Add model readme

Signed-off-by: Aivin V. Solatorio <avsolatorio@gmail.com>

README.md

@@ -1,3 +1,2685 @@
 ---
+language:
+- en
+library_name: sentence-transformers
 license: mit
+pipeline_tag: sentence-similarity
+tags:
+  - feature-extraction
+  - mteb
+  - sentence-similarity
+  - sentence-transformers
+
+model-index:
+- name: GIST-all-MiniLM-L6-v2
+  results:
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_counterfactual
+      name: MTEB AmazonCounterfactualClassification (en)
+      config: en
+      split: test
+      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
+    metrics:
+    - type: accuracy
+      value: 69.68656716417911
+    - type: ap
+      value: 31.84640905923114
+    - type: f1
+      value: 63.4379647836158
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_polarity
+      name: MTEB AmazonPolarityClassification
+      config: default
+      split: test
+      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
+    metrics:
+    - type: accuracy
+      value: 82.078025
+    - type: ap
+      value: 77.3451894150185
+    - type: f1
+      value: 81.97258648080654
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_reviews_multi
+      name: MTEB AmazonReviewsClassification (en)
+      config: en
+      split: test
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+    metrics:
+    - type: accuracy
+      value: 38.254
+    - type: f1
+      value: 37.940387801030376
+  - task:
+      type: Retrieval
+    dataset:
+      type: arguana
+      name: MTEB ArguAna
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 28.876
+    - type: map_at_10
+      value: 44.741
+    - type: map_at_100
+      value: 45.688
+    - type: map_at_1000
+      value: 45.695
+    - type: map_at_3
+      value: 39.829
+    - type: map_at_5
+      value: 42.646
+    - type: mrr_at_1
+      value: 30.156
+    - type: mrr_at_10
+      value: 45.196
+    - type: mrr_at_100
+      value: 46.149
+    - type: mrr_at_1000
+      value: 46.156000000000006
+    - type: mrr_at_3
+      value: 40.339000000000006
+    - type: mrr_at_5
+      value: 43.120000000000005
+    - type: ndcg_at_1
+      value: 28.876
+    - type: ndcg_at_10
+      value: 53.581
+    - type: ndcg_at_100
+      value: 57.428000000000004
+    - type: ndcg_at_1000
+      value: 57.599000000000004
+    - type: ndcg_at_3
+      value: 43.46
+    - type: ndcg_at_5
+      value: 48.501
+    - type: precision_at_1
+      value: 28.876
+    - type: precision_at_10
+      value: 8.186
+    - type: precision_at_100
+      value: 0.9820000000000001
+    - type: precision_at_1000
+      value: 0.1
+    - type: precision_at_3
+      value: 17.994
+    - type: precision_at_5
+      value: 13.229
+    - type: recall_at_1
+      value: 28.876
+    - type: recall_at_10
+      value: 81.863
+    - type: recall_at_100
+      value: 98.222
+    - type: recall_at_1000
+      value: 99.502
+    - type: recall_at_3
+      value: 53.983000000000004
+    - type: recall_at_5
+      value: 66.145
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-p2p
+      name: MTEB ArxivClusteringP2P
+      config: default
+      split: test
+      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
+    metrics:
+    - type: v_measure
+      value: 44.81109445338116
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-s2s
+      name: MTEB ArxivClusteringS2S
+      config: default
+      split: test
+      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
+    metrics:
+    - type: v_measure
+      value: 35.705350248894476
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/askubuntudupquestions-reranking
+      name: MTEB AskUbuntuDupQuestions
+      config: default
+      split: test
+      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
+    metrics:
+    - type: map
+      value: 63.13335364248881
+    - type: mrr
+      value: 76.80605021325243
+  - task:
+      type: STS
+    dataset:
+      type: mteb/biosses-sts
+      name: MTEB BIOSSES
+      config: default
+      split: test
+      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
+    metrics:
+    - type: cos_sim_pearson
+      value: 83.33741812376516
+    - type: cos_sim_spearman
+      value: 80.51267790947811
+    - type: euclidean_pearson
+      value: 67.49002803470997
+    - type: euclidean_spearman
+      value: 65.39064659674824
+    - type: manhattan_pearson
+      value: 67.3390206944745
+    - type: manhattan_spearman
+      value: 65.35329634810715
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/banking77
+      name: MTEB Banking77Classification
+      config: default
+      split: test
+      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
+    metrics:
+    - type: accuracy
+      value: 83.13636363636364
+    - type: f1
+      value: 83.10810612376775
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-p2p
+      name: MTEB BiorxivClusteringP2P
+      config: default
+      split: test
+      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
+    metrics:
+    - type: v_measure
+      value: 38.47849860204599
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-s2s
+      name: MTEB BiorxivClusteringS2S
+      config: default
+      split: test
+      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
+    metrics:
+    - type: v_measure
+      value: 31.159196233892057
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackAndroidRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 34.096
+    - type: map_at_10
+      value: 46.61
+    - type: map_at_100
+      value: 48.163
+    - type: map_at_1000
+      value: 48.272
+    - type: map_at_3
+      value: 43.03
+    - type: map_at_5
+      value: 45.036
+    - type: mrr_at_1
+      value: 42.489
+    - type: mrr_at_10
+      value: 52.83
+    - type: mrr_at_100
+      value: 53.525
+    - type: mrr_at_1000
+      value: 53.561
+    - type: mrr_at_3
+      value: 50.453
+    - type: mrr_at_5
+      value: 51.991
+    - type: ndcg_at_1
+      value: 42.489
+    - type: ndcg_at_10
+      value: 53.21900000000001
+    - type: ndcg_at_100
+      value: 58.277
+    - type: ndcg_at_1000
+      value: 59.836999999999996
+    - type: ndcg_at_3
+      value: 48.64
+    - type: ndcg_at_5
+      value: 50.800999999999995
+    - type: precision_at_1
+      value: 42.489
+    - type: precision_at_10
+      value: 10.343
+    - type: precision_at_100
+      value: 1.624
+    - type: precision_at_1000
+      value: 0.20400000000000001
+    - type: precision_at_3
+      value: 23.605
+    - type: precision_at_5
+      value: 16.881
+    - type: recall_at_1
+      value: 34.096
+    - type: recall_at_10
+      value: 65.003
+    - type: recall_at_100
+      value: 86.211
+    - type: recall_at_1000
+      value: 96.017
+    - type: recall_at_3
+      value: 51.307
+    - type: recall_at_5
+      value: 57.873
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackEnglishRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 29.482000000000003
+    - type: map_at_10
+      value: 39.793
+    - type: map_at_100
+      value: 41.028
+    - type: map_at_1000
+      value: 41.163
+    - type: map_at_3
+      value: 36.674
+    - type: map_at_5
+      value: 38.379999999999995
+    - type: mrr_at_1
+      value: 37.197
+    - type: mrr_at_10
+      value: 45.991
+    - type: mrr_at_100
+      value: 46.599000000000004
+    - type: mrr_at_1000
+      value: 46.649
+    - type: mrr_at_3
+      value: 43.662
+    - type: mrr_at_5
+      value: 45.054
+    - type: ndcg_at_1
+      value: 37.197
+    - type: ndcg_at_10
+      value: 45.73
+    - type: ndcg_at_100
+      value: 50.074
+    - type: ndcg_at_1000
+      value: 52.312000000000005
+    - type: ndcg_at_3
+      value: 41.308
+    - type: ndcg_at_5
+      value: 43.323
+    - type: precision_at_1
+      value: 37.197
+    - type: precision_at_10
+      value: 8.854
+    - type: precision_at_100
+      value: 1.411
+    - type: precision_at_1000
+      value: 0.191
+    - type: precision_at_3
+      value: 20.085
+    - type: precision_at_5
+      value: 14.42
+    - type: recall_at_1
+      value: 29.482000000000003
+    - type: recall_at_10
+      value: 56.077999999999996
+    - type: recall_at_100
+      value: 74.83800000000001
+    - type: recall_at_1000
+      value: 89.128
+    - type: recall_at_3
+      value: 42.971
+    - type: recall_at_5
+      value: 48.577
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGamingRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 38.679
+    - type: map_at_10
+      value: 50.854
+    - type: map_at_100
+      value: 51.849000000000004
+    - type: map_at_1000
+      value: 51.909000000000006
+    - type: map_at_3
+      value: 47.82
+    - type: map_at_5
+      value: 49.479
+    - type: mrr_at_1
+      value: 44.263000000000005
+    - type: mrr_at_10
+      value: 54.161
+    - type: mrr_at_100
+      value: 54.833
+    - type: mrr_at_1000
+      value: 54.86600000000001
+    - type: mrr_at_3
+      value: 51.912000000000006
+    - type: mrr_at_5
+      value: 53.201
+    - type: ndcg_at_1
+      value: 44.263000000000005
+    - type: ndcg_at_10
+      value: 56.486000000000004
+    - type: ndcg_at_100
+      value: 60.553999999999995
+    - type: ndcg_at_1000
+      value: 61.77
+    - type: ndcg_at_3
+      value: 51.456999999999994
+    - type: ndcg_at_5
+      value: 53.83
+    - type: precision_at_1
+      value: 44.263000000000005
+    - type: precision_at_10
+      value: 9.041
+    - type: precision_at_100
+      value: 1.204
+    - type: precision_at_1000
+      value: 0.135
+    - type: precision_at_3
+      value: 22.989
+    - type: precision_at_5
+      value: 15.598999999999998
+    - type: recall_at_1
+      value: 38.679
+    - type: recall_at_10
+      value: 69.77799999999999
+    - type: recall_at_100
+      value: 87.59
+    - type: recall_at_1000
+      value: 96.202
+    - type: recall_at_3
+      value: 56.351
+    - type: recall_at_5
+      value: 62.16199999999999
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGisRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 27.245
+    - type: map_at_10
+      value: 36.104
+    - type: map_at_100
+      value: 37.207
+    - type: map_at_1000
+      value: 37.288
+    - type: map_at_3
+      value: 33.427
+    - type: map_at_5
+      value: 34.866
+    - type: mrr_at_1
+      value: 29.604999999999997
+    - type: mrr_at_10
+      value: 38.346999999999994
+    - type: mrr_at_100
+      value: 39.274
+    - type: mrr_at_1000
+      value: 39.336
+    - type: mrr_at_3
+      value: 35.876000000000005
+    - type: mrr_at_5
+      value: 37.164
+    - type: ndcg_at_1
+      value: 29.604999999999997
+    - type: ndcg_at_10
+      value: 41.253
+    - type: ndcg_at_100
+      value: 46.511
+    - type: ndcg_at_1000
+      value: 48.503
+    - type: ndcg_at_3
+      value: 35.975
+    - type: ndcg_at_5
+      value: 38.35
+    - type: precision_at_1
+      value: 29.604999999999997
+    - type: precision_at_10
+      value: 6.305
+    - type: precision_at_100
+      value: 0.9440000000000001
+    - type: precision_at_1000
+      value: 0.11499999999999999
+    - type: precision_at_3
+      value: 15.179
+    - type: precision_at_5
+      value: 10.508000000000001
+    - type: recall_at_1
+      value: 27.245
+    - type: recall_at_10
+      value: 55.07300000000001
+    - type: recall_at_100
+      value: 79.036
+    - type: recall_at_1000
+      value: 93.809
+    - type: recall_at_3
+      value: 40.593
+    - type: recall_at_5
+      value: 46.318
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackMathematicaRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 15.440000000000001
+    - type: map_at_10
+      value: 23.758000000000003
+    - type: map_at_100
+      value: 25.1
+    - type: map_at_1000
+      value: 25.230000000000004
+    - type: map_at_3
+      value: 21.093
+    - type: map_at_5
+      value: 22.431
+    - type: mrr_at_1
+      value: 19.279
+    - type: mrr_at_10
+      value: 28.077
+    - type: mrr_at_100
+      value: 29.164
+    - type: mrr_at_1000
+      value: 29.237000000000002
+    - type: mrr_at_3
+      value: 25.497999999999998
+    - type: mrr_at_5
+      value: 26.76
+    - type: ndcg_at_1
+      value: 19.279
+    - type: ndcg_at_10
+      value: 29.025000000000002
+    - type: ndcg_at_100
+      value: 35.244
+    - type: ndcg_at_1000
+      value: 38.112
+    - type: ndcg_at_3
+      value: 24.079
+    - type: ndcg_at_5
+      value: 26.064999999999998
+    - type: precision_at_1
+      value: 19.279
+    - type: precision_at_10
+      value: 5.498
+    - type: precision_at_100
+      value: 0.985
+    - type: precision_at_1000
+      value: 0.136
+    - type: precision_at_3
+      value: 11.692
+    - type: precision_at_5
+      value: 8.383000000000001
+    - type: recall_at_1
+      value: 15.440000000000001
+    - type: recall_at_10
+      value: 40.855999999999995
+    - type: recall_at_100
+      value: 67.916
+    - type: recall_at_1000
+      value: 88.11
+    - type: recall_at_3
+      value: 27.387
+    - type: recall_at_5
+      value: 32.387
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackPhysicsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 29.351
+    - type: map_at_10
+      value: 40.477999999999994
+    - type: map_at_100
+      value: 41.8
+    - type: map_at_1000
+      value: 41.926
+    - type: map_at_3
+      value: 37.246
+    - type: map_at_5
+      value: 39.206
+    - type: mrr_at_1
+      value: 36.092
+    - type: mrr_at_10
+      value: 46.319
+    - type: mrr_at_100
+      value: 47.087
+    - type: mrr_at_1000
+      value: 47.13
+    - type: mrr_at_3
+      value: 43.808
+    - type: mrr_at_5
+      value: 45.406
+    - type: ndcg_at_1
+      value: 36.092
+    - type: ndcg_at_10
+      value: 46.707
+    - type: ndcg_at_100
+      value: 52.266
+    - type: ndcg_at_1000
+      value: 54.303000000000004
+    - type: ndcg_at_3
+      value: 41.858000000000004
+    - type: ndcg_at_5
+      value: 44.407999999999994
+    - type: precision_at_1
+      value: 36.092
+    - type: precision_at_10
+      value: 8.527
+    - type: precision_at_100
+      value: 1.34
+    - type: precision_at_1000
+      value: 0.172
+    - type: precision_at_3
+      value: 20.212
+    - type: precision_at_5
+      value: 14.456
+    - type: recall_at_1
+      value: 29.351
+    - type: recall_at_10
+      value: 59.254
+    - type: recall_at_100
+      value: 83.047
+    - type: recall_at_1000
+      value: 95.911
+    - type: recall_at_3
+      value: 45.488
+    - type: recall_at_5
+      value: 52.186
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackProgrammersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 25.601000000000003
+    - type: map_at_10
+      value: 34.589999999999996
+    - type: map_at_100
+      value: 35.917
+    - type: map_at_1000
+      value: 36.032
+    - type: map_at_3
+      value: 31.338
+    - type: map_at_5
+      value: 33.128
+    - type: mrr_at_1
+      value: 31.163999999999998
+    - type: mrr_at_10
+      value: 39.646
+    - type: mrr_at_100
+      value: 40.491
+    - type: mrr_at_1000
+      value: 40.549
+    - type: mrr_at_3
+      value: 36.91
+    - type: mrr_at_5
+      value: 38.446000000000005
+    - type: ndcg_at_1
+      value: 31.163999999999998
+    - type: ndcg_at_10
+      value: 40.321
+    - type: ndcg_at_100
+      value: 45.894
+    - type: ndcg_at_1000
+      value: 48.233
+    - type: ndcg_at_3
+      value: 34.871
+    - type: ndcg_at_5
+      value: 37.302
+    - type: precision_at_1
+      value: 31.163999999999998
+    - type: precision_at_10
+      value: 7.523000000000001
+    - type: precision_at_100
+      value: 1.188
+    - type: precision_at_1000
+      value: 0.157
+    - type: precision_at_3
+      value: 16.591
+    - type: precision_at_5
+      value: 12.055
+    - type: recall_at_1
+      value: 25.601000000000003
+    - type: recall_at_10
+      value: 52.422000000000004
+    - type: recall_at_100
+      value: 76.426
+    - type: recall_at_1000
+      value: 92.142
+    - type: recall_at_3
+      value: 37.141000000000005
+    - type: recall_at_5
+      value: 43.449
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 26.267916666666668
+    - type: map_at_10
+      value: 35.758250000000004
+    - type: map_at_100
+      value: 37.0185
+    - type: map_at_1000
+      value: 37.136916666666664
+    - type: map_at_3
+      value: 32.85125
+    - type: map_at_5
+      value: 34.4165
+    - type: mrr_at_1
+      value: 31.131083333333343
+    - type: mrr_at_10
+      value: 39.95941666666667
+    - type: mrr_at_100
+      value: 40.81541666666666
+    - type: mrr_at_1000
+      value: 40.87358333333332
+    - type: mrr_at_3
+      value: 37.5175
+    - type: mrr_at_5
+      value: 38.86833333333334
+    - type: ndcg_at_1
+      value: 31.131083333333343
+    - type: ndcg_at_10
+      value: 41.26174999999999
+    - type: ndcg_at_100
+      value: 46.55975
+    - type: ndcg_at_1000
+      value: 48.80016666666666
+    - type: ndcg_at_3
+      value: 36.37566666666667
+    - type: ndcg_at_5
+      value: 38.55166666666667
+    - type: precision_at_1
+      value: 31.131083333333343
+    - type: precision_at_10
+      value: 7.315916666666666
+    - type: precision_at_100
+      value: 1.1813333333333333
+    - type: precision_at_1000
+      value: 0.15666666666666665
+    - type: precision_at_3
+      value: 16.818166666666663
+    - type: precision_at_5
+      value: 11.923
+    - type: recall_at_1
+      value: 26.267916666666668
+    - type: recall_at_10
+      value: 53.28391666666666
+    - type: recall_at_100
+      value: 76.53983333333332
+    - type: recall_at_1000
+      value: 91.93008333333334
+    - type: recall_at_3
+      value: 39.60583333333334
+    - type: recall_at_5
+      value: 45.25741666666667
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackStatsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 23.372
+    - type: map_at_10
+      value: 30.916
+    - type: map_at_100
+      value: 31.980999999999998
+    - type: map_at_1000
+      value: 32.07
+    - type: map_at_3
+      value: 28.778
+    - type: map_at_5
+      value: 29.872
+    - type: mrr_at_1
+      value: 26.074
+    - type: mrr_at_10
+      value: 33.451
+    - type: mrr_at_100
+      value: 34.366
+    - type: mrr_at_1000
+      value: 34.424
+    - type: mrr_at_3
+      value: 31.569999999999997
+    - type: mrr_at_5
+      value: 32.467
+    - type: ndcg_at_1
+      value: 26.074
+    - type: ndcg_at_10
+      value: 35.119
+    - type: ndcg_at_100
+      value: 40.357
+    - type: ndcg_at_1000
+      value: 42.548
+    - type: ndcg_at_3
+      value: 31.281
+    - type: ndcg_at_5
+      value: 32.866
+    - type: precision_at_1
+      value: 26.074
+    - type: precision_at_10
+      value: 5.583
+    - type: precision_at_100
+      value: 0.899
+    - type: precision_at_1000
+      value: 0.116
+    - type: precision_at_3
+      value: 13.700999999999999
+    - type: precision_at_5
+      value: 9.447999999999999
+    - type: recall_at_1
+      value: 23.372
+    - type: recall_at_10
+      value: 45.396
+    - type: recall_at_100
+      value: 69.26
+    - type: recall_at_1000
+      value: 85.438
+    - type: recall_at_3
+      value: 34.373
+    - type: recall_at_5
+      value: 38.509
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackTexRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 17.483999999999998
+    - type: map_at_10
+      value: 25.191999999999997
+    - type: map_at_100
+      value: 26.432
+    - type: map_at_1000
+      value: 26.566000000000003
+    - type: map_at_3
+      value: 22.697
+    - type: map_at_5
+      value: 24.101
+    - type: mrr_at_1
+      value: 21.645
+    - type: mrr_at_10
+      value: 29.243000000000002
+    - type: mrr_at_100
+      value: 30.232
+    - type: mrr_at_1000
+      value: 30.312
+    - type: mrr_at_3
+      value: 26.967000000000002
+    - type: mrr_at_5
+      value: 28.262999999999998
+    - type: ndcg_at_1
+      value: 21.645
+    - type: ndcg_at_10
+      value: 30.087999999999997
+    - type: ndcg_at_100
+      value: 35.806
+    - type: ndcg_at_1000
+      value: 38.763
+    - type: ndcg_at_3
+      value: 25.746999999999996
+    - type: ndcg_at_5
+      value: 27.765
+    - type: precision_at_1
+      value: 21.645
+    - type: precision_at_10
+      value: 5.6129999999999995
+    - type: precision_at_100
+      value: 1.004
+    - type: precision_at_1000
+      value: 0.14400000000000002
+    - type: precision_at_3
+      value: 12.331
+    - type: precision_at_5
+      value: 9.009
+    - type: recall_at_1
+      value: 17.483999999999998
+    - type: recall_at_10
+      value: 40.723
+    - type: recall_at_100
+      value: 66.226
+    - type: recall_at_1000
+      value: 87.312
+    - type: recall_at_3
+      value: 28.481
+    - type: recall_at_5
+      value: 33.777
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackUnixRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 26.735
+    - type: map_at_10
+      value: 36.431000000000004
+    - type: map_at_100
+      value: 37.696000000000005
+    - type: map_at_1000
+      value: 37.793
+    - type: map_at_3
+      value: 33.416000000000004
+    - type: map_at_5
+      value: 34.934
+    - type: mrr_at_1
+      value: 31.25
+    - type: mrr_at_10
+      value: 40.516000000000005
+    - type: mrr_at_100
+      value: 41.392
+    - type: mrr_at_1000
+      value: 41.449000000000005
+    - type: mrr_at_3
+      value: 37.842
+    - type: mrr_at_5
+      value: 39.265
+    - type: ndcg_at_1
+      value: 31.25
+    - type: ndcg_at_10
+      value: 42.191
+    - type: ndcg_at_100
+      value: 47.683
+    - type: ndcg_at_1000
+      value: 49.815
+    - type: ndcg_at_3
+      value: 36.744
+    - type: ndcg_at_5
+      value: 39.007
+    - type: precision_at_1
+      value: 31.25
+    - type: precision_at_10
+      value: 7.276000000000001
+    - type: precision_at_100
+      value: 1.125
+    - type: precision_at_1000
+      value: 0.14100000000000001
+    - type: precision_at_3
+      value: 16.76
+    - type: precision_at_5
+      value: 11.791
+    - type: recall_at_1
+      value: 26.735
+    - type: recall_at_10
+      value: 55.444
+    - type: recall_at_100
+      value: 79.098
+    - type: recall_at_1000
+      value: 93.815
+    - type: recall_at_3
+      value: 40.623
+    - type: recall_at_5
+      value: 46.322
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWebmastersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 26.495
+    - type: map_at_10
+      value: 35.648
+    - type: map_at_100
+      value: 37.275000000000006
+    - type: map_at_1000
+      value: 37.494
+    - type: map_at_3
+      value: 32.446999999999996
+    - type: map_at_5
+      value: 34.233000000000004
+    - type: mrr_at_1
+      value: 31.225
+    - type: mrr_at_10
+      value: 40.127
+    - type: mrr_at_100
+      value: 41.092
+    - type: mrr_at_1000
+      value: 41.148
+    - type: mrr_at_3
+      value: 37.153999999999996
+    - type: mrr_at_5
+      value: 38.873999999999995
+    - type: ndcg_at_1
+      value: 31.225
+    - type: ndcg_at_10
+      value: 41.665
+    - type: ndcg_at_100
+      value: 47.557
+    - type: ndcg_at_1000
+      value: 49.992
+    - type: ndcg_at_3
+      value: 36.114000000000004
+    - type: ndcg_at_5
+      value: 38.675
+    - type: precision_at_1
+      value: 31.225
+    - type: precision_at_10
+      value: 7.904999999999999
+    - type: precision_at_100
+      value: 1.5890000000000002
+    - type: precision_at_1000
+      value: 0.246
+    - type: precision_at_3
+      value: 16.535
+    - type: precision_at_5
+      value: 12.134
+    - type: recall_at_1
+      value: 26.495
+    - type: recall_at_10
+      value: 53.727000000000004
+    - type: recall_at_100
+      value: 79.34400000000001
+    - type: recall_at_1000
+      value: 94.35900000000001
+    - type: recall_at_3
+      value: 38.432
+    - type: recall_at_5
+      value: 45.050000000000004
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWordpressRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 21.235
+    - type: map_at_10
+      value: 28.725
+    - type: map_at_100
+      value: 29.774
+    - type: map_at_1000
+      value: 29.9
+    - type: map_at_3
+      value: 26.249
+    - type: map_at_5
+      value: 27.332
+    - type: mrr_at_1
+      value: 23.29
+    - type: mrr_at_10
+      value: 30.805
+    - type: mrr_at_100
+      value: 31.730000000000004
+    - type: mrr_at_1000
+      value: 31.822
+    - type: mrr_at_3
+      value: 28.558
+    - type: mrr_at_5
+      value: 29.529
+    - type: ndcg_at_1
+      value: 23.29
+    - type: ndcg_at_10
+      value: 33.337
+    - type: ndcg_at_100
+      value: 38.494
+    - type: ndcg_at_1000
+      value: 41.414
+    - type: ndcg_at_3
+      value: 28.433999999999997
+    - type: ndcg_at_5
+      value: 30.227999999999998
+    - type: precision_at_1
+      value: 23.29
+    - type: precision_at_10
+      value: 5.323
+    - type: precision_at_100
+      value: 0.8630000000000001
+    - type: precision_at_1000
+      value: 0.123
+    - type: precision_at_3
+      value: 12.138
+    - type: precision_at_5
+      value: 8.392
+    - type: recall_at_1
+      value: 21.235
+    - type: recall_at_10
+      value: 45.653
+    - type: recall_at_100
+      value: 69.486
+    - type: recall_at_1000
+      value: 90.91799999999999
+    - type: recall_at_3
+      value: 32.123000000000005
+    - type: recall_at_5
+      value: 36.479
+  - task:
+      type: Retrieval
+    dataset:
+      type: climate-fever
+      name: MTEB ClimateFEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 9.180000000000001
+    - type: map_at_10
+      value: 16.461000000000002
+    - type: map_at_100
+      value: 18.093999999999998
+    - type: map_at_1000
+      value: 18.297
+    - type: map_at_3
+      value: 13.475000000000001
+    - type: map_at_5
+      value: 15.02
+    - type: mrr_at_1
+      value: 21.303
+    - type: mrr_at_10
+      value: 31.755
+    - type: mrr_at_100
+      value: 32.826
+    - type: mrr_at_1000
+      value: 32.873000000000005
+    - type: mrr_at_3
+      value: 28.469
+    - type: mrr_at_5
+      value: 30.325999999999997
+    - type: ndcg_at_1
+      value: 21.303
+    - type: ndcg_at_10
+      value: 23.892
+    - type: ndcg_at_100
+      value: 30.848
+    - type: ndcg_at_1000
+      value: 34.577999999999996
+    - type: ndcg_at_3
+      value: 18.88
+    - type: ndcg_at_5
+      value: 20.683
+    - type: precision_at_1
+      value: 21.303
+    - type: precision_at_10
+      value: 7.693999999999999
+    - type: precision_at_100
+      value: 1.517
+    - type: precision_at_1000
+      value: 0.22
+    - type: precision_at_3
+      value: 14.180000000000001
+    - type: precision_at_5
+      value: 11.231
+    - type: recall_at_1
+      value: 9.180000000000001
+    - type: recall_at_10
+      value: 29.813000000000002
+    - type: recall_at_100
+      value: 54.116
+    - type: recall_at_1000
+      value: 75.248
+    - type: recall_at_3
+      value: 17.684
+    - type: recall_at_5
+      value: 22.557
+  - task:
+      type: Retrieval
+    dataset:
+      type: dbpedia-entity
+      name: MTEB DBPedia
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 8.508000000000001
+    - type: map_at_10
+      value: 16.39
+    - type: map_at_100
+      value: 21.981
+    - type: map_at_1000
+      value: 23.253
+    - type: map_at_3
+      value: 12.465
+    - type: map_at_5
+      value: 14.194999999999999
+    - type: mrr_at_1
+      value: 60.0
+    - type: mrr_at_10
+      value: 68.499
+    - type: mrr_at_100
+      value: 69.014
+    - type: mrr_at_1000
+      value: 69.024
+    - type: mrr_at_3
+      value: 66.625
+    - type: mrr_at_5
+      value: 67.887
+    - type: ndcg_at_1
+      value: 48.5
+    - type: ndcg_at_10
+      value: 34.870000000000005
+    - type: ndcg_at_100
+      value: 38.448
+    - type: ndcg_at_1000
+      value: 45.668
+    - type: ndcg_at_3
+      value: 39.931
+    - type: ndcg_at_5
+      value: 37.007
+    - type: precision_at_1
+      value: 60.0
+    - type: precision_at_10
+      value: 26.924999999999997
+    - type: precision_at_100
+      value: 8.358
+    - type: precision_at_1000
+      value: 1.7850000000000001
+    - type: precision_at_3
+      value: 43.0
+    - type: precision_at_5
+      value: 35.449999999999996
+    - type: recall_at_1
+      value: 8.508000000000001
+    - type: recall_at_10
+      value: 21.089
+    - type: recall_at_100
+      value: 43.146
+    - type: recall_at_1000
+      value: 66.776
+    - type: recall_at_3
+      value: 13.33
+    - type: recall_at_5
+      value: 16.225
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/emotion
+      name: MTEB EmotionClassification
+      config: default
+      split: test
+      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
+    metrics:
+    - type: accuracy
+      value: 46.735
+    - type: f1
+      value: 42.30853263256299
+  - task:
+      type: Retrieval
+    dataset:
+      type: fever
+      name: MTEB FEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 54.54
+    - type: map_at_10
+      value: 65.24600000000001
+    - type: map_at_100
+      value: 65.69
+    - type: map_at_1000
+      value: 65.71000000000001
+    - type: map_at_3
+      value: 63.234
+    - type: map_at_5
+      value: 64.455
+    - type: mrr_at_1
+      value: 58.821
+    - type: mrr_at_10
+      value: 69.616
+    - type: mrr_at_100
+      value: 69.98
+    - type: mrr_at_1000
+      value: 69.992
+    - type: mrr_at_3
+      value: 67.782
+    - type: mrr_at_5
+      value: 68.917
+    - type: ndcg_at_1
+      value: 58.821
+    - type: ndcg_at_10
+      value: 70.798
+    - type: ndcg_at_100
+      value: 72.719
+    - type: ndcg_at_1000
+      value: 73.19600000000001
+    - type: ndcg_at_3
+      value: 67.037
+    - type: ndcg_at_5
+      value: 69.048
+    - type: precision_at_1
+      value: 58.821
+    - type: precision_at_10
+      value: 9.182
+    - type: precision_at_100
+      value: 1.024
+    - type: precision_at_1000
+      value: 0.108
+    - type: precision_at_3
+      value: 26.662999999999997
+    - type: precision_at_5
+      value: 17.159
+    - type: recall_at_1
+      value: 54.54
+    - type: recall_at_10
+      value: 83.67999999999999
+    - type: recall_at_100
+      value: 92.099
+    - type: recall_at_1000
+      value: 95.532
+    - type: recall_at_3
+      value: 73.478
+    - type: recall_at_5
+      value: 78.424
+  - task:
+      type: Retrieval
+    dataset:
+      type: fiqa
+      name: MTEB FiQA2018
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 17.601
+    - type: map_at_10
+      value: 28.676000000000002
+    - type: map_at_100
+      value: 30.463
+    - type: map_at_1000
+      value: 30.666
+    - type: map_at_3
+      value: 24.734
+    - type: map_at_5
+      value: 27.026
+    - type: mrr_at_1
+      value: 34.259
+    - type: mrr_at_10
+      value: 43.613
+    - type: mrr_at_100
+      value: 44.535000000000004
+    - type: mrr_at_1000
+      value: 44.583
+    - type: mrr_at_3
+      value: 41.307
+    - type: mrr_at_5
+      value: 42.626
+    - type: ndcg_at_1
+      value: 34.259
+    - type: ndcg_at_10
+      value: 36.097
+    - type: ndcg_at_100
+      value: 43.039
+    - type: ndcg_at_1000
+      value: 46.498
+    - type: ndcg_at_3
+      value: 32.244
+    - type: ndcg_at_5
+      value: 33.711999999999996
+    - type: precision_at_1
+      value: 34.259
+    - type: precision_at_10
+      value: 10.030999999999999
+    - type: precision_at_100
+      value: 1.7239999999999998
+    - type: precision_at_1000
+      value: 0.234
+    - type: precision_at_3
+      value: 21.193
+    - type: precision_at_5
+      value: 15.956999999999999
+    - type: recall_at_1
+      value: 17.601
+    - type: recall_at_10
+      value: 42.807
+    - type: recall_at_100
+      value: 68.571
+    - type: recall_at_1000
+      value: 89.237
+    - type: recall_at_3
+      value: 29.301
+    - type: recall_at_5
+      value: 35.528999999999996
+  - task:
+      type: Retrieval
+    dataset:
+      type: hotpotqa
+      name: MTEB HotpotQA
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 31.182
+    - type: map_at_10
+      value: 42.631
+    - type: map_at_100
+      value: 43.577
+    - type: map_at_1000
+      value: 43.661
+    - type: map_at_3
+      value: 40.06
+    - type: map_at_5
+      value: 41.591
+    - type: mrr_at_1
+      value: 62.363
+    - type: mrr_at_10
+      value: 69.047
+    - type: mrr_at_100
+      value: 69.46
+    - type: mrr_at_1000
+      value: 69.48100000000001
+    - type: mrr_at_3
+      value: 67.574
+    - type: mrr_at_5
+      value: 68.487
+    - type: ndcg_at_1
+      value: 62.363
+    - type: ndcg_at_10
+      value: 51.629999999999995
+    - type: ndcg_at_100
+      value: 55.301
+    - type: ndcg_at_1000
+      value: 57.071000000000005
+    - type: ndcg_at_3
+      value: 47.496
+    - type: ndcg_at_5
+      value: 49.687
+    - type: precision_at_1
+      value: 62.363
+    - type: precision_at_10
+      value: 10.628
+    - type: precision_at_100
+      value: 1.352
+    - type: precision_at_1000
+      value: 0.159
+    - type: precision_at_3
+      value: 29.296
+    - type: precision_at_5
+      value: 19.309
+    - type: recall_at_1
+      value: 31.182
+    - type: recall_at_10
+      value: 53.14
+    - type: recall_at_100
+      value: 67.596
+    - type: recall_at_1000
+      value: 79.372
+    - type: recall_at_3
+      value: 43.943
+    - type: recall_at_5
+      value: 48.271
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/imdb
+      name: MTEB ImdbClassification
+      config: default
+      split: test
+      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
+    metrics:
+    - type: accuracy
+      value: 71.55319999999999
+    - type: ap
+      value: 65.44170899953346
+    - type: f1
+      value: 71.33420141354401
+  - task:
+      type: Retrieval
+    dataset:
+      type: msmarco
+      name: MTEB MSMARCO
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 18.89
+    - type: map_at_10
+      value: 30.076999999999998
+    - type: map_at_100
+      value: 31.281
+    - type: map_at_1000
+      value: 31.341
+    - type: map_at_3
+      value: 26.391
+    - type: map_at_5
+      value: 28.557
+    - type: mrr_at_1
+      value: 19.312
+    - type: mrr_at_10
+      value: 30.566
+    - type: mrr_at_100
+      value: 31.728
+    - type: mrr_at_1000
+      value: 31.781
+    - type: mrr_at_3
+      value: 26.901000000000003
+    - type: mrr_at_5
+      value: 29.072
+    - type: ndcg_at_1
+      value: 19.326999999999998
+    - type: ndcg_at_10
+      value: 36.516999999999996
+    - type: ndcg_at_100
+      value: 42.458
+    - type: ndcg_at_1000
+      value: 43.99
+    - type: ndcg_at_3
+      value: 29.005
+    - type: ndcg_at_5
+      value: 32.889
+    - type: precision_at_1
+      value: 19.326999999999998
+    - type: precision_at_10
+      value: 5.868
+    - type: precision_at_100
+      value: 0.8880000000000001
+    - type: precision_at_1000
+      value: 0.10200000000000001
+    - type: precision_at_3
+      value: 12.388
+    - type: precision_at_5
+      value: 9.401
+    - type: recall_at_1
+      value: 18.89
+    - type: recall_at_10
+      value: 56.442
+    - type: recall_at_100
+      value: 84.16
+    - type: recall_at_1000
+      value: 95.97099999999999
+    - type: recall_at_3
+      value: 36.077999999999996
+    - type: recall_at_5
+      value: 45.395
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_domain
+      name: MTEB MTOPDomainClassification (en)
+      config: en
+      split: test
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+    metrics:
+    - type: accuracy
+      value: 93.69585043319653
+    - type: f1
+      value: 93.27706251110098
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_intent
+      name: MTEB MTOPIntentClassification (en)
+      config: en
+      split: test
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+    metrics:
+    - type: accuracy
+      value: 74.62836297309622
+    - type: f1
+      value: 56.21163652384411
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (en)
+      config: en
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 71.37861466039006
+    - type: f1
+      value: 69.85338860172736
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (en)
+      config: en
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 75.58170813718897
+    - type: f1
+      value: 75.77358464349743
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-p2p
+      name: MTEB MedrxivClusteringP2P
+      config: default
+      split: test
+      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
+    metrics:
+    - type: v_measure
+      value: 33.29659845527655
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-s2s
+      name: MTEB MedrxivClusteringS2S
+      config: default
+      split: test
+      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
+    metrics:
+    - type: v_measure
+      value: 29.97507851301835
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/mind_small
+      name: MTEB MindSmallReranking
+      config: default
+      split: test
+      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
+    metrics:
+    - type: map
+      value: 31.158968289313076
+    - type: mrr
+      value: 32.27027446726339
+  - task:
+      type: Retrieval
+    dataset:
+      type: nfcorpus
+      name: MTEB NFCorpus
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 5.021
+    - type: map_at_10
+      value: 11.346
+    - type: map_at_100
+      value: 14.457
+    - type: map_at_1000
+      value: 15.875
+    - type: map_at_3
+      value: 8.376999999999999
+    - type: map_at_5
+      value: 9.793000000000001
+    - type: mrr_at_1
+      value: 43.344
+    - type: mrr_at_10
+      value: 51.266
+    - type: mrr_at_100
+      value: 51.871
+    - type: mrr_at_1000
+      value: 51.915
+    - type: mrr_at_3
+      value: 49.174
+    - type: mrr_at_5
+      value: 50.475
+    - type: ndcg_at_1
+      value: 41.331
+    - type: ndcg_at_10
+      value: 31.257
+    - type: ndcg_at_100
+      value: 29.264000000000003
+    - type: ndcg_at_1000
+      value: 38.024
+    - type: ndcg_at_3
+      value: 36.643
+    - type: ndcg_at_5
+      value: 34.808
+    - type: precision_at_1
+      value: 43.034
+    - type: precision_at_10
+      value: 22.972
+    - type: precision_at_100
+      value: 7.576
+    - type: precision_at_1000
+      value: 2.0629999999999997
+    - type: precision_at_3
+      value: 34.572
+    - type: precision_at_5
+      value: 30.341
+    - type: recall_at_1
+      value: 5.021
+    - type: recall_at_10
+      value: 15.197
+    - type: recall_at_100
+      value: 30.874000000000002
+    - type: recall_at_1000
+      value: 61.934
+    - type: recall_at_3
+      value: 9.467
+    - type: recall_at_5
+      value: 11.904
+  - task:
+      type: Retrieval
+    dataset:
+      type: nq
+      name: MTEB NQ
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 24.468999999999998
+    - type: map_at_10
+      value: 38.885999999999996
+    - type: map_at_100
+      value: 40.154
+    - type: map_at_1000
+      value: 40.195
+    - type: map_at_3
+      value: 34.565
+    - type: map_at_5
+      value: 37.069
+    - type: mrr_at_1
+      value: 27.578000000000003
+    - type: mrr_at_10
+      value: 41.079
+    - type: mrr_at_100
+      value: 42.081
+    - type: mrr_at_1000
+      value: 42.109
+    - type: mrr_at_3
+      value: 37.278
+    - type: mrr_at_5
+      value: 39.585
+    - type: ndcg_at_1
+      value: 27.549
+    - type: ndcg_at_10
+      value: 46.506
+    - type: ndcg_at_100
+      value: 51.92400000000001
+    - type: ndcg_at_1000
+      value: 52.833
+    - type: ndcg_at_3
+      value: 38.214999999999996
+    - type: ndcg_at_5
+      value: 42.498000000000005
+    - type: precision_at_1
+      value: 27.549
+    - type: precision_at_10
+      value: 8.019
+    - type: precision_at_100
+      value: 1.103
+    - type: precision_at_1000
+      value: 0.11900000000000001
+    - type: precision_at_3
+      value: 17.806
+    - type: precision_at_5
+      value: 13.100000000000001
+    - type: recall_at_1
+      value: 24.468999999999998
+    - type: recall_at_10
+      value: 67.632
+    - type: recall_at_100
+      value: 91.169
+    - type: recall_at_1000
+      value: 97.851
+    - type: recall_at_3
+      value: 46.043
+    - type: recall_at_5
+      value: 55.962999999999994
+  - task:
+      type: Retrieval
+    dataset:
+      type: quora
+      name: MTEB QuoraRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 70.44
+    - type: map_at_10
+      value: 84.209
+    - type: map_at_100
+      value: 84.868
+    - type: map_at_1000
+      value: 84.884
+    - type: map_at_3
+      value: 81.192
+    - type: map_at_5
+      value: 83.06099999999999
+    - type: mrr_at_1
+      value: 81.12
+    - type: mrr_at_10
+      value: 87.30499999999999
+    - type: mrr_at_100
+      value: 87.413
+    - type: mrr_at_1000
+      value: 87.414
+    - type: mrr_at_3
+      value: 86.337
+    - type: mrr_at_5
+      value: 86.985
+    - type: ndcg_at_1
+      value: 81.15
+    - type: ndcg_at_10
+      value: 88.032
+    - type: ndcg_at_100
+      value: 89.292
+    - type: ndcg_at_1000
+      value: 89.393
+    - type: ndcg_at_3
+      value: 85.098
+    - type: ndcg_at_5
+      value: 86.691
+    - type: precision_at_1
+      value: 81.15
+    - type: precision_at_10
+      value: 13.395999999999999
+    - type: precision_at_100
+      value: 1.5310000000000001
+    - type: precision_at_1000
+      value: 0.157
+    - type: precision_at_3
+      value: 37.16
+    - type: precision_at_5
+      value: 24.458
+    - type: recall_at_1
+      value: 70.44
+    - type: recall_at_10
+      value: 95.204
+    - type: recall_at_100
+      value: 99.506
+    - type: recall_at_1000
+      value: 99.978
+    - type: recall_at_3
+      value: 86.83999999999999
+    - type: recall_at_5
+      value: 91.328
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering
+      name: MTEB RedditClustering
+      config: default
+      split: test
+      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
+    metrics:
+    - type: v_measure
+      value: 44.091918771223966
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering-p2p
+      name: MTEB RedditClusteringP2P
+      config: default
+      split: test
+      revision: 282350215ef01743dc01b456c7f5241fa8937f16
+    metrics:
+    - type: v_measure
+      value: 49.3850718319815
+  - task:
+      type: Retrieval
+    dataset:
+      type: scidocs
+      name: MTEB SCIDOCS
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 5.108
+    - type: map_at_10
+      value: 12.878
+    - type: map_at_100
+      value: 15.398
+    - type: map_at_1000
+      value: 15.762
+    - type: map_at_3
+      value: 9.028
+    - type: map_at_5
+      value: 10.886
+    - type: mrr_at_1
+      value: 25.2
+    - type: mrr_at_10
+      value: 36.051
+    - type: mrr_at_100
+      value: 37.198
+    - type: mrr_at_1000
+      value: 37.254
+    - type: mrr_at_3
+      value: 32.483000000000004
+    - type: mrr_at_5
+      value: 34.583000000000006
+    - type: ndcg_at_1
+      value: 25.2
+    - type: ndcg_at_10
+      value: 21.436
+    - type: ndcg_at_100
+      value: 30.758000000000003
+    - type: ndcg_at_1000
+      value: 36.774
+    - type: ndcg_at_3
+      value: 19.977
+    - type: ndcg_at_5
+      value: 17.634
+    - type: precision_at_1
+      value: 25.2
+    - type: precision_at_10
+      value: 11.16
+    - type: precision_at_100
+      value: 2.46
+    - type: precision_at_1000
+      value: 0.38999999999999996
+    - type: precision_at_3
+      value: 18.4
+    - type: precision_at_5
+      value: 15.440000000000001
+    - type: recall_at_1
+      value: 5.108
+    - type: recall_at_10
+      value: 22.615
+    - type: recall_at_100
+      value: 49.838
+    - type: recall_at_1000
+      value: 79.12700000000001
+    - type: recall_at_3
+      value: 11.203000000000001
+    - type: recall_at_5
+      value: 15.638
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sickr-sts
+      name: MTEB SICK-R
+      config: default
+      split: test
+      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
+    metrics:
+    - type: cos_sim_pearson
+      value: 84.87907802108278
+    - type: cos_sim_spearman
+      value: 78.47745630820519
+    - type: euclidean_pearson
+      value: 81.24598854050433
+    - type: euclidean_spearman
+      value: 76.49536405466311
+    - type: manhattan_pearson
+      value: 81.2143517198192
+    - type: manhattan_spearman
+      value: 76.41735187637899
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts12-sts
+      name: MTEB STS12
+      config: default
+      split: test
+      revision: a0d554a64d88156834ff5ae9920b964011b16384
+    metrics:
+    - type: cos_sim_pearson
+      value: 84.72222146895906
+    - type: cos_sim_spearman
+      value: 75.78345138703104
+    - type: euclidean_pearson
+      value: 81.35072741369821
+    - type: euclidean_spearman
+      value: 71.44372390021385
+    - type: manhattan_pearson
+      value: 81.42777992212991
+    - type: manhattan_spearman
+      value: 71.50748732911025
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts13-sts
+      name: MTEB STS13
+      config: default
+      split: test
+      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
+    metrics:
+    - type: cos_sim_pearson
+      value: 82.46314178714782
+    - type: cos_sim_spearman
+      value: 83.30487501773337
+    - type: euclidean_pearson
+      value: 81.97496753880277
+    - type: euclidean_spearman
+      value: 83.26569157819903
+    - type: manhattan_pearson
+      value: 81.95087299528338
+    - type: manhattan_spearman
+      value: 83.25657383286989
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts14-sts
+      name: MTEB STS14
+      config: default
+      split: test
+      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
+    metrics:
+    - type: cos_sim_pearson
+      value: 82.38192118423038
+    - type: cos_sim_spearman
+      value: 78.40410104736917
+    - type: euclidean_pearson
+      value: 79.48941144435967
+    - type: euclidean_spearman
+      value: 76.87243228899331
+    - type: manhattan_pearson
+      value: 79.37383745954276
+    - type: manhattan_spearman
+      value: 76.81624170740595
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts15-sts
+      name: MTEB STS15
+      config: default
+      split: test
+      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
+    metrics:
+    - type: cos_sim_pearson
+      value: 84.89499997364136
+    - type: cos_sim_spearman
+      value: 86.49722400765071
+    - type: euclidean_pearson
+      value: 80.83327622391033
+    - type: euclidean_spearman
+      value: 81.77906221038033
+    - type: manhattan_pearson
+      value: 80.68927444298423
+    - type: manhattan_spearman
+      value: 81.67585996918764
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts16-sts
+      name: MTEB STS16
+      config: default
+      split: test
+      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
+    metrics:
+    - type: cos_sim_pearson
+      value: 80.85434430333662
+    - type: cos_sim_spearman
+      value: 82.32641704038703
+    - type: euclidean_pearson
+      value: 78.92319495883405
+    - type: euclidean_spearman
+      value: 80.06748121443441
+    - type: manhattan_pearson
+      value: 78.68188267117745
+    - type: manhattan_spearman
+      value: 79.72019793896195
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts17-crosslingual-sts
+      name: MTEB STS17 (en-en)
+      config: en-en
+      split: test
+      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
+    metrics:
+    - type: cos_sim_pearson
+      value: 87.0896689258414
+    - type: cos_sim_spearman
+      value: 87.31114069713735
+    - type: euclidean_pearson
+      value: 83.93671908621272
+    - type: euclidean_spearman
+      value: 82.83918654090873
+    - type: manhattan_pearson
+      value: 83.5943550673816
+    - type: manhattan_spearman
+      value: 82.47327946394148
+  - 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: 66.4799391480602
+    - type: cos_sim_spearman
+      value: 66.59141182659532
+    - type: euclidean_pearson
+      value: 45.85714541149068
+    - type: euclidean_spearman
+      value: 61.605252732946404
+    - type: manhattan_pearson
+      value: 46.69415667711241
+    - type: manhattan_spearman
+      value: 61.38490967409539
+  - task:
+      type: STS
+    dataset:
+      type: mteb/stsbenchmark-sts
+      name: MTEB STSBenchmark
+      config: default
+      split: test
+      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
+    metrics:
+    - type: cos_sim_pearson
+      value: 82.22064334651283
+    - type: cos_sim_spearman
+      value: 84.23556405551305
+    - type: euclidean_pearson
+      value: 80.64484589022672
+    - type: euclidean_spearman
+      value: 80.27585966983669
+    - type: manhattan_pearson
+      value: 80.44248540454653
+    - type: manhattan_spearman
+      value: 80.06071452831723
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/scidocs-reranking
+      name: MTEB SciDocsRR
+      config: default
+      split: test
+      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
+    metrics:
+    - type: map
+      value: 86.82632940766443
+    - type: mrr
+      value: 96.27367186190715
+  - task:
+      type: Retrieval
+    dataset:
+      type: scifact
+      name: MTEB SciFact
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 48.443999999999996
+    - type: map_at_10
+      value: 58.309
+    - type: map_at_100
+      value: 59.116
+    - type: map_at_1000
+      value: 59.155
+    - type: map_at_3
+      value: 55.598000000000006
+    - type: map_at_5
+      value: 57.550999999999995
+    - type: mrr_at_1
+      value: 50.666999999999994
+    - type: mrr_at_10
+      value: 59.099000000000004
+    - type: mrr_at_100
+      value: 59.843
+    - type: mrr_at_1000
+      value: 59.879000000000005
+    - type: mrr_at_3
+      value: 57.167
+    - type: mrr_at_5
+      value: 58.5
+    - type: ndcg_at_1
+      value: 50.666999999999994
+    - type: ndcg_at_10
+      value: 62.483999999999995
+    - type: ndcg_at_100
+      value: 66.131
+    - type: ndcg_at_1000
+      value: 67.17
+    - type: ndcg_at_3
+      value: 58.07299999999999
+    - type: ndcg_at_5
+      value: 60.87200000000001
+    - type: precision_at_1
+      value: 50.666999999999994
+    - type: precision_at_10
+      value: 8.4
+    - type: precision_at_100
+      value: 1.0330000000000001
+    - type: precision_at_1000
+      value: 0.11199999999999999
+    - type: precision_at_3
+      value: 22.889
+    - type: precision_at_5
+      value: 15.467
+    - type: recall_at_1
+      value: 48.443999999999996
+    - type: recall_at_10
+      value: 74.26700000000001
+    - type: recall_at_100
+      value: 90.5
+    - type: recall_at_1000
+      value: 98.667
+    - type: recall_at_3
+      value: 63.039
+    - type: recall_at_5
+      value: 69.706
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/sprintduplicatequestions-pairclassification
+      name: MTEB SprintDuplicateQuestions
+      config: default
+      split: test
+      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
+    metrics:
+    - type: cos_sim_accuracy
+      value: 99.76336633663367
+    - type: cos_sim_ap
+      value: 94.05677361006421
+    - type: cos_sim_f1
+      value: 87.85894206549118
+    - type: cos_sim_precision
+      value: 88.52791878172589
+    - type: cos_sim_recall
+      value: 87.2
+    - type: dot_accuracy
+      value: 99.06732673267327
+    - type: dot_ap
+      value: 25.234902506145275
+    - type: dot_f1
+      value: 31.687715269804816
+    - type: dot_precision
+      value: 37.19676549865229
+    - type: dot_recall
+      value: 27.6
+    - type: euclidean_accuracy
+      value: 99.73861386138614
+    - type: euclidean_ap
+      value: 92.39504711224613
+    - type: euclidean_f1
+      value: 86.40576725025747
+    - type: euclidean_precision
+      value: 89.06581740976645
+    - type: euclidean_recall
+      value: 83.89999999999999
+    - type: manhattan_accuracy
+      value: 99.74455445544554
+    - type: manhattan_ap
+      value: 92.5050066340186
+    - type: manhattan_f1
+      value: 86.67355371900827
+    - type: manhattan_precision
+      value: 89.63675213675214
+    - type: manhattan_recall
+      value: 83.89999999999999
+    - type: max_accuracy
+      value: 99.76336633663367
+    - type: max_ap
+      value: 94.05677361006421
+    - type: max_f1
+      value: 87.85894206549118
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering
+      name: MTEB StackExchangeClustering
+      config: default
+      split: test
+      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
+    metrics:
+    - type: v_measure
+      value: 52.66315650755836
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering-p2p
+      name: MTEB StackExchangeClusteringP2P
+      config: default
+      split: test
+      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
+    metrics:
+    - type: v_measure
+      value: 32.36019149648443
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/stackoverflowdupquestions-reranking
+      name: MTEB StackOverflowDupQuestions
+      config: default
+      split: test
+      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
+    metrics:
+    - type: map
+      value: 50.10933600138655
+    - type: mrr
+      value: 50.84273671589848
+  - task:
+      type: Summarization
+    dataset:
+      type: mteb/summeval
+      name: MTEB SummEval
+      config: default
+      split: test
+      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
+    metrics:
+    - type: cos_sim_pearson
+      value: 30.342194052503917
+    - type: cos_sim_spearman
+      value: 30.74326118928312
+    - type: dot_pearson
+      value: 12.329727800033176
+    - type: dot_spearman
+      value: 14.54557726626662
+  - task:
+      type: Retrieval
+    dataset:
+      type: trec-covid
+      name: MTEB TRECCOVID
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 0.173
+    - type: map_at_10
+      value: 1.1320000000000001
+    - type: map_at_100
+      value: 5.885
+    - type: map_at_1000
+      value: 14.762
+    - type: map_at_3
+      value: 0.443
+    - type: map_at_5
+      value: 0.66
+    - type: mrr_at_1
+      value: 66.0
+    - type: mrr_at_10
+      value: 76.34100000000001
+    - type: mrr_at_100
+      value: 76.37
+    - type: mrr_at_1000
+      value: 76.376
+    - type: mrr_at_3
+      value: 74.667
+    - type: mrr_at_5
+      value: 74.667
+    - type: ndcg_at_1
+      value: 59.0
+    - type: ndcg_at_10
+      value: 50.047
+    - type: ndcg_at_100
+      value: 37.744
+    - type: ndcg_at_1000
+      value: 35.903
+    - type: ndcg_at_3
+      value: 55.95
+    - type: ndcg_at_5
+      value: 53.379
+    - type: precision_at_1
+      value: 66.0
+    - type: precision_at_10
+      value: 53.0
+    - type: precision_at_100
+      value: 38.78
+    - type: precision_at_1000
+      value: 16.24
+    - type: precision_at_3
+      value: 60.0
+    - type: precision_at_5
+      value: 56.39999999999999
+    - type: recall_at_1
+      value: 0.173
+    - type: recall_at_10
+      value: 1.379
+    - type: recall_at_100
+      value: 9.196
+    - type: recall_at_1000
+      value: 34.488
+    - type: recall_at_3
+      value: 0.475
+    - type: recall_at_5
+      value: 0.738
+  - task:
+      type: Retrieval
+    dataset:
+      type: webis-touche2020
+      name: MTEB Touche2020
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 2.1260000000000003
+    - type: map_at_10
+      value: 7.216
+    - type: map_at_100
+      value: 12.732
+    - type: map_at_1000
+      value: 14.158999999999999
+    - type: map_at_3
+      value: 3.9530000000000003
+    - type: map_at_5
+      value: 5.252
+    - type: mrr_at_1
+      value: 24.490000000000002
+    - type: mrr_at_10
+      value: 36.949
+    - type: mrr_at_100
+      value: 38.0
+    - type: mrr_at_1000
+      value: 38.0
+    - type: mrr_at_3
+      value: 31.973000000000003
+    - type: mrr_at_5
+      value: 34.32
+    - type: ndcg_at_1
+      value: 19.387999999999998
+    - type: ndcg_at_10
+      value: 17.918
+    - type: ndcg_at_100
+      value: 30.558999999999997
+    - type: ndcg_at_1000
+      value: 42.028
+    - type: ndcg_at_3
+      value: 17.202
+    - type: ndcg_at_5
+      value: 17.788
+    - type: precision_at_1
+      value: 24.490000000000002
+    - type: precision_at_10
+      value: 17.347
+    - type: precision_at_100
+      value: 6.918
+    - type: precision_at_1000
+      value: 1.4569999999999999
+    - type: precision_at_3
+      value: 19.728
+    - type: precision_at_5
+      value: 19.592000000000002
+    - type: recall_at_1
+      value: 2.1260000000000003
+    - type: recall_at_10
+      value: 12.897
+    - type: recall_at_100
+      value: 42.632999999999996
+    - type: recall_at_1000
+      value: 77.783
+    - type: recall_at_3
+      value: 4.836
+    - type: recall_at_5
+      value: 7.331
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/toxic_conversations_50k
+      name: MTEB ToxicConversationsClassification
+      config: default
+      split: test
+      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
+    metrics:
+    - type: accuracy
+      value: 70.9516
+    - type: ap
+      value: 14.148097836321893
+    - type: f1
+      value: 54.52189833022899
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/tweet_sentiment_extraction
+      name: MTEB TweetSentimentExtractionClassification
+      config: default
+      split: test
+      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
+    metrics:
+    - type: accuracy
+      value: 58.33899264289756
+    - type: f1
+      value: 58.684516042056565
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/twentynewsgroups-clustering
+      name: MTEB TwentyNewsgroupsClustering
+      config: default
+      split: test
+      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
+    metrics:
+    - type: v_measure
+      value: 41.45569187892743
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twittersemeval2015-pairclassification
+      name: MTEB TwitterSemEval2015
+      config: default
+      split: test
+      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
+    metrics:
+    - type: cos_sim_accuracy
+      value: 85.05692316862371
+    - type: cos_sim_ap
+      value: 70.54785019750204
+    - type: cos_sim_f1
+      value: 65.99060103883255
+    - type: cos_sim_precision
+      value: 62.10428305400373
+    - type: cos_sim_recall
+      value: 70.3957783641161
+    - type: dot_accuracy
+      value: 77.82678667222984
+    - type: dot_ap
+      value: 32.73452779849359
+    - type: dot_f1
+      value: 38.1269911832259
+    - type: dot_precision
+      value: 26.5066446893994
+    - type: dot_recall
+      value: 67.8891820580475
+    - type: euclidean_accuracy
+      value: 84.62180365977231
+    - type: euclidean_ap
+      value: 68.57434108453688
+    - type: euclidean_f1
+      value: 65.23069391751316
+    - type: euclidean_precision
+      value: 60.83086053412463
+    - type: euclidean_recall
+      value: 70.31662269129288
+    - type: manhattan_accuracy
+      value: 84.57411933003517
+    - type: manhattan_ap
+      value: 68.3530821550187
+    - type: manhattan_f1
+      value: 64.74820143884892
+    - type: manhattan_precision
+      value: 61.09550561797753
+    - type: manhattan_recall
+      value: 68.86543535620054
+    - type: max_accuracy
+      value: 85.05692316862371
+    - type: max_ap
+      value: 70.54785019750204
+    - type: max_f1
+      value: 65.99060103883255
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twitterurlcorpus-pairclassification
+      name: MTEB TwitterURLCorpus
+      config: default
+      split: test
+      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
+    metrics:
+    - type: cos_sim_accuracy
+      value: 88.77440136608841
+    - type: cos_sim_ap
+      value: 85.6224854550336
+    - type: cos_sim_f1
+      value: 77.76333865518139
+    - type: cos_sim_precision
+      value: 75.09501613481535
+    - type: cos_sim_recall
+      value: 80.6282722513089
+    - type: dot_accuracy
+      value: 79.73570846431483
+    - type: dot_ap
+      value: 59.509855217305315
+    - type: dot_f1
+      value: 57.20318336852364
+    - type: dot_precision
+      value: 49.474630555711634
+    - type: dot_recall
+      value: 67.79334770557438
+    - type: euclidean_accuracy
+      value: 87.06096945705748
+    - type: euclidean_ap
+      value: 81.65241378370953
+    - type: euclidean_f1
+      value: 73.29885784441386
+    - type: euclidean_precision
+      value: 70.91642070405298
+    - type: euclidean_recall
+      value: 75.8469356328919
+    - type: manhattan_accuracy
+      value: 86.973648465091
+    - type: manhattan_ap
+      value: 81.57560533116907
+    - type: manhattan_f1
+      value: 73.2408287397833
+    - type: manhattan_precision
+      value: 72.33611173687767
+    - type: manhattan_recall
+      value: 74.16846319679703
+    - type: max_accuracy
+      value: 88.77440136608841
+    - type: max_ap
+      value: 85.6224854550336
+    - type: max_f1
+      value: 77.76333865518139
 ---
+<h1 align="center">GIST Embedding v0 - all-MiniLM-L6-v2</h1>
+
+*GIST Embedding: Guided In-sample Selection of Training Negatives for Text Embedding*
+
+The model is fine-tuned on top of the [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) 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 details of the model will be published shortly.
+
+# Data
+
+The dataset used is a compilation of the MEDI dataset and the MTEB Classification training dataset. 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, which could have caused the observed performance degradation. Further work is currently being undertaken to validate this hypothesis.
+
+# 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 in  case the model is updated.
+
+model = SentenceTransformer("avsolatorio/GIST-all-MiniLM-L6-v2", 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
+```
+
+Specific training details and strategies will be published shortly.
+
+# Evaluation
+
+The model was evaluated using the [MTEB Evaluation](https://huggingface.co/mteb) suite.
+
+
+# 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.