      value: 43.47
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWebmastersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 23.953
+    - type: map_at_10
+      value: 32.649
+    - type: map_at_100
+      value: 34.181
+    - type: map_at_1000
+      value: 34.398
+    - type: map_at_3
+      value: 29.567
+    - type: map_at_5
+      value: 31.263
+    - type: mrr_at_1
+      value: 29.051
+    - type: mrr_at_10
+      value: 37.419999999999995
+    - type: mrr_at_100
+      value: 38.396
+    - type: mrr_at_1000
+      value: 38.458
+    - type: mrr_at_3
+      value: 34.782999999999994
+    - type: mrr_at_5
+      value: 36.254999999999995
+    - type: ndcg_at_1
+      value: 29.051
+    - type: ndcg_at_10
+      value: 38.595
+    - type: ndcg_at_100
+      value: 44.6
+    - type: ndcg_at_1000
+      value: 47.158
+    - type: ndcg_at_3
+      value: 33.56
+    - type: ndcg_at_5
+      value: 35.870000000000005
+    - type: precision_at_1
+      value: 29.051
+    - type: precision_at_10
+      value: 7.53
+    - type: precision_at_100
+      value: 1.538
+    - type: precision_at_1000
+      value: 0.24
+    - type: precision_at_3
+      value: 15.744
+    - type: precision_at_5
+      value: 11.542
+    - type: recall_at_1
+      value: 23.953
+    - type: recall_at_10
+      value: 50.08200000000001
+    - type: recall_at_100
+      value: 77.364
+    - type: recall_at_1000
+      value: 93.57799999999999
+    - type: recall_at_3
+      value: 35.432
+    - type: recall_at_5
+      value: 41.875
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWordpressRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 17.72
+    - type: map_at_10
+      value: 25.724000000000004
+    - type: map_at_100
+      value: 26.846999999999998
+    - type: map_at_1000
+      value: 26.964
+    - type: map_at_3
+      value: 22.909
+    - type: map_at_5
+      value: 24.596999999999998
+    - type: mrr_at_1
+      value: 18.854000000000003
+    - type: mrr_at_10
+      value: 27.182000000000002
+    - type: mrr_at_100
+      value: 28.182000000000002
+    - type: mrr_at_1000
+      value: 28.274
+    - type: mrr_at_3
+      value: 24.276
+    - type: mrr_at_5
+      value: 26.115
+    - type: ndcg_at_1
+      value: 18.854000000000003
+    - type: ndcg_at_10
+      value: 30.470000000000002
+    - type: ndcg_at_100
+      value: 35.854
+    - type: ndcg_at_1000
+      value: 38.701
+    - type: ndcg_at_3
+      value: 24.924
+    - type: ndcg_at_5
+      value: 27.895999999999997
+    - type: precision_at_1
+      value: 18.854000000000003
+    - type: precision_at_10
+      value: 5.009
+    - type: precision_at_100
+      value: 0.835
+    - type: precision_at_1000
+      value: 0.117
+    - type: precision_at_3
+      value: 10.721
+    - type: precision_at_5
+      value: 8.133
+    - type: recall_at_1
+      value: 17.72
+    - type: recall_at_10
+      value: 43.617
+    - type: recall_at_100
+      value: 67.941
+    - type: recall_at_1000
+      value: 88.979
+    - type: recall_at_3
+      value: 29.044999999999998
+    - type: recall_at_5
+      value: 36.044
+  - task:
+      type: Retrieval
+    dataset:
+      type: climate-fever
+      name: MTEB ClimateFEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 13.427
+    - type: map_at_10
+      value: 22.935
+    - type: map_at_100
+      value: 24.808
+    - type: map_at_1000
+      value: 24.994
+    - type: map_at_3
+      value: 19.533
+    - type: map_at_5
+      value: 21.261
+    - type: mrr_at_1
+      value: 30.945
+    - type: mrr_at_10
+      value: 43.242000000000004
+    - type: mrr_at_100
+      value: 44.013999999999996
+    - type: mrr_at_1000
+      value: 44.048
+    - type: mrr_at_3
+      value: 40.109
+    - type: mrr_at_5
+      value: 42.059999999999995
+    - type: ndcg_at_1
+      value: 30.945
+    - type: ndcg_at_10
+      value: 31.828
+    - type: ndcg_at_100
+      value: 38.801
+    - type: ndcg_at_1000
+      value: 42.126999999999995
+    - type: ndcg_at_3
+      value: 26.922
+    - type: ndcg_at_5
+      value: 28.483999999999998
+    - type: precision_at_1
+      value: 30.945
+    - type: precision_at_10
+      value: 9.844
+    - type: precision_at_100
+      value: 1.7309999999999999
+    - type: precision_at_1000
+      value: 0.23500000000000001
+    - type: precision_at_3
+      value: 20.477999999999998
+    - type: precision_at_5
+      value: 15.27
+    - type: recall_at_1
+      value: 13.427
+    - type: recall_at_10
+      value: 37.141000000000005
+    - type: recall_at_100
+      value: 61.007
+    - type: recall_at_1000
+      value: 79.742
+    - type: recall_at_3
+      value: 24.431
+    - type: recall_at_5
+      value: 29.725
+  - task:
+      type: Retrieval
+    dataset:
+      type: dbpedia-entity
+      name: MTEB DBPedia
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 9.122
+    - type: map_at_10
+      value: 18.799
+    - type: map_at_100
+      value: 25.724999999999998
+    - type: map_at_1000
+      value: 27.205000000000002
+    - type: map_at_3
+      value: 14.194999999999999
+    - type: map_at_5
+      value: 16.225
+    - type: mrr_at_1
+      value: 68.0
+    - type: mrr_at_10
+      value: 76.035
+    - type: mrr_at_100
+      value: 76.292
+    - type: mrr_at_1000
+      value: 76.297
+    - type: mrr_at_3
+      value: 74.458
+    - type: mrr_at_5
+      value: 75.558
+    - type: ndcg_at_1
+      value: 56.00000000000001
+    - type: ndcg_at_10
+      value: 39.761
+    - type: ndcg_at_100
+      value: 43.736999999999995
+    - type: ndcg_at_1000
+      value: 51.146
+    - type: ndcg_at_3
+      value: 45.921
+    - type: ndcg_at_5
+      value: 42.756
+    - type: precision_at_1
+      value: 68.0
+    - type: precision_at_10
+      value: 30.275000000000002
+    - type: precision_at_100
+      value: 9.343
+    - type: precision_at_1000
+      value: 1.8270000000000002
+    - type: precision_at_3
+      value: 49.167
+    - type: precision_at_5
+      value: 40.699999999999996
+    - type: recall_at_1
+      value: 9.122
+    - type: recall_at_10
+      value: 23.669999999999998
+    - type: recall_at_100
+      value: 48.719
+    - type: recall_at_1000
+      value: 72.033
+    - type: recall_at_3
+      value: 15.498999999999999
+    - type: recall_at_5
+      value: 18.657
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/emotion
+      name: MTEB EmotionClassification
+      config: default
+      split: test
+      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
+    metrics:
+    - type: accuracy
+      value: 55.885000000000005
+    - type: f1
+      value: 50.70726446938571
+  - task:
+      type: Retrieval
+    dataset:
+      type: fever
+      name: MTEB FEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 75.709
+    - type: map_at_10
+      value: 83.345
+    - type: map_at_100
+      value: 83.557
+    - type: map_at_1000
+      value: 83.572
+    - type: map_at_3
+      value: 82.425
+    - type: map_at_5
+      value: 83.013
+    - type: mrr_at_1
+      value: 81.593
+    - type: mrr_at_10
+      value: 88.331
+    - type: mrr_at_100
+      value: 88.408
+    - type: mrr_at_1000
+      value: 88.41
+    - type: mrr_at_3
+      value: 87.714
+    - type: mrr_at_5
+      value: 88.122
+    - type: ndcg_at_1
+      value: 81.593
+    - type: ndcg_at_10
+      value: 86.925
+    - type: ndcg_at_100
+      value: 87.67
+    - type: ndcg_at_1000
+      value: 87.924
+    - type: ndcg_at_3
+      value: 85.5
+    - type: ndcg_at_5
+      value: 86.283
+    - type: precision_at_1
+      value: 81.593
+    - type: precision_at_10
+      value: 10.264
+    - type: precision_at_100
+      value: 1.084
+    - type: precision_at_1000
+      value: 0.11199999999999999
+    - type: precision_at_3
+      value: 32.388
+    - type: precision_at_5
+      value: 19.991
+    - type: recall_at_1
+      value: 75.709
+    - type: recall_at_10
+      value: 93.107
+    - type: recall_at_100
+      value: 96.024
+    - type: recall_at_1000
+      value: 97.603
+    - type: recall_at_3
+      value: 89.08500000000001
+    - type: recall_at_5
+      value: 91.15299999999999
+  - task:
+      type: Retrieval
+    dataset:
+      type: fiqa
+      name: MTEB FiQA2018
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 19.121
+    - type: map_at_10
+      value: 31.78
+    - type: map_at_100
+      value: 33.497
+    - type: map_at_1000
+      value: 33.696
+    - type: map_at_3
+      value: 27.893
+    - type: map_at_5
+      value: 30.087000000000003
+    - type: mrr_at_1
+      value: 38.272
+    - type: mrr_at_10
+      value: 47.176
+    - type: mrr_at_100
+      value: 48.002
+    - type: mrr_at_1000
+      value: 48.044
+    - type: mrr_at_3
+      value: 45.086999999999996
+    - type: mrr_at_5
+      value: 46.337
+    - type: ndcg_at_1
+      value: 38.272
+    - type: ndcg_at_10
+      value: 39.145
+    - type: ndcg_at_100
+      value: 45.696999999999996
+    - type: ndcg_at_1000
+      value: 49.0
+    - type: ndcg_at_3
+      value: 36.148
+    - type: ndcg_at_5
+      value: 37.023
+    - type: precision_at_1
+      value: 38.272
+    - type: precision_at_10
+      value: 11.065
+    - type: precision_at_100
+      value: 1.7840000000000003
+    - type: precision_at_1000
+      value: 0.23600000000000002
+    - type: precision_at_3
+      value: 24.587999999999997
+    - type: precision_at_5
+      value: 18.056
+    - type: recall_at_1
+      value: 19.121
+    - type: recall_at_10
+      value: 44.857
+    - type: recall_at_100
+      value: 69.774
+    - type: recall_at_1000
+      value: 89.645
+    - type: recall_at_3
+      value: 32.588
+    - type: recall_at_5
+      value: 37.939
+  - task:
+      type: Retrieval
+    dataset:
+      type: hotpotqa
+      name: MTEB HotpotQA
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 36.428
+    - type: map_at_10
+      value: 56.891999999999996
+    - type: map_at_100
+      value: 57.82899999999999
+    - type: map_at_1000
+      value: 57.896
+    - type: map_at_3
+      value: 53.762
+    - type: map_at_5
+      value: 55.718
+    - type: mrr_at_1
+      value: 72.856
+    - type: mrr_at_10
+      value: 79.245
+    - type: mrr_at_100
+      value: 79.515
+    - type: mrr_at_1000
+      value: 79.525
+    - type: mrr_at_3
+      value: 78.143
+    - type: mrr_at_5
+      value: 78.822
+    - type: ndcg_at_1
+      value: 72.856
+    - type: ndcg_at_10
+      value: 65.204
+    - type: ndcg_at_100
+      value: 68.552
+    - type: ndcg_at_1000
+      value: 69.902
+    - type: ndcg_at_3
+      value: 60.632
+    - type: ndcg_at_5
+      value: 63.161
+    - type: precision_at_1
+      value: 72.856
+    - type: precision_at_10
+      value: 13.65
+    - type: precision_at_100
+      value: 1.6260000000000001
+    - type: precision_at_1000
+      value: 0.181
+    - type: precision_at_3
+      value: 38.753
+    - type: precision_at_5
+      value: 25.251
+    - type: recall_at_1
+      value: 36.428
+    - type: recall_at_10
+      value: 68.25099999999999
+    - type: recall_at_100
+      value: 81.317
+    - type: recall_at_1000
+      value: 90.27
+    - type: recall_at_3
+      value: 58.13
+    - type: recall_at_5
+      value: 63.126000000000005
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/imdb
+      name: MTEB ImdbClassification
+      config: default
+      split: test
+      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
+    metrics:
+    - type: accuracy
+      value: 89.4868
+    - type: ap
+      value: 84.88319192880247
+    - type: f1
+      value: 89.46144458052846
+  - task:
+      type: Retrieval
+    dataset:
+      type: msmarco
+      name: MTEB MSMARCO
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 21.282999999999998
+    - type: map_at_10
+      value: 33.045
+    - type: map_at_100
+      value: 34.238
+    - type: map_at_1000
+      value: 34.29
+    - type: map_at_3
+      value: 29.305999999999997
+    - type: map_at_5
+      value: 31.391000000000002
+    - type: mrr_at_1
+      value: 21.92
+    - type: mrr_at_10
+      value: 33.649
+    - type: mrr_at_100
+      value: 34.791
+    - type: mrr_at_1000
+      value: 34.837
+    - type: mrr_at_3
+      value: 30.0
+    - type: mrr_at_5
+      value: 32.039
+    - type: ndcg_at_1
+      value: 21.92
+    - type: ndcg_at_10
+      value: 39.729
+    - type: ndcg_at_100
+      value: 45.484
+    - type: ndcg_at_1000
+      value: 46.817
+    - type: ndcg_at_3
+      value: 32.084
+    - type: ndcg_at_5
+      value: 35.789
+    - type: precision_at_1
+      value: 21.92
+    - type: precision_at_10
+      value: 6.297
+    - type: precision_at_100
+      value: 0.918
+    - type: precision_at_1000
+      value: 0.10300000000000001
+    - type: precision_at_3
+      value: 13.639000000000001
+    - type: precision_at_5
+      value: 10.054
+    - type: recall_at_1
+      value: 21.282999999999998
+    - type: recall_at_10
+      value: 60.343999999999994
+    - type: recall_at_100
+      value: 86.981
+    - type: recall_at_1000
+      value: 97.205
+    - type: recall_at_3
+      value: 39.452999999999996
+    - type: recall_at_5
+      value: 48.333
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_domain
+      name: MTEB MTOPDomainClassification (en)
+      config: en
+      split: test
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+    metrics:
+    - type: accuracy
+      value: 95.47879616963064
+    - type: f1
+      value: 95.21800589958251
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_intent
+      name: MTEB MTOPIntentClassification (en)
+      config: en
+      split: test
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+    metrics:
+    - type: accuracy
+      value: 79.09256725946192
+    - type: f1
+      value: 60.554043889452515
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (en)
+      config: en
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 75.53463349024882
+    - type: f1
+      value: 73.14418495756476
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (en)
+      config: en
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 79.22663080026899
+    - type: f1
+      value: 79.331456217501
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-p2p
+      name: MTEB MedrxivClusteringP2P
+      config: default
+      split: test
+      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
+    metrics:
+    - type: v_measure
+      value: 34.50316010430136
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-s2s
+      name: MTEB MedrxivClusteringS2S
+      config: default
+      split: test
+      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
+    metrics:
+    - type: v_measure
+      value: 32.15612040042282
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/mind_small
+      name: MTEB MindSmallReranking
+      config: default
+      split: test
+      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
+    metrics:
+    - type: map
+      value: 32.36227552557184
+    - type: mrr
+      value: 33.57901344209811
+  - task:
+      type: Retrieval
+    dataset:
+      type: nfcorpus
+      name: MTEB NFCorpus
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 5.6610000000000005
+    - type: map_at_10
+      value: 12.992
+    - type: map_at_100
+      value: 16.756999999999998
+    - type: map_at_1000
+      value: 18.25
+    - type: map_at_3
+      value: 9.471
+    - type: map_at_5
+      value: 11.116
+    - type: mrr_at_1
+      value: 43.653
+    - type: mrr_at_10
+      value: 53.388999999999996
+    - type: mrr_at_100
+      value: 53.982
+    - type: mrr_at_1000
+      value: 54.033
+    - type: mrr_at_3
+      value: 51.858000000000004
+    - type: mrr_at_5
+      value: 53.019000000000005
+    - type: ndcg_at_1
+      value: 41.641
+    - type: ndcg_at_10
+      value: 34.691
+    - type: ndcg_at_100
+      value: 32.305
+    - type: ndcg_at_1000
+      value: 41.132999999999996
+    - type: ndcg_at_3
+      value: 40.614
+    - type: ndcg_at_5
+      value: 38.456
+    - type: precision_at_1
+      value: 43.344
+    - type: precision_at_10
+      value: 25.881999999999998
+    - type: precision_at_100
+      value: 8.483
+    - type: precision_at_1000
+      value: 2.131
+    - type: precision_at_3
+      value: 38.803
+    - type: precision_at_5
+      value: 33.87
+    - type: recall_at_1
+      value: 5.6610000000000005
+    - type: recall_at_10
+      value: 16.826
+    - type: recall_at_100
+      value: 32.939
+    - type: recall_at_1000
+      value: 65.161
+    - type: recall_at_3
+      value: 10.756
+    - type: recall_at_5
+      value: 13.331000000000001
+  - task:
+      type: Retrieval
+    dataset:
+      type: nq
+      name: MTEB NQ
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 26.692
+    - type: map_at_10
+      value: 41.065000000000005
+    - type: map_at_100
+      value: 42.235
+    - type: map_at_1000
+      value: 42.27
+    - type: map_at_3
+      value: 36.635
+    - type: map_at_5
+      value: 39.219
+    - type: mrr_at_1
+      value: 30.214000000000002
+    - type: mrr_at_10
+      value: 43.443
+    - type: mrr_at_100
+      value: 44.326
+    - type: mrr_at_1000
+      value: 44.352000000000004
+    - type: mrr_at_3
+      value: 39.623999999999995
+    - type: mrr_at_5
+      value: 41.898
+    - type: ndcg_at_1
+      value: 30.214000000000002
+    - type: ndcg_at_10
+      value: 48.692
+    - type: ndcg_at_100
+      value: 53.671
+    - type: ndcg_at_1000
+      value: 54.522000000000006
+    - type: ndcg_at_3
+      value: 40.245
+    - type: ndcg_at_5
+      value: 44.580999999999996
+    - type: precision_at_1
+      value: 30.214000000000002
+    - type: precision_at_10
+      value: 8.3
+    - type: precision_at_100
+      value: 1.1079999999999999
+    - type: precision_at_1000
+      value: 0.11900000000000001
+    - type: precision_at_3
+      value: 18.521
+    - type: precision_at_5
+      value: 13.627
+    - type: recall_at_1
+      value: 26.692
+    - type: recall_at_10
+      value: 69.699
+    - type: recall_at_100
+      value: 91.425
+    - type: recall_at_1000
+      value: 97.78099999999999
+    - type: recall_at_3
+      value: 47.711
+    - type: recall_at_5
+      value: 57.643
+  - task:
+      type: Retrieval
+    dataset:
+      type: quora
+      name: MTEB QuoraRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 70.962
+    - type: map_at_10
+      value: 84.772
+    - type: map_at_100
+      value: 85.402
+    - type: map_at_1000
+      value: 85.418
+    - type: map_at_3
+      value: 81.89
+    - type: map_at_5
+      value: 83.685
+    - type: mrr_at_1
+      value: 81.67
+    - type: mrr_at_10
+      value: 87.681
+    - type: mrr_at_100
+      value: 87.792
+    - type: mrr_at_1000
+      value: 87.79299999999999
+    - type: mrr_at_3
+      value: 86.803
+    - type: mrr_at_5
+      value: 87.392
+    - type: ndcg_at_1
+      value: 81.69
+    - type: ndcg_at_10
+      value: 88.429
+    - type: ndcg_at_100
+      value: 89.66
+    - type: ndcg_at_1000
+      value: 89.762
+    - type: ndcg_at_3
+      value: 85.75
+    - type: ndcg_at_5
+      value: 87.20700000000001
+    - type: precision_at_1
+      value: 81.69
+    - type: precision_at_10
+      value: 13.395000000000001
+    - type: precision_at_100
+      value: 1.528
+    - type: precision_at_1000
+      value: 0.157
+    - type: precision_at_3
+      value: 37.507000000000005
+    - type: precision_at_5
+      value: 24.614
+    - type: recall_at_1
+      value: 70.962
+    - type: recall_at_10
+      value: 95.339
+    - type: recall_at_100
+      value: 99.543
+    - type: recall_at_1000
+      value: 99.984
+    - type: recall_at_3
+      value: 87.54899999999999
+    - type: recall_at_5
+      value: 91.726
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering
+      name: MTEB RedditClustering
+      config: default
+      split: test
+      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
+    metrics:
+    - type: v_measure
+      value: 55.506631779239555
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering-p2p
+      name: MTEB RedditClusteringP2P
+      config: default
+      split: test
+      revision: 282350215ef01743dc01b456c7f5241fa8937f16
+    metrics:
+    - type: v_measure
+      value: 60.63731341848479
+  - task:
+      type: Retrieval
+    dataset:
+      type: scidocs
+      name: MTEB SCIDOCS
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 4.852
+    - type: map_at_10
+      value: 13.175
+    - type: map_at_100
+      value: 15.623999999999999
+    - type: map_at_1000
+      value: 16.002
+    - type: map_at_3
+      value: 9.103
+    - type: map_at_5
+      value: 11.068999999999999
+    - type: mrr_at_1
+      value: 23.9
+    - type: mrr_at_10
+      value: 35.847
+    - type: mrr_at_100
+      value: 36.968
+    - type: mrr_at_1000
+      value: 37.018
+    - type: mrr_at_3
+      value: 32.300000000000004
+    - type: mrr_at_5
+      value: 34.14
+    - type: ndcg_at_1
+      value: 23.9
+    - type: ndcg_at_10
+      value: 21.889
+    - type: ndcg_at_100
+      value: 30.903000000000002
+    - type: ndcg_at_1000
+      value: 36.992000000000004
+    - type: ndcg_at_3
+      value: 20.274
+    - type: ndcg_at_5
+      value: 17.773
+    - type: precision_at_1
+      value: 23.9
+    - type: precision_at_10
+      value: 11.61
+    - type: precision_at_100
+      value: 2.4539999999999997
+    - type: precision_at_1000
+      value: 0.391
+    - type: precision_at_3
+      value: 19.133
+    - type: precision_at_5
+      value: 15.740000000000002
+    - type: recall_at_1
+      value: 4.852
+    - type: recall_at_10
+      value: 23.507
+    - type: recall_at_100
+      value: 49.775000000000006
+    - type: recall_at_1000
+      value: 79.308
+    - type: recall_at_3
+      value: 11.637
+    - type: recall_at_5
+      value: 15.947
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sickr-sts
+      name: MTEB SICK-R
+      config: default
+      split: test
+      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
+    metrics:
+    - type: cos_sim_pearson
+      value: 86.03345827446948
+    - type: cos_sim_spearman
+      value: 80.53174518259549
+    - type: euclidean_pearson
+      value: 83.44538971660883
+    - type: euclidean_spearman
+      value: 80.57344324098692
+    - type: manhattan_pearson
+      value: 83.36528808195459
+    - type: manhattan_spearman
+      value: 80.48931287157902
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts12-sts
+      name: MTEB STS12
+      config: default
+      split: test
+      revision: a0d554a64d88156834ff5ae9920b964011b16384
+    metrics:
+    - type: cos_sim_pearson
+      value: 85.21363088257881
+    - type: cos_sim_spearman
+      value: 75.56589127055523
+    - type: euclidean_pearson
+      value: 82.32868324521908
+    - type: euclidean_spearman
+      value: 75.31928550664554
+    - type: manhattan_pearson
+      value: 82.31332875713211
+    - type: manhattan_spearman
+      value: 75.35376322099196
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts13-sts
+      name: MTEB STS13
+      config: default
+      split: test
+      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
+    metrics:
+    - type: cos_sim_pearson
+      value: 85.09085593258487
+    - type: cos_sim_spearman
+      value: 86.26355088415221
+    - type: euclidean_pearson
+      value: 85.49646115361156
+    - type: euclidean_spearman
+      value: 86.20652472228703
+    - type: manhattan_pearson
+      value: 85.44084081123815
+    - type: manhattan_spearman
+      value: 86.1162623448951
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts14-sts
+      name: MTEB STS14
+      config: default
+      split: test
+      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
+    metrics:
+    - type: cos_sim_pearson
+      value: 84.68250248349368
+    - type: cos_sim_spearman
+      value: 82.29883673695083
+    - type: euclidean_pearson
+      value: 84.17633035446019
+    - type: euclidean_spearman
+      value: 82.19990511264791
+    - type: manhattan_pearson
+      value: 84.17408410692279
+    - type: manhattan_spearman
+      value: 82.249873895981
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts15-sts
+      name: MTEB STS15
+      config: default
+      split: test
+      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
+    metrics:
+    - type: cos_sim_pearson
+      value: 87.31878760045024
+    - type: cos_sim_spearman
+      value: 88.7364409031183
+    - type: euclidean_pearson
+      value: 88.230537618603
+    - type: euclidean_spearman
+      value: 88.76484309646318
+    - type: manhattan_pearson
+      value: 88.17689071136469
+    - type: manhattan_spearman
+      value: 88.72809249037928
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts16-sts
+      name: MTEB STS16
+      config: default
+      split: test
+      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
+    metrics:
+    - type: cos_sim_pearson
+      value: 83.41078559110638
+    - type: cos_sim_spearman
+      value: 85.27439135411049
+    - type: euclidean_pearson
+      value: 84.5333571592088
+    - type: euclidean_spearman
+      value: 85.25645460575957
+    - type: manhattan_pearson
+      value: 84.38428921610226
+    - type: manhattan_spearman
+      value: 85.07796040798796
+  - 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: 88.82374132382576
+    - type: cos_sim_spearman
+      value: 89.02101343562433
+    - type: euclidean_pearson
+      value: 89.50729765458932
+    - type: euclidean_spearman
+      value: 89.04184772869253
+    - type: manhattan_pearson
+      value: 89.51737904059856
+    - type: manhattan_spearman
+      value: 89.12925950440676
+  - 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: 67.56051823873482
+    - type: cos_sim_spearman
+      value: 68.50988748185463
+    - type: euclidean_pearson
+      value: 69.16524346147456
+    - type: euclidean_spearman
+      value: 68.61859952449579
+    - type: manhattan_pearson
+      value: 69.10618915706995
+    - type: manhattan_spearman
+      value: 68.36401769459522
+  - task:
+      type: STS
+    dataset:
+      type: mteb/stsbenchmark-sts
+      name: MTEB STSBenchmark
+      config: default
+      split: test
+      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
+    metrics:
+    - type: cos_sim_pearson
+      value: 85.4159693872625
+    - type: cos_sim_spearman
+      value: 87.07819121764247
+    - type: euclidean_pearson
+      value: 87.03013260863153
+    - type: euclidean_spearman
+      value: 87.06547293631309
+    - type: manhattan_pearson
+      value: 86.8129744446062
+    - type: manhattan_spearman
+      value: 86.88494096335627
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/scidocs-reranking
+      name: MTEB SciDocsRR
+      config: default
+      split: test
+      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
+    metrics:
+    - type: map
+      value: 86.47758088996575
+    - type: mrr
+      value: 96.17891458577733
+  - task:
+      type: Retrieval
+    dataset:
+      type: scifact
+      name: MTEB SciFact
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 57.538999999999994
+    - type: map_at_10
+      value: 66.562
+    - type: map_at_100
+      value: 67.254
+    - type: map_at_1000
+      value: 67.284
+    - type: map_at_3
+      value: 63.722
+    - type: map_at_5
+      value: 65.422
+    - type: mrr_at_1
+      value: 60.0
+    - type: mrr_at_10
+      value: 67.354
+    - type: mrr_at_100
+      value: 67.908
+    - type: mrr_at_1000
+      value: 67.93299999999999
+    - type: mrr_at_3
+      value: 65.056
+    - type: mrr_at_5
+      value: 66.43900000000001
+    - type: ndcg_at_1
+      value: 60.0
+    - type: ndcg_at_10
+      value: 70.858
+    - type: ndcg_at_100
+      value: 73.67099999999999
+    - type: ndcg_at_1000
+      value: 74.26700000000001
+    - type: ndcg_at_3
+      value: 65.911
+    - type: ndcg_at_5
+      value: 68.42200000000001
+    - type: precision_at_1
+      value: 60.0
+    - type: precision_at_10
+      value: 9.4
+    - type: precision_at_100
+      value: 1.083
+    - type: precision_at_1000
+      value: 0.11299999999999999
+    - type: precision_at_3
+      value: 25.444
+    - type: precision_at_5
+      value: 17.0
+    - type: recall_at_1
+      value: 57.538999999999994
+    - type: recall_at_10
+      value: 83.233
+    - type: recall_at_100
+      value: 95.667
+    - type: recall_at_1000
+      value: 100.0
+    - type: recall_at_3
+      value: 69.883
+    - type: recall_at_5
+      value: 76.19399999999999
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/sprintduplicatequestions-pairclassification
+      name: MTEB SprintDuplicateQuestions
+      config: default
+      split: test
+      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
+    metrics:
+    - type: cos_sim_accuracy
+      value: 99.82574257425742
+    - type: cos_sim_ap
+      value: 95.78722833053911
+    - type: cos_sim_f1
+      value: 90.94650205761316
+    - type: cos_sim_precision
+      value: 93.64406779661016
+    - type: cos_sim_recall
+      value: 88.4
+    - type: dot_accuracy
+      value: 99.83366336633664
+    - type: dot_ap
+      value: 95.89733601612964
+    - type: dot_f1
+      value: 91.41981613891727
+    - type: dot_precision
+      value: 93.42379958246346
+    - type: dot_recall
+      value: 89.5
+    - type: euclidean_accuracy
+      value: 99.82574257425742
+    - type: euclidean_ap
+      value: 95.75227035138846
+    - type: euclidean_f1
+      value: 90.96509240246407
+    - type: euclidean_precision
+      value: 93.45991561181435
+    - type: euclidean_recall
+      value: 88.6
+    - type: manhattan_accuracy
+      value: 99.82574257425742
+    - type: manhattan_ap
+      value: 95.76278266220176
+    - type: manhattan_f1
+      value: 91.08409321175279
+    - type: manhattan_precision
+      value: 92.29979466119097
+    - type: manhattan_recall
+      value: 89.9
+    - type: max_accuracy
+      value: 99.83366336633664
+    - type: max_ap
+      value: 95.89733601612964
+    - type: max_f1
+      value: 91.41981613891727
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering
+      name: MTEB StackExchangeClustering
+      config: default
+      split: test
+      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
+    metrics:
+    - type: v_measure
+      value: 61.905425988638605
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering-p2p
+      name: MTEB StackExchangeClusteringP2P
+      config: default
+      split: test
+      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
+    metrics:
+    - type: v_measure
+      value: 36.159589881679736
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/stackoverflowdupquestions-reranking
+      name: MTEB StackOverflowDupQuestions
+      config: default
+      split: test
+      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
+    metrics:
+    - type: map
+      value: 53.0605499476397
+    - type: mrr
+      value: 53.91594516594517
+  - task:
+      type: Summarization
+    dataset:
+      type: mteb/summeval
+      name: MTEB SummEval
+      config: default
+      split: test
+      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
+    metrics:
+    - type: cos_sim_pearson
+      value: 30.202718009067
+    - type: cos_sim_spearman
+      value: 31.136199912366987
+    - type: dot_pearson
+      value: 30.66329011927951
+    - type: dot_spearman
+      value: 30.107664909625107
+  - task:
+      type: Retrieval
+    dataset:
+      type: trec-covid
+      name: MTEB TRECCOVID
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 0.209
+    - type: map_at_10
+      value: 1.712
+    - type: map_at_100
+      value: 9.464
+    - type: map_at_1000
+      value: 23.437
+    - type: map_at_3
+      value: 0.609
+    - type: map_at_5
+      value: 0.9440000000000001
+    - type: mrr_at_1
+      value: 78.0
+    - type: mrr_at_10
+      value: 86.833
+    - type: mrr_at_100
+      value: 86.833
+    - type: mrr_at_1000
+      value: 86.833
+    - type: mrr_at_3
+      value: 85.333
+    - type: mrr_at_5
+      value: 86.833
+    - type: ndcg_at_1
+      value: 74.0
+    - type: ndcg_at_10
+      value: 69.14
+    - type: ndcg_at_100
+      value: 53.047999999999995
+    - type: ndcg_at_1000
+      value: 48.577
+    - type: ndcg_at_3
+      value: 75.592
+    - type: ndcg_at_5
+      value: 72.509
+    - type: precision_at_1
+      value: 78.0
+    - type: precision_at_10
+      value: 73.0
+    - type: precision_at_100
+      value: 54.44
+    - type: precision_at_1000
+      value: 21.326
+    - type: precision_at_3
+      value: 80.667
+    - type: precision_at_5
+      value: 77.2
+    - type: recall_at_1
+      value: 0.209
+    - type: recall_at_10
+      value: 1.932
+    - type: recall_at_100
+      value: 13.211999999999998
+    - type: recall_at_1000
+      value: 45.774
+    - type: recall_at_3
+      value: 0.644
+    - type: recall_at_5
+      value: 1.0290000000000001
+  - task:
+      type: Retrieval
+    dataset:
+      type: webis-touche2020
+      name: MTEB Touche2020
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 2.609
+    - type: map_at_10
+      value: 8.334999999999999
+    - type: map_at_100
+      value: 14.604000000000001
+    - type: map_at_1000
+      value: 16.177
+    - type: map_at_3
+      value: 4.87
+    - type: map_at_5
+      value: 6.3149999999999995
+    - type: mrr_at_1
+      value: 32.653
+    - type: mrr_at_10
+      value: 45.047
+    - type: mrr_at_100
+      value: 45.808
+    - type: mrr_at_1000
+      value: 45.808
+    - type: mrr_at_3
+      value: 41.497
+    - type: mrr_at_5
+      value: 43.231
+    - type: ndcg_at_1
+      value: 30.612000000000002
+    - type: ndcg_at_10
+      value: 21.193
+    - type: ndcg_at_100
+      value: 34.97
+    - type: ndcg_at_1000
+      value: 46.69
+    - type: ndcg_at_3
+      value: 24.823
+    - type: ndcg_at_5
+      value: 22.872999999999998
+    - type: precision_at_1
+      value: 32.653
+    - type: precision_at_10
+      value: 17.959
+    - type: precision_at_100
+      value: 7.4079999999999995
+    - type: precision_at_1000
+      value: 1.537
+    - type: precision_at_3
+      value: 25.85
+    - type: precision_at_5
+      value: 22.448999999999998
+    - type: recall_at_1
+      value: 2.609
+    - type: recall_at_10
+      value: 13.63
+    - type: recall_at_100
+      value: 47.014
+    - type: recall_at_1000
+      value: 83.176
+    - type: recall_at_3
+      value: 5.925
+    - type: recall_at_5
+      value: 8.574
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/toxic_conversations_50k
+      name: MTEB ToxicConversationsClassification
+      config: default
+      split: test
+      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
+    metrics:
+    - type: accuracy
+      value: 72.80239999999999
+    - type: ap
+      value: 15.497911013214791
+    - type: f1
+      value: 56.258411577947285
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/tweet_sentiment_extraction
+      name: MTEB TweetSentimentExtractionClassification
+      config: default
+      split: test
+      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
+    metrics:
+    - type: accuracy
+      value: 61.00452744765139
+    - type: f1
+      value: 61.42228624410908
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/twentynewsgroups-clustering
+      name: MTEB TwentyNewsgroupsClustering
+      config: default
+      split: test
+      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
+    metrics:
+    - type: v_measure
+      value: 50.00516915962345
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twittersemeval2015-pairclassification
+      name: MTEB TwitterSemEval2015
+      config: default
+      split: test
+      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
+    metrics:
+    - type: cos_sim_accuracy
+      value: 85.62317458425225
+    - type: cos_sim_ap
+      value: 72.95115658063823
+    - type: cos_sim_f1
+      value: 66.78976523344764
+    - type: cos_sim_precision
+      value: 66.77215189873418
+    - type: cos_sim_recall
+      value: 66.80738786279683
+    - type: dot_accuracy
+      value: 85.62317458425225
+    - type: dot_ap
+      value: 73.10385271517778
+    - type: dot_f1
+      value: 66.94853829427399
+    - type: dot_precision
+      value: 61.74242424242424
+    - type: dot_recall
+      value: 73.11345646437995
+    - type: euclidean_accuracy
+      value: 85.65893783155511
+    - type: euclidean_ap
+      value: 72.87428208473992
+    - type: euclidean_f1
+      value: 66.70919994896005
+    - type: euclidean_precision
+      value: 64.5910551025451
+    - type: euclidean_recall
+      value: 68.97097625329816
+    - type: manhattan_accuracy
+      value: 85.59933241938367
+    - type: manhattan_ap
+      value: 72.67282695064966
+    - type: manhattan_f1
+      value: 66.67537215983286
+    - type: manhattan_precision
+      value: 66.00310237849017
+    - type: manhattan_recall
+      value: 67.36147757255937
+    - type: max_accuracy
+      value: 85.65893783155511
+    - type: max_ap
+      value: 73.10385271517778
+    - type: max_f1
+      value: 66.94853829427399
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twitterurlcorpus-pairclassification
+      name: MTEB TwitterURLCorpus
+      config: default
+      split: test
+      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
+    metrics:
+    - type: cos_sim_accuracy
+      value: 88.69096130709822
+    - type: cos_sim_ap
+      value: 85.30326978668063
+    - type: cos_sim_f1
+      value: 77.747088683189
+    - type: cos_sim_precision
+      value: 75.4491451753115
+    - type: cos_sim_recall
+      value: 80.189405605174
+    - type: dot_accuracy
+      value: 88.43870066363954
+    - type: dot_ap
+      value: 84.62999949222983
+    - type: dot_f1
+      value: 77.3074661963551
+    - type: dot_precision
+      value: 73.93871239808828
+    - type: dot_recall
+      value: 80.99784416384355
+    - type: euclidean_accuracy
+      value: 88.70066363953894
+    - type: euclidean_ap
+      value: 85.34184508966621
+    - type: euclidean_f1
+      value: 77.76871756856931
+    - type: euclidean_precision
+      value: 74.97855917667239
+    - type: euclidean_recall
+      value: 80.77456113335386
+    - type: manhattan_accuracy
+      value: 88.68319944114566
+    - type: manhattan_ap
+      value: 85.3026464242333
+    - type: manhattan_f1
+      value: 77.66561049296294
+    - type: manhattan_precision
+      value: 74.4665818849795
+    - type: manhattan_recall
+      value: 81.15183246073299
+    - type: max_accuracy
+      value: 88.70066363953894
+    - type: max_ap
+      value: 85.34184508966621
+    - type: max_f1
+      value: 77.76871756856931
+---
+<h1 align="center">GIST small Embedding v0</h1>
+
+*GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning*
+
+The model is fine-tuned on top of the [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) using the [MEDI dataset](https://github.com/xlang-ai/instructor-embedding.git) augmented with mined triplets from the [MTEB Classification](https://huggingface.co/mteb) training dataset (excluding data from the Amazon Polarity Classification task).
+
+The model does not require any instruction for generating embeddings. This means that queries for retrieval tasks can be directly encoded without crafting instructions.
+
+Technical paper: [GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning](https://arxiv.org/abs/2402.16829)
+
+
+# Data
+
+The dataset used is a compilation of the MEDI and MTEB Classification training datasets. Third-party datasets may be subject to additional terms and conditions under their associated licenses. A HuggingFace Dataset version of the compiled dataset, and the specific revision used to train the model, is available:
+
+- Dataset: [avsolatorio/medi-data-mteb_avs_triplets](https://huggingface.co/datasets/avsolatorio/medi-data-mteb_avs_triplets)
+- Revision: 238a0499b6e6b690cc64ea56fde8461daa8341bb
+
+The dataset contains a `task_type` key, which can be used to select only the mteb classification tasks (prefixed with `mteb_`).
+
+The **MEDI Dataset** is published in the following paper: [One Embedder, Any Task: Instruction-Finetuned Text Embeddings](https://arxiv.org/abs/2212.09741).
+
+The MTEB Benchmark results of the GIST embedding model, compared with the base model, suggest that the fine-tuning dataset has perturbed the model considerably, which resulted in significant improvements in certain tasks while adversely degrading performance in some.
+
+The retrieval performance for the TRECCOVID task is of note. The fine-tuning dataset does not contain significant knowledge about COVID-19, which could have caused the observed performance degradation. We found some evidence, detailed in the paper, that thematic coverage of the fine-tuning data can affect downstream performance.
+
+# Usage
+
+The model can be easily loaded using the Sentence Transformers library.
+
+```Python
+import torch.nn.functional as F
+from sentence_transformers import SentenceTransformer
+
+revision = None  # Replace with the specific revision to ensure reproducibility if the model is updated.
+
+model = SentenceTransformer("avsolatorio/GIST-small-Embedding-v0", revision=revision)
+
+texts = [
+    "Illustration of the REaLTabFormer model. The left block shows the non-relational tabular data model using GPT-2 with a causal LM head. In contrast, the right block shows how a relational dataset's child table is modeled using a sequence-to-sequence (Seq2Seq) model. The Seq2Seq model uses the observations in the parent table to condition the generation of the observations in the child table. The trained GPT-2 model on the parent table, with weights frozen, is also used as the encoder in the Seq2Seq model.",
+    "Predicting human mobility holds significant practical value, with applications ranging from enhancing disaster risk planning to simulating epidemic spread. In this paper, we present the GeoFormer, a decoder-only transformer model adapted from the GPT architecture to forecast human mobility.",
+    "As the economies of Southeast Asia continue adopting digital technologies, policy makers increasingly ask how to prepare the workforce for emerging labor demands. However, little is known about the skills that workers need to adapt to these changes"
+]
+
+# Compute embeddings
+embeddings = model.encode(texts, convert_to_tensor=True)
+
+# Compute cosine-similarity for each pair of sentences
+scores = F.cosine_similarity(embeddings.unsqueeze(1), embeddings.unsqueeze(0), dim=-1)
+
+print(scores.cpu().numpy())
+```
+
+# Training Parameters
+
+Below are the training parameters used to fine-tune the model:
+
+```
+Epochs = 40
+Warmup ratio = 0.1
+Learning rate = 5e-6
+Batch size = 16
+Checkpoint step = 102000
+Contrastive loss temperature = 0.01
+```
+
+
+# Evaluation
+
+The model was evaluated using the [MTEB Evaluation](https://huggingface.co/mteb) suite.
+
+
+# Citation
+
+Please cite our work if you use GISTEmbed or the datasets we published in your projects or research. 🤗
+
+```
+@article{solatorio2024gistembed,
+    title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning},
+    author={Aivin V. Solatorio},
+    journal={arXiv preprint arXiv:2402.16829},
+    year={2024},
+    URL={https://arxiv.org/abs/2402.16829}
+    eprint={2402.16829},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
+}
+```
+
+# Acknowledgements
+
+This work is supported by the "KCP IV - Exploring Data Use in the Development Economics Literature using Large Language Models (AI and LLMs)" project funded by the [Knowledge for Change Program (KCP)](https://www.worldbank.org/en/programs/knowledge-for-change) of the World Bank - RA-P503405-RESE-TF0C3444.
+
+The findings, interpretations, and conclusions expressed in this material are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

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tokenizer_config.json

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+}