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+---
+base_model: avsolatorio/GIST-all-MiniLM-L6-v2
+inference: true
+language:
+- en
+library_name: sentence-transformers
+license: mit
+model-index:
+- name: GIST-all-MiniLM-L6-v2
+  results:
+  - dataset:
+      config: en
+      name: MTEB AmazonCounterfactualClassification (en)
+      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
+      split: test
+      type: mteb/amazon_counterfactual
+    metrics:
+    - type: accuracy
+      value: 72.8955223880597
+    - type: ap
+      value: 35.447605103320775
+    - type: f1
+      value: 66.82951715365854
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB AmazonPolarityClassification
+      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
+      split: test
+      type: mteb/amazon_polarity
+    metrics:
+    - type: accuracy
+      value: 87.19474999999998
+    - type: ap
+      value: 83.09577890808514
+    - type: f1
+      value: 87.13833121762009
+    task:
+      type: Classification
+  - dataset:
+      config: en
+      name: MTEB AmazonReviewsClassification (en)
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+      split: test
+      type: mteb/amazon_reviews_multi
+    metrics:
+    - type: accuracy
+      value: 42.556000000000004
+    - type: f1
+      value: 42.236256693772276
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB ArguAna
+      revision: None
+      split: test
+      type: arguana
+    metrics:
+    - type: map_at_1
+      value: 26.884999999999998
+    - type: map_at_10
+      value: 42.364000000000004
+    - type: map_at_100
+      value: 43.382
+    - type: map_at_1000
+      value: 43.391000000000005
+    - type: map_at_3
+      value: 37.162
+    - type: map_at_5
+      value: 40.139
+    - type: mrr_at_1
+      value: 26.884999999999998
+    - type: mrr_at_10
+      value: 42.193999999999996
+    - type: mrr_at_100
+      value: 43.211
+    - type: mrr_at_1000
+      value: 43.221
+    - type: mrr_at_3
+      value: 36.949
+    - type: mrr_at_5
+      value: 40.004
+    - type: ndcg_at_1
+      value: 26.884999999999998
+    - type: ndcg_at_10
+      value: 51.254999999999995
+    - type: ndcg_at_100
+      value: 55.481
+    - type: ndcg_at_1000
+      value: 55.68300000000001
+    - type: ndcg_at_3
+      value: 40.565
+    - type: ndcg_at_5
+      value: 45.882
+    - type: precision_at_1
+      value: 26.884999999999998
+    - type: precision_at_10
+      value: 7.9799999999999995
+    - type: precision_at_100
+      value: 0.98
+    - type: precision_at_1000
+      value: 0.1
+    - type: precision_at_3
+      value: 16.808999999999997
+    - type: precision_at_5
+      value: 12.645999999999999
+    - type: recall_at_1
+      value: 26.884999999999998
+    - type: recall_at_10
+      value: 79.801
+    - type: recall_at_100
+      value: 98.009
+    - type: recall_at_1000
+      value: 99.502
+    - type: recall_at_3
+      value: 50.427
+    - type: recall_at_5
+      value: 63.229
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB ArxivClusteringP2P
+      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
+      split: test
+      type: mteb/arxiv-clustering-p2p
+    metrics:
+    - type: v_measure
+      value: 45.31044837358167
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB ArxivClusteringS2S
+      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
+      split: test
+      type: mteb/arxiv-clustering-s2s
+    metrics:
+    - type: v_measure
+      value: 35.44751738734691
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB AskUbuntuDupQuestions
+      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
+      split: test
+      type: mteb/askubuntudupquestions-reranking
+    metrics:
+    - type: map
+      value: 62.96517580629869
+    - type: mrr
+      value: 76.30051004704744
+    task:
+      type: Reranking
+  - dataset:
+      config: default
+      name: MTEB BIOSSES
+      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
+      split: test
+      type: mteb/biosses-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 83.97262600499639
+    - type: cos_sim_spearman
+      value: 81.25787561220484
+    - type: euclidean_pearson
+      value: 64.96260261677082
+    - type: euclidean_spearman
+      value: 64.17616109254686
+    - type: manhattan_pearson
+      value: 65.05620628102835
+    - type: manhattan_spearman
+      value: 64.71171546419122
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB Banking77Classification
+      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
+      split: test
+      type: mteb/banking77
+    metrics:
+    - type: accuracy
+      value: 84.2435064935065
+    - type: f1
+      value: 84.2334859253828
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB BiorxivClusteringP2P
+      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
+      split: test
+      type: mteb/biorxiv-clustering-p2p
+    metrics:
+    - type: v_measure
+      value: 38.38358435972693
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB BiorxivClusteringS2S
+      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
+      split: test
+      type: mteb/biorxiv-clustering-s2s
+    metrics:
+    - type: v_measure
+      value: 31.093619653843124
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB CQADupstackAndroidRetrieval
+      revision: None
+      split: test
+      type: BeIR/cqadupstack
+    metrics:
+    - type: map_at_1
+      value: 35.016999999999996
+    - type: map_at_10
+      value: 47.019
+    - type: map_at_100
+      value: 48.634
+    - type: map_at_1000
+      value: 48.757
+    - type: map_at_3
+      value: 43.372
+    - type: map_at_5
+      value: 45.314
+    - type: mrr_at_1
+      value: 43.491
+    - type: mrr_at_10
+      value: 53.284
+    - type: mrr_at_100
+      value: 54.038
+    - type: mrr_at_1000
+      value: 54.071000000000005
+    - type: mrr_at_3
+      value: 51.001
+    - type: mrr_at_5
+      value: 52.282
+    - type: ndcg_at_1
+      value: 43.491
+    - type: ndcg_at_10
+      value: 53.498999999999995
+    - type: ndcg_at_100
+      value: 58.733999999999995
+    - type: ndcg_at_1000
+      value: 60.307
+    - type: ndcg_at_3
+      value: 48.841
+    - type: ndcg_at_5
+      value: 50.76199999999999
+    - type: precision_at_1
+      value: 43.491
+    - type: precision_at_10
+      value: 10.315000000000001
+    - type: precision_at_100
+      value: 1.6209999999999998
+    - type: precision_at_1000
+      value: 0.20500000000000002
+    - type: precision_at_3
+      value: 23.462
+    - type: precision_at_5
+      value: 16.652
+    - type: recall_at_1
+      value: 35.016999999999996
+    - type: recall_at_10
+      value: 64.92
+    - type: recall_at_100
+      value: 86.605
+    - type: recall_at_1000
+      value: 96.174
+    - type: recall_at_3
+      value: 50.99
+    - type: recall_at_5
+      value: 56.93
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB CQADupstackEnglishRetrieval
+      revision: None
+      split: test
+      type: BeIR/cqadupstack
+    metrics:
+    - type: map_at_1
+      value: 29.866
+    - type: map_at_10
+      value: 40.438
+    - type: map_at_100
+      value: 41.77
+    - type: map_at_1000
+      value: 41.913
+    - type: map_at_3
+      value: 37.634
+    - type: map_at_5
+      value: 39.226
+    - type: mrr_at_1
+      value: 37.834
+    - type: mrr_at_10
+      value: 46.765
+    - type: mrr_at_100
+      value: 47.410000000000004
+    - type: mrr_at_1000
+      value: 47.461
+    - type: mrr_at_3
+      value: 44.735
+    - type: mrr_at_5
+      value: 46.028000000000006
+    - type: ndcg_at_1
+      value: 37.834
+    - type: ndcg_at_10
+      value: 46.303
+    - type: ndcg_at_100
+      value: 50.879
+    - type: ndcg_at_1000
+      value: 53.112
+    - type: ndcg_at_3
+      value: 42.601
+    - type: ndcg_at_5
+      value: 44.384
+    - type: precision_at_1
+      value: 37.834
+    - type: precision_at_10
+      value: 8.898
+    - type: precision_at_100
+      value: 1.4409999999999998
+    - type: precision_at_1000
+      value: 0.19499999999999998
+    - type: precision_at_3
+      value: 20.977
+    - type: precision_at_5
+      value: 14.841
+    - type: recall_at_1
+      value: 29.866
+    - type: recall_at_10
+      value: 56.06100000000001
+    - type: recall_at_100
+      value: 75.809
+    - type: recall_at_1000
+      value: 89.875
+    - type: recall_at_3
+      value: 44.707
+    - type: recall_at_5
+      value: 49.846000000000004
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB CQADupstackGamingRetrieval
+      revision: None
+      split: test
+      type: BeIR/cqadupstack
+    metrics:
+    - type: map_at_1
+      value: 38.985
+    - type: map_at_10
+      value: 51.165000000000006
+    - type: map_at_100
+      value: 52.17
+    - type: map_at_1000
+      value: 52.229000000000006
+    - type: map_at_3
+      value: 48.089999999999996
+    - type: map_at_5
+      value: 49.762
+    - type: mrr_at_1
+      value: 44.577
+    - type: mrr_at_10
+      value: 54.493
+    - type: mrr_at_100
+      value: 55.137
+    - type: mrr_at_1000
+      value: 55.167
+    - type: mrr_at_3
+      value: 52.079
+    - type: mrr_at_5
+      value: 53.518
+    - type: ndcg_at_1
+      value: 44.577
+    - type: ndcg_at_10
+      value: 56.825
+    - type: ndcg_at_100
+      value: 60.842
+    - type: ndcg_at_1000
+      value: 62.015
+    - type: ndcg_at_3
+      value: 51.699
+    - type: ndcg_at_5
+      value: 54.11
+    - type: precision_at_1
+      value: 44.577
+    - type: precision_at_10
+      value: 9.11
+    - type: precision_at_100
+      value: 1.206
+    - type: precision_at_1000
+      value: 0.135
+    - type: precision_at_3
+      value: 23.156
+    - type: precision_at_5
+      value: 15.737000000000002
+    - type: recall_at_1
+      value: 38.985
+    - type: recall_at_10
+      value: 70.164
+    - type: recall_at_100
+      value: 87.708
+    - type: recall_at_1000
+      value: 95.979
+    - type: recall_at_3
+      value: 56.285
+    - type: recall_at_5
+      value: 62.303
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB CQADupstackGisRetrieval
+      revision: None
+      split: test
+      type: BeIR/cqadupstack
+    metrics:
+    - type: map_at_1
+      value: 28.137
+    - type: map_at_10
+      value: 36.729
+    - type: map_at_100
+      value: 37.851
+    - type: map_at_1000
+      value: 37.932
+    - type: map_at_3
+      value: 34.074
+    - type: map_at_5
+      value: 35.398
+    - type: mrr_at_1
+      value: 30.621
+    - type: mrr_at_10
+      value: 39.007
+    - type: mrr_at_100
+      value: 39.961
+    - type: mrr_at_1000
+      value: 40.02
+    - type: mrr_at_3
+      value: 36.591
+    - type: mrr_at_5
+      value: 37.806
+    - type: ndcg_at_1
+      value: 30.621
+    - type: ndcg_at_10
+      value: 41.772
+    - type: ndcg_at_100
+      value: 47.181
+    - type: ndcg_at_1000
+      value: 49.053999999999995
+    - type: ndcg_at_3
+      value: 36.577
+    - type: ndcg_at_5
+      value: 38.777
+    - type: precision_at_1
+      value: 30.621
+    - type: precision_at_10
+      value: 6.372999999999999
+    - type: precision_at_100
+      value: 0.955
+    - type: precision_at_1000
+      value: 0.11499999999999999
+    - type: precision_at_3
+      value: 15.367
+    - type: precision_at_5
+      value: 10.531
+    - type: recall_at_1
+      value: 28.137
+    - type: recall_at_10
+      value: 55.162
+    - type: recall_at_100
+      value: 79.931
+    - type: recall_at_1000
+      value: 93.67
+    - type: recall_at_3
+      value: 41.057
+    - type: recall_at_5
+      value: 46.327
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB CQADupstackMathematicaRetrieval
+      revision: None
+      split: test
+      type: BeIR/cqadupstack
+    metrics:
+    - type: map_at_1
+      value: 16.798
+    - type: map_at_10
+      value: 25.267
+    - type: map_at_100
+      value: 26.579000000000004
+    - type: map_at_1000
+      value: 26.697
+    - type: map_at_3
+      value: 22.456
+    - type: map_at_5
+      value: 23.912
+    - type: mrr_at_1
+      value: 20.771
+    - type: mrr_at_10
+      value: 29.843999999999998
+    - type: mrr_at_100
+      value: 30.849
+    - type: mrr_at_1000
+      value: 30.916
+    - type: mrr_at_3
+      value: 27.156000000000002
+    - type: mrr_at_5
+      value: 28.518
+    - type: ndcg_at_1
+      value: 20.771
+    - type: ndcg_at_10
+      value: 30.792
+    - type: ndcg_at_100
+      value: 36.945
+    - type: ndcg_at_1000
+      value: 39.619
+    - type: ndcg_at_3
+      value: 25.52
+    - type: ndcg_at_5
+      value: 27.776
+    - type: precision_at_1
+      value: 20.771
+    - type: precision_at_10
+      value: 5.734
+    - type: precision_at_100
+      value: 1.031
+    - type: precision_at_1000
+      value: 0.13899999999999998
+    - type: precision_at_3
+      value: 12.148
+    - type: precision_at_5
+      value: 9.055
+    - type: recall_at_1
+      value: 16.798
+    - type: recall_at_10
+      value: 43.332
+    - type: recall_at_100
+      value: 70.016
+    - type: recall_at_1000
+      value: 88.90400000000001
+    - type: recall_at_3
+      value: 28.842000000000002
+    - type: recall_at_5
+      value: 34.37
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB CQADupstackPhysicsRetrieval
+      revision: None
+      split: test
+      type: BeIR/cqadupstack
+    metrics:
+    - type: map_at_1
+      value: 31.180000000000003
+    - type: map_at_10
+      value: 41.78
+    - type: map_at_100
+      value: 43.102000000000004
+    - type: map_at_1000
+      value: 43.222
+    - type: map_at_3
+      value: 38.505
+    - type: map_at_5
+      value: 40.443
+    - type: mrr_at_1
+      value: 37.824999999999996
+    - type: mrr_at_10
+      value: 47.481
+    - type: mrr_at_100
+      value: 48.268
+    - type: mrr_at_1000
+      value: 48.313
+    - type: mrr_at_3
+      value: 44.946999999999996
+    - type: mrr_at_5
+      value: 46.492
+    - type: ndcg_at_1
+      value: 37.824999999999996
+    - type: ndcg_at_10
+      value: 47.827
+    - type: ndcg_at_100
+      value: 53.407000000000004
+    - type: ndcg_at_1000
+      value: 55.321
+    - type: ndcg_at_3
+      value: 42.815
+    - type: ndcg_at_5
+      value: 45.363
+    - type: precision_at_1
+      value: 37.824999999999996
+    - type: precision_at_10
+      value: 8.652999999999999
+    - type: precision_at_100
+      value: 1.354
+    - type: precision_at_1000
+      value: 0.172
+    - type: precision_at_3
+      value: 20.372
+    - type: precision_at_5
+      value: 14.591000000000001
+    - type: recall_at_1
+      value: 31.180000000000003
+    - type: recall_at_10
+      value: 59.894000000000005
+    - type: recall_at_100
+      value: 83.722
+    - type: recall_at_1000
+      value: 95.705
+    - type: recall_at_3
+      value: 45.824
+    - type: recall_at_5
+      value: 52.349999999999994
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB CQADupstackProgrammersRetrieval
+      revision: None
+      split: test
+      type: BeIR/cqadupstack
+    metrics:
+    - type: map_at_1
+      value: 24.66
+    - type: map_at_10
+      value: 34.141
+    - type: map_at_100
+      value: 35.478
+    - type: map_at_1000
+      value: 35.594
+    - type: map_at_3
+      value: 30.446
+    - type: map_at_5
+      value: 32.583
+    - type: mrr_at_1
+      value: 29.909000000000002
+    - type: mrr_at_10
+      value: 38.949
+    - type: mrr_at_100
+      value: 39.803
+    - type: mrr_at_1000
+      value: 39.867999999999995
+    - type: mrr_at_3
+      value: 35.921
+    - type: mrr_at_5
+      value: 37.753
+    - type: ndcg_at_1
+      value: 29.909000000000002
+    - type: ndcg_at_10
+      value: 40.012
+    - type: ndcg_at_100
+      value: 45.707
+    - type: ndcg_at_1000
+      value: 48.15
+    - type: ndcg_at_3
+      value: 34.015
+    - type: ndcg_at_5
+      value: 37.002
+    - type: precision_at_1
+      value: 29.909000000000002
+    - type: precision_at_10
+      value: 7.693999999999999
+    - type: precision_at_100
+      value: 1.2229999999999999
+    - type: precision_at_1000
+      value: 0.16
+    - type: precision_at_3
+      value: 16.323999999999998
+    - type: precision_at_5
+      value: 12.306000000000001
+    - type: recall_at_1
+      value: 24.66
+    - type: recall_at_10
+      value: 52.478
+    - type: recall_at_100
+      value: 77.051
+    - type: recall_at_1000
+      value: 93.872
+    - type: recall_at_3
+      value: 36.382999999999996
+    - type: recall_at_5
+      value: 43.903999999999996
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB CQADupstackRetrieval
+      revision: None
+      split: test
+      type: BeIR/cqadupstack
+    metrics:
+    - type: map_at_1
+      value: 26.768416666666667
+    - type: map_at_10
+      value: 36.2485
+    - type: map_at_100
+      value: 37.520833333333336
+    - type: map_at_1000
+      value: 37.64033333333334
+    - type: map_at_3
+      value: 33.25791666666667
+    - type: map_at_5
+      value: 34.877250000000004
+    - type: mrr_at_1
+      value: 31.65408333333334
+    - type: mrr_at_10
+      value: 40.43866666666667
+    - type: mrr_at_100
+      value: 41.301249999999996
+    - type: mrr_at_1000
+      value: 41.357499999999995
+    - type: mrr_at_3
+      value: 37.938916666666664
+    - type: mrr_at_5
+      value: 39.35183333333334
+    - type: ndcg_at_1
+      value: 31.65408333333334
+    - type: ndcg_at_10
+      value: 41.76983333333334
+    - type: ndcg_at_100
+      value: 47.138
+    - type: ndcg_at_1000
+      value: 49.33816666666667
+    - type: ndcg_at_3
+      value: 36.76683333333333
+    - type: ndcg_at_5
+      value: 39.04441666666666
+    - type: precision_at_1
+      value: 31.65408333333334
+    - type: precision_at_10
+      value: 7.396249999999998
+    - type: precision_at_100
+      value: 1.1974166666666666
+    - type: precision_at_1000
+      value: 0.15791666666666668
+    - type: precision_at_3
+      value: 16.955583333333333
+    - type: precision_at_5
+      value: 12.09925
+    - type: recall_at_1
+      value: 26.768416666666667
+    - type: recall_at_10
+      value: 53.82366666666667
+    - type: recall_at_100
+      value: 77.39600000000002
+    - type: recall_at_1000
+      value: 92.46300000000001
+    - type: recall_at_3
+      value: 39.90166666666667
+    - type: recall_at_5
+      value: 45.754000000000005
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB CQADupstackStatsRetrieval
+      revision: None
+      split: test
+      type: BeIR/cqadupstack
+    metrics:
+    - type: map_at_1
+      value: 24.369
+    - type: map_at_10
+      value: 32.025
+    - type: map_at_100
+      value: 33.08
+    - type: map_at_1000
+      value: 33.169
+    - type: map_at_3
+      value: 29.589
+    - type: map_at_5
+      value: 30.894
+    - type: mrr_at_1
+      value: 27.301
+    - type: mrr_at_10
+      value: 34.64
+    - type: mrr_at_100
+      value: 35.556
+    - type: mrr_at_1000
+      value: 35.616
+    - type: mrr_at_3
+      value: 32.515
+    - type: mrr_at_5
+      value: 33.666000000000004
+    - type: ndcg_at_1
+      value: 27.301
+    - type: ndcg_at_10
+      value: 36.386
+    - type: ndcg_at_100
+      value: 41.598
+    - type: ndcg_at_1000
+      value: 43.864999999999995
+    - type: ndcg_at_3
+      value: 32.07
+    - type: ndcg_at_5
+      value: 34.028999999999996
+    - type: precision_at_1
+      value: 27.301
+    - type: precision_at_10
+      value: 5.782
+    - type: precision_at_100
+      value: 0.923
+    - type: precision_at_1000
+      value: 0.11900000000000001
+    - type: precision_at_3
+      value: 13.804
+    - type: precision_at_5
+      value: 9.693
+    - type: recall_at_1
+      value: 24.369
+    - type: recall_at_10
+      value: 47.026
+    - type: recall_at_100
+      value: 70.76400000000001
+    - type: recall_at_1000
+      value: 87.705
+    - type: recall_at_3
+      value: 35.366
+    - type: recall_at_5
+      value: 40.077
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB CQADupstackTexRetrieval
+      revision: None
+      split: test
+      type: BeIR/cqadupstack
+    metrics:
+    - type: map_at_1
+      value: 17.878
+    - type: map_at_10
+      value: 25.582
+    - type: map_at_100
+      value: 26.848
+    - type: map_at_1000
+      value: 26.985
+    - type: map_at_3
+      value: 22.997
+    - type: map_at_5
+      value: 24.487000000000002
+    - type: mrr_at_1
+      value: 22.023
+    - type: mrr_at_10
+      value: 29.615000000000002
+    - type: mrr_at_100
+      value: 30.656
+    - type: mrr_at_1000
+      value: 30.737
+    - type: mrr_at_3
+      value: 27.322999999999997
+    - type: mrr_at_5
+      value: 28.665000000000003
+    - type: ndcg_at_1
+      value: 22.023
+    - type: ndcg_at_10
+      value: 30.476999999999997
+    - type: ndcg_at_100
+      value: 36.258
+    - type: ndcg_at_1000
+      value: 39.287
+    - type: ndcg_at_3
+      value: 25.995
+    - type: ndcg_at_5
+      value: 28.174
+    - type: precision_at_1
+      value: 22.023
+    - type: precision_at_10
+      value: 5.657
+    - type: precision_at_100
+      value: 1.01
+    - type: precision_at_1000
+      value: 0.145
+    - type: precision_at_3
+      value: 12.491
+    - type: precision_at_5
+      value: 9.112
+    - type: recall_at_1
+      value: 17.878
+    - type: recall_at_10
+      value: 41.155
+    - type: recall_at_100
+      value: 66.62599999999999
+    - type: recall_at_1000
+      value: 88.08200000000001
+    - type: recall_at_3
+      value: 28.505000000000003
+    - type: recall_at_5
+      value: 34.284
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB CQADupstackUnixRetrieval
+      revision: None
+      split: test
+      type: BeIR/cqadupstack
+    metrics:
+    - type: map_at_1
+      value: 26.369999999999997
+    - type: map_at_10
+      value: 36.115
+    - type: map_at_100
+      value: 37.346000000000004
+    - type: map_at_1000
+      value: 37.449
+    - type: map_at_3
+      value: 32.976
+    - type: map_at_5
+      value: 34.782000000000004
+    - type: mrr_at_1
+      value: 30.784
+    - type: mrr_at_10
+      value: 40.014
+    - type: mrr_at_100
+      value: 40.913
+    - type: mrr_at_1000
+      value: 40.967999999999996
+    - type: mrr_at_3
+      value: 37.205
+    - type: mrr_at_5
+      value: 38.995999999999995
+    - type: ndcg_at_1
+      value: 30.784
+    - type: ndcg_at_10
+      value: 41.797000000000004
+    - type: ndcg_at_100
+      value: 47.355000000000004
+    - type: ndcg_at_1000
+      value: 49.535000000000004
+    - type: ndcg_at_3
+      value: 36.29
+    - type: ndcg_at_5
+      value: 39.051
+    - type: precision_at_1
+      value: 30.784
+    - type: precision_at_10
+      value: 7.164
+    - type: precision_at_100
+      value: 1.122
+    - type: precision_at_1000
+      value: 0.14200000000000002
+    - type: precision_at_3
+      value: 16.636
+    - type: precision_at_5
+      value: 11.996
+    - type: recall_at_1
+      value: 26.369999999999997
+    - type: recall_at_10
+      value: 55.010000000000005
+    - type: recall_at_100
+      value: 79.105
+    - type: recall_at_1000
+      value: 94.053
+    - type: recall_at_3
+      value: 40.139
+    - type: recall_at_5
+      value: 47.089
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB CQADupstackWebmastersRetrieval
+      revision: None
+      split: test
+      type: BeIR/cqadupstack
+    metrics:
+    - type: map_at_1
+      value: 26.421
+    - type: map_at_10
+      value: 35.253
+    - type: map_at_100
+      value: 36.97
+    - type: map_at_1000
+      value: 37.195
+    - type: map_at_3
+      value: 32.068000000000005
+    - type: map_at_5
+      value: 33.763
+    - type: mrr_at_1
+      value: 31.423000000000002
+    - type: mrr_at_10
+      value: 39.995999999999995
+    - type: mrr_at_100
+      value: 40.977999999999994
+    - type: mrr_at_1000
+      value: 41.024
+    - type: mrr_at_3
+      value: 36.989
+    - type: mrr_at_5
+      value: 38.629999999999995
+    - type: ndcg_at_1
+      value: 31.423000000000002
+    - type: ndcg_at_10
+      value: 41.382000000000005
+    - type: ndcg_at_100
+      value: 47.532000000000004
+    - type: ndcg_at_1000
+      value: 49.829
+    - type: ndcg_at_3
+      value: 35.809000000000005
+    - type: ndcg_at_5
+      value: 38.308
+    - type: precision_at_1
+      value: 31.423000000000002
+    - type: precision_at_10
+      value: 7.885000000000001
+    - type: precision_at_100
+      value: 1.609
+    - type: precision_at_1000
+      value: 0.246
+    - type: precision_at_3
+      value: 16.469
+    - type: precision_at_5
+      value: 12.174
+    - type: recall_at_1
+      value: 26.421
+    - type: recall_at_10
+      value: 53.618
+    - type: recall_at_100
+      value: 80.456
+    - type: recall_at_1000
+      value: 94.505
+    - type: recall_at_3
+      value: 37.894
+    - type: recall_at_5
+      value: 44.352999999999994
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB CQADupstackWordpressRetrieval
+      revision: None
+      split: test
+      type: BeIR/cqadupstack
+    metrics:
+    - type: map_at_1
+      value: 21.54
+    - type: map_at_10
+      value: 29.468
+    - type: map_at_100
+      value: 30.422
+    - type: map_at_1000
+      value: 30.542
+    - type: map_at_3
+      value: 26.888
+    - type: map_at_5
+      value: 27.962999999999997
+    - type: mrr_at_1
+      value: 23.29
+    - type: mrr_at_10
+      value: 31.176
+    - type: mrr_at_100
+      value: 32.046
+    - type: mrr_at_1000
+      value: 32.129000000000005
+    - type: mrr_at_3
+      value: 28.804999999999996
+    - type: mrr_at_5
+      value: 29.868
+    - type: ndcg_at_1
+      value: 23.29
+    - type: ndcg_at_10
+      value: 34.166000000000004
+    - type: ndcg_at_100
+      value: 39.217999999999996
+    - type: ndcg_at_1000
+      value: 41.964
+    - type: ndcg_at_3
+      value: 28.970000000000002
+    - type: ndcg_at_5
+      value: 30.797
+    - type: precision_at_1
+      value: 23.29
+    - type: precision_at_10
+      value: 5.489999999999999
+    - type: precision_at_100
+      value: 0.874
+    - type: precision_at_1000
+      value: 0.122
+    - type: precision_at_3
+      value: 12.261
+    - type: precision_at_5
+      value: 8.503
+    - type: recall_at_1
+      value: 21.54
+    - type: recall_at_10
+      value: 47.064
+    - type: recall_at_100
+      value: 70.959
+    - type: recall_at_1000
+      value: 91.032
+    - type: recall_at_3
+      value: 32.828
+    - type: recall_at_5
+      value: 37.214999999999996
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB ClimateFEVER
+      revision: None
+      split: test
+      type: climate-fever
+    metrics:
+    - type: map_at_1
+      value: 10.102
+    - type: map_at_10
+      value: 17.469
+    - type: map_at_100
+      value: 19.244
+    - type: map_at_1000
+      value: 19.435
+    - type: map_at_3
+      value: 14.257
+    - type: map_at_5
+      value: 16.028000000000002
+    - type: mrr_at_1
+      value: 22.866
+    - type: mrr_at_10
+      value: 33.535
+    - type: mrr_at_100
+      value: 34.583999999999996
+    - type: mrr_at_1000
+      value: 34.622
+    - type: mrr_at_3
+      value: 29.946
+    - type: mrr_at_5
+      value: 32.157000000000004
+    - type: ndcg_at_1
+      value: 22.866
+    - type: ndcg_at_10
+      value: 25.16
+    - type: ndcg_at_100
+      value: 32.347
+    - type: ndcg_at_1000
+      value: 35.821
+    - type: ndcg_at_3
+      value: 19.816
+    - type: ndcg_at_5
+      value: 22.026
+    - type: precision_at_1
+      value: 22.866
+    - type: precision_at_10
+      value: 8.072
+    - type: precision_at_100
+      value: 1.5709999999999997
+    - type: precision_at_1000
+      value: 0.22200000000000003
+    - type: precision_at_3
+      value: 14.701
+    - type: precision_at_5
+      value: 11.960999999999999
+    - type: recall_at_1
+      value: 10.102
+    - type: recall_at_10
+      value: 31.086000000000002
+    - type: recall_at_100
+      value: 55.896
+    - type: recall_at_1000
+      value: 75.375
+    - type: recall_at_3
+      value: 18.343999999999998
+    - type: recall_at_5
+      value: 24.102
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB DBPedia
+      revision: None
+      split: test
+      type: dbpedia-entity
+    metrics:
+    - type: map_at_1
+      value: 7.961
+    - type: map_at_10
+      value: 16.058
+    - type: map_at_100
+      value: 21.878
+    - type: map_at_1000
+      value: 23.156
+    - type: map_at_3
+      value: 12.206999999999999
+    - type: map_at_5
+      value: 13.747000000000002
+    - type: mrr_at_1
+      value: 60.5
+    - type: mrr_at_10
+      value: 68.488
+    - type: mrr_at_100
+      value: 69.02199999999999
+    - type: mrr_at_1000
+      value: 69.03200000000001
+    - type: mrr_at_3
+      value: 66.792
+    - type: mrr_at_5
+      value: 67.62899999999999
+    - type: ndcg_at_1
+      value: 49.125
+    - type: ndcg_at_10
+      value: 34.827999999999996
+    - type: ndcg_at_100
+      value: 38.723
+    - type: ndcg_at_1000
+      value: 45.988
+    - type: ndcg_at_3
+      value: 40.302
+    - type: ndcg_at_5
+      value: 36.781000000000006
+    - type: precision_at_1
+      value: 60.5
+    - type: precision_at_10
+      value: 26.825
+    - type: precision_at_100
+      value: 8.445
+    - type: precision_at_1000
+      value: 1.7000000000000002
+    - type: precision_at_3
+      value: 43.25
+    - type: precision_at_5
+      value: 34.5
+    - type: recall_at_1
+      value: 7.961
+    - type: recall_at_10
+      value: 20.843
+    - type: recall_at_100
+      value: 43.839
+    - type: recall_at_1000
+      value: 67.33
+    - type: recall_at_3
+      value: 13.516
+    - type: recall_at_5
+      value: 15.956000000000001
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB EmotionClassification
+      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
+      split: test
+      type: mteb/emotion
+    metrics:
+    - type: accuracy
+      value: 52.06000000000001
+    - type: f1
+      value: 47.21494728335567
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB FEVER
+      revision: None
+      split: test
+      type: fever
+    metrics:
+    - type: map_at_1
+      value: 56.798
+    - type: map_at_10
+      value: 67.644
+    - type: map_at_100
+      value: 68.01700000000001
+    - type: map_at_1000
+      value: 68.038
+    - type: map_at_3
+      value: 65.539
+    - type: map_at_5
+      value: 66.912
+    - type: mrr_at_1
+      value: 61.221000000000004
+    - type: mrr_at_10
+      value: 71.97099999999999
+    - type: mrr_at_100
+      value: 72.262
+    - type: mrr_at_1000
+      value: 72.27
+    - type: mrr_at_3
+      value: 70.052
+    - type: mrr_at_5
+      value: 71.324
+    - type: ndcg_at_1
+      value: 61.221000000000004
+    - type: ndcg_at_10
+      value: 73.173
+    - type: ndcg_at_100
+      value: 74.779
+    - type: ndcg_at_1000
+      value: 75.229
+    - type: ndcg_at_3
+      value: 69.291
+    - type: ndcg_at_5
+      value: 71.552
+    - type: precision_at_1
+      value: 61.221000000000004
+    - type: precision_at_10
+      value: 9.449
+    - type: precision_at_100
+      value: 1.0370000000000001
+    - type: precision_at_1000
+      value: 0.109
+    - type: precision_at_3
+      value: 27.467999999999996
+    - type: precision_at_5
+      value: 17.744
+    - type: recall_at_1
+      value: 56.798
+    - type: recall_at_10
+      value: 85.991
+    - type: recall_at_100
+      value: 92.973
+    - type: recall_at_1000
+      value: 96.089
+    - type: recall_at_3
+      value: 75.576
+    - type: recall_at_5
+      value: 81.12
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB FiQA2018
+      revision: None
+      split: test
+      type: fiqa
+    metrics:
+    - type: map_at_1
+      value: 18.323
+    - type: map_at_10
+      value: 30.279
+    - type: map_at_100
+      value: 32.153999999999996
+    - type: map_at_1000
+      value: 32.339
+    - type: map_at_3
+      value: 26.336
+    - type: map_at_5
+      value: 28.311999999999998
+    - type: mrr_at_1
+      value: 35.339999999999996
+    - type: mrr_at_10
+      value: 44.931
+    - type: mrr_at_100
+      value: 45.818999999999996
+    - type: mrr_at_1000
+      value: 45.864
+    - type: mrr_at_3
+      value: 42.618
+    - type: mrr_at_5
+      value: 43.736999999999995
+    - type: ndcg_at_1
+      value: 35.339999999999996
+    - type: ndcg_at_10
+      value: 37.852999999999994
+    - type: ndcg_at_100
+      value: 44.888
+    - type: ndcg_at_1000
+      value: 48.069
+    - type: ndcg_at_3
+      value: 34.127
+    - type: ndcg_at_5
+      value: 35.026
+    - type: precision_at_1
+      value: 35.339999999999996
+    - type: precision_at_10
+      value: 10.617
+    - type: precision_at_100
+      value: 1.7930000000000001
+    - type: precision_at_1000
+      value: 0.23600000000000002
+    - type: precision_at_3
+      value: 22.582
+    - type: precision_at_5
+      value: 16.605
+    - type: recall_at_1
+      value: 18.323
+    - type: recall_at_10
+      value: 44.948
+    - type: recall_at_100
+      value: 71.11800000000001
+    - type: recall_at_1000
+      value: 90.104
+    - type: recall_at_3
+      value: 31.661
+    - type: recall_at_5
+      value: 36.498000000000005
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB HotpotQA
+      revision: None
+      split: test
+      type: hotpotqa
+    metrics:
+    - type: map_at_1
+      value: 30.668
+    - type: map_at_10
+      value: 43.669999999999995
+    - type: map_at_100
+      value: 44.646
+    - type: map_at_1000
+      value: 44.731
+    - type: map_at_3
+      value: 40.897
+    - type: map_at_5
+      value: 42.559999999999995
+    - type: mrr_at_1
+      value: 61.336999999999996
+    - type: mrr_at_10
+      value: 68.496
+    - type: mrr_at_100
+      value: 68.916
+    - type: mrr_at_1000
+      value: 68.938
+    - type: mrr_at_3
+      value: 66.90700000000001
+    - type: mrr_at_5
+      value: 67.91199999999999
+    - type: ndcg_at_1
+      value: 61.336999999999996
+    - type: ndcg_at_10
+      value: 52.588
+    - type: ndcg_at_100
+      value: 56.389
+    - type: ndcg_at_1000
+      value: 58.187999999999995
+    - type: ndcg_at_3
+      value: 48.109
+    - type: ndcg_at_5
+      value: 50.498
+    - type: precision_at_1
+      value: 61.336999999999996
+    - type: precision_at_10
+      value: 11.033
+    - type: precision_at_100
+      value: 1.403
+    - type: precision_at_1000
+      value: 0.164
+    - type: precision_at_3
+      value: 30.105999999999998
+    - type: precision_at_5
+      value: 19.954
+    - type: recall_at_1
+      value: 30.668
+    - type: recall_at_10
+      value: 55.165
+    - type: recall_at_100
+      value: 70.169
+    - type: recall_at_1000
+      value: 82.12
+    - type: recall_at_3
+      value: 45.159
+    - type: recall_at_5
+      value: 49.885000000000005
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB ImdbClassification
+      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
+      split: test
+      type: mteb/imdb
+    metrics:
+    - type: accuracy
+      value: 78.542
+    - type: ap
+      value: 72.50692137216646
+    - type: f1
+      value: 78.40630687221642
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB MSMARCO
+      revision: None
+      split: dev
+      type: msmarco
+    metrics:
+    - type: map_at_1
+      value: 18.613
+    - type: map_at_10
+      value: 29.98
+    - type: map_at_100
+      value: 31.136999999999997
+    - type: map_at_1000
+      value: 31.196
+    - type: map_at_3
+      value: 26.339000000000002
+    - type: map_at_5
+      value: 28.351
+    - type: mrr_at_1
+      value: 19.054
+    - type: mrr_at_10
+      value: 30.476
+    - type: mrr_at_100
+      value: 31.588
+    - type: mrr_at_1000
+      value: 31.641000000000002
+    - type: mrr_at_3
+      value: 26.834000000000003
+    - type: mrr_at_5
+      value: 28.849000000000004
+    - type: ndcg_at_1
+      value: 19.083
+    - type: ndcg_at_10
+      value: 36.541000000000004
+    - type: ndcg_at_100
+      value: 42.35
+    - type: ndcg_at_1000
+      value: 43.9
+    - type: ndcg_at_3
+      value: 29.015
+    - type: ndcg_at_5
+      value: 32.622
+    - type: precision_at_1
+      value: 19.083
+    - type: precision_at_10
+      value: 5.914
+    - type: precision_at_100
+      value: 0.889
+    - type: precision_at_1000
+      value: 0.10200000000000001
+    - type: precision_at_3
+      value: 12.483
+    - type: precision_at_5
+      value: 9.315
+    - type: recall_at_1
+      value: 18.613
+    - type: recall_at_10
+      value: 56.88999999999999
+    - type: recall_at_100
+      value: 84.207
+    - type: recall_at_1000
+      value: 96.20100000000001
+    - type: recall_at_3
+      value: 36.262
+    - type: recall_at_5
+      value: 44.925
+    task:
+      type: Retrieval
+  - dataset:
+      config: en
+      name: MTEB MTOPDomainClassification (en)
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+      split: test
+      type: mteb/mtop_domain
+    metrics:
+    - type: accuracy
+      value: 94.77656178750571
+    - type: f1
+      value: 94.37966073742972
+    task:
+      type: Classification
+  - dataset:
+      config: en
+      name: MTEB MTOPIntentClassification (en)
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+      split: test
+      type: mteb/mtop_intent
+    metrics:
+    - type: accuracy
+      value: 77.72457820337438
+    - type: f1
+      value: 59.11327646329634
+    task:
+      type: Classification
+  - dataset:
+      config: en
+      name: MTEB MassiveIntentClassification (en)
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+      split: test
+      type: mteb/amazon_massive_intent
+    metrics:
+    - type: accuracy
+      value: 73.17753866846
+    - type: f1
+      value: 71.22604635414544
+    task:
+      type: Classification
+  - dataset:
+      config: en
+      name: MTEB MassiveScenarioClassification (en)
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+      split: test
+      type: mteb/amazon_massive_scenario
+    metrics:
+    - type: accuracy
+      value: 76.67787491593813
+    - type: f1
+      value: 76.87653151298177
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB MedrxivClusteringP2P
+      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
+      split: test
+      type: mteb/medrxiv-clustering-p2p
+    metrics:
+    - type: v_measure
+      value: 33.3485843514749
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB MedrxivClusteringS2S
+      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
+      split: test
+      type: mteb/medrxiv-clustering-s2s
+    metrics:
+    - type: v_measure
+      value: 29.792796913883617
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB MindSmallReranking
+      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
+      split: test
+      type: mteb/mind_small
+    metrics:
+    - type: map
+      value: 31.310305659169963
+    - type: mrr
+      value: 32.38286775798406
+    task:
+      type: Reranking
+  - dataset:
+      config: default
+      name: MTEB NFCorpus
+      revision: None
+      split: test
+      type: nfcorpus
+    metrics:
+    - type: map_at_1
+      value: 4.968
+    - type: map_at_10
+      value: 11.379
+    - type: map_at_100
+      value: 14.618999999999998
+    - type: map_at_1000
+      value: 16.055
+    - type: map_at_3
+      value: 8.34
+    - type: map_at_5
+      value: 9.690999999999999
+    - type: mrr_at_1
+      value: 43.034
+    - type: mrr_at_10
+      value: 51.019999999999996
+    - type: mrr_at_100
+      value: 51.63100000000001
+    - type: mrr_at_1000
+      value: 51.681
+    - type: mrr_at_3
+      value: 49.174
+    - type: mrr_at_5
+      value: 50.181
+    - type: ndcg_at_1
+      value: 41.176
+    - type: ndcg_at_10
+      value: 31.341
+    - type: ndcg_at_100
+      value: 29.451
+    - type: ndcg_at_1000
+      value: 38.007000000000005
+    - type: ndcg_at_3
+      value: 36.494
+    - type: ndcg_at_5
+      value: 34.499
+    - type: precision_at_1
+      value: 43.034
+    - type: precision_at_10
+      value: 23.375
+    - type: precision_at_100
+      value: 7.799
+    - type: precision_at_1000
+      value: 2.059
+    - type: precision_at_3
+      value: 34.675
+    - type: precision_at_5
+      value: 30.154999999999998
+    - type: recall_at_1
+      value: 4.968
+    - type: recall_at_10
+      value: 15.104999999999999
+    - type: recall_at_100
+      value: 30.741000000000003
+    - type: recall_at_1000
+      value: 61.182
+    - type: recall_at_3
+      value: 9.338000000000001
+    - type: recall_at_5
+      value: 11.484
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB NQ
+      revision: None
+      split: test
+      type: nq
+    metrics:
+    - type: map_at_1
+      value: 23.716
+    - type: map_at_10
+      value: 38.32
+    - type: map_at_100
+      value: 39.565
+    - type: map_at_1000
+      value: 39.602
+    - type: map_at_3
+      value: 33.848
+    - type: map_at_5
+      value: 36.471
+    - type: mrr_at_1
+      value: 26.912000000000003
+    - type: mrr_at_10
+      value: 40.607
+    - type: mrr_at_100
+      value: 41.589
+    - type: mrr_at_1000
+      value: 41.614000000000004
+    - type: mrr_at_3
+      value: 36.684
+    - type: mrr_at_5
+      value: 39.036
+    - type: ndcg_at_1
+      value: 26.883000000000003
+    - type: ndcg_at_10
+      value: 46.096
+    - type: ndcg_at_100
+      value: 51.513
+    - type: ndcg_at_1000
+      value: 52.366
+    - type: ndcg_at_3
+      value: 37.549
+    - type: ndcg_at_5
+      value: 41.971000000000004
+    - type: precision_at_1
+      value: 26.883000000000003
+    - type: precision_at_10
+      value: 8.004
+    - type: precision_at_100
+      value: 1.107
+    - type: precision_at_1000
+      value: 0.11900000000000001
+    - type: precision_at_3
+      value: 17.516000000000002
+    - type: precision_at_5
+      value: 13.019
+    - type: recall_at_1
+      value: 23.716
+    - type: recall_at_10
+      value: 67.656
+    - type: recall_at_100
+      value: 91.413
+    - type: recall_at_1000
+      value: 97.714
+    - type: recall_at_3
+      value: 45.449
+    - type: recall_at_5
+      value: 55.598000000000006
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB QuoraRetrieval
+      revision: None
+      split: test
+      type: quora
+    metrics:
+    - type: map_at_1
+      value: 70.486
+    - type: map_at_10
+      value: 84.292
+    - type: map_at_100
+      value: 84.954
+    - type: map_at_1000
+      value: 84.969
+    - type: map_at_3
+      value: 81.295
+    - type: map_at_5
+      value: 83.165
+    - type: mrr_at_1
+      value: 81.16
+    - type: mrr_at_10
+      value: 87.31
+    - type: mrr_at_100
+      value: 87.423
+    - type: mrr_at_1000
+      value: 87.423
+    - type: mrr_at_3
+      value: 86.348
+    - type: mrr_at_5
+      value: 86.991
+    - type: ndcg_at_1
+      value: 81.17
+    - type: ndcg_at_10
+      value: 88.067
+    - type: ndcg_at_100
+      value: 89.34
+    - type: ndcg_at_1000
+      value: 89.43900000000001
+    - type: ndcg_at_3
+      value: 85.162
+    - type: ndcg_at_5
+      value: 86.752
+    - type: precision_at_1
+      value: 81.17
+    - type: precision_at_10
+      value: 13.394
+    - type: precision_at_100
+      value: 1.5310000000000001
+    - type: precision_at_1000
+      value: 0.157
+    - type: precision_at_3
+      value: 37.193
+    - type: precision_at_5
+      value: 24.482
+    - type: recall_at_1
+      value: 70.486
+    - type: recall_at_10
+      value: 95.184
+    - type: recall_at_100
+      value: 99.53999999999999
+    - type: recall_at_1000
+      value: 99.98700000000001
+    - type: recall_at_3
+      value: 86.89
+    - type: recall_at_5
+      value: 91.365
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB RedditClustering
+      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
+      split: test
+      type: mteb/reddit-clustering
+    metrics:
+    - type: v_measure
+      value: 44.118229475102154
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB RedditClusteringP2P
+      revision: 282350215ef01743dc01b456c7f5241fa8937f16
+      split: test
+      type: mteb/reddit-clustering-p2p
+    metrics:
+    - type: v_measure
+      value: 48.68049097629063
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB SCIDOCS
+      revision: None
+      split: test
+      type: scidocs
+    metrics:
+    - type: map_at_1
+      value: 4.888
+    - type: map_at_10
+      value: 12.770999999999999
+    - type: map_at_100
+      value: 15.238
+    - type: map_at_1000
+      value: 15.616
+    - type: map_at_3
+      value: 8.952
+    - type: map_at_5
+      value: 10.639999999999999
+    - type: mrr_at_1
+      value: 24.099999999999998
+    - type: mrr_at_10
+      value: 35.375
+    - type: mrr_at_100
+      value: 36.442
+    - type: mrr_at_1000
+      value: 36.488
+    - type: mrr_at_3
+      value: 31.717000000000002
+    - type: mrr_at_5
+      value: 33.722
+    - type: ndcg_at_1
+      value: 24.099999999999998
+    - type: ndcg_at_10
+      value: 21.438
+    - type: ndcg_at_100
+      value: 30.601
+    - type: ndcg_at_1000
+      value: 36.678
+    - type: ndcg_at_3
+      value: 19.861
+    - type: ndcg_at_5
+      value: 17.263
+    - type: precision_at_1
+      value: 24.099999999999998
+    - type: precision_at_10
+      value: 11.4
+    - type: precision_at_100
+      value: 2.465
+    - type: precision_at_1000
+      value: 0.392
+    - type: precision_at_3
+      value: 18.733
+    - type: precision_at_5
+      value: 15.22
+    - type: recall_at_1
+      value: 4.888
+    - type: recall_at_10
+      value: 23.118
+    - type: recall_at_100
+      value: 49.995
+    - type: recall_at_1000
+      value: 79.577
+    - type: recall_at_3
+      value: 11.398
+    - type: recall_at_5
+      value: 15.428
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB SICK-R
+      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
+      split: test
+      type: mteb/sickr-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 85.33198632617024
+    - type: cos_sim_spearman
+      value: 79.09232997136625
+    - type: euclidean_pearson
+      value: 81.49986011523868
+    - type: euclidean_spearman
+      value: 77.03530620283338
+    - type: manhattan_pearson
+      value: 81.4741227286667
+    - type: manhattan_spearman
+      value: 76.98641133116311
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STS12
+      revision: a0d554a64d88156834ff5ae9920b964011b16384
+      split: test
+      type: mteb/sts12-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 84.60103674582464
+    - type: cos_sim_spearman
+      value: 75.03945035801914
+    - type: euclidean_pearson
+      value: 80.82455267481467
+    - type: euclidean_spearman
+      value: 70.3317366248871
+    - type: manhattan_pearson
+      value: 80.8928091531445
+    - type: manhattan_spearman
+      value: 70.43207370945672
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STS13
+      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
+      split: test
+      type: mteb/sts13-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 82.52453177109315
+    - type: cos_sim_spearman
+      value: 83.26431569305103
+    - type: euclidean_pearson
+      value: 82.10494657997404
+    - type: euclidean_spearman
+      value: 83.41028425949024
+    - type: manhattan_pearson
+      value: 82.08669822983934
+    - type: manhattan_spearman
+      value: 83.39959776442115
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STS14
+      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
+      split: test
+      type: mteb/sts14-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 82.67472020277681
+    - type: cos_sim_spearman
+      value: 78.61877889763109
+    - type: euclidean_pearson
+      value: 80.07878012437722
+    - type: euclidean_spearman
+      value: 77.44374494215397
+    - type: manhattan_pearson
+      value: 79.95988483102258
+    - type: manhattan_spearman
+      value: 77.36018101061366
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STS15
+      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
+      split: test
+      type: mteb/sts15-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 85.55450610494437
+    - type: cos_sim_spearman
+      value: 87.03494331841401
+    - type: euclidean_pearson
+      value: 81.4319784394287
+    - type: euclidean_spearman
+      value: 82.47893040599372
+    - type: manhattan_pearson
+      value: 81.32627203699644
+    - type: manhattan_spearman
+      value: 82.40660565070675
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STS16
+      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
+      split: test
+      type: mteb/sts16-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 81.51576965454805
+    - type: cos_sim_spearman
+      value: 83.0062959588245
+    - type: euclidean_pearson
+      value: 79.98888882568556
+    - type: euclidean_spearman
+      value: 81.08948911791873
+    - type: manhattan_pearson
+      value: 79.77952719568583
+    - type: manhattan_spearman
+      value: 80.79471040445408
+    task:
+      type: STS
+  - dataset:
+      config: en-en
+      name: MTEB STS17 (en-en)
+      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
+      split: test
+      type: mteb/sts17-crosslingual-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 87.28313046682885
+    - type: cos_sim_spearman
+      value: 87.35865211085007
+    - type: euclidean_pearson
+      value: 84.11501613667811
+    - type: euclidean_spearman
+      value: 82.82038954956121
+    - type: manhattan_pearson
+      value: 83.891278147302
+    - type: manhattan_spearman
+      value: 82.59947685165902
+    task:
+      type: STS
+  - dataset:
+      config: en
+      name: MTEB STS22 (en)
+      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
+      split: test
+      type: mteb/sts22-crosslingual-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 67.80653738006102
+    - type: cos_sim_spearman
+      value: 68.11259151179601
+    - type: euclidean_pearson
+      value: 43.16707985094242
+    - type: euclidean_spearman
+      value: 58.96200382968696
+    - type: manhattan_pearson
+      value: 43.84146858566507
+    - type: manhattan_spearman
+      value: 59.05193977207514
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STSBenchmark
+      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
+      split: test
+      type: mteb/stsbenchmark-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 82.62068205073571
+    - type: cos_sim_spearman
+      value: 84.40071593577095
+    - type: euclidean_pearson
+      value: 80.90824726252514
+    - type: euclidean_spearman
+      value: 80.54974812534094
+    - type: manhattan_pearson
+      value: 80.6759008187939
+    - type: manhattan_spearman
+      value: 80.31149103896973
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB SciDocsRR
+      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
+      split: test
+      type: mteb/scidocs-reranking
+    metrics:
+    - type: map
+      value: 87.13774787530915
+    - type: mrr
+      value: 96.22233793802422
+    task:
+      type: Reranking
+  - dataset:
+      config: default
+      name: MTEB SciFact
+      revision: None
+      split: test
+      type: scifact
+    metrics:
+    - type: map_at_1
+      value: 49.167
+    - type: map_at_10
+      value: 59.852000000000004
+    - type: map_at_100
+      value: 60.544
+    - type: map_at_1000
+      value: 60.577000000000005
+    - type: map_at_3
+      value: 57.242000000000004
+    - type: map_at_5
+      value: 58.704
+    - type: mrr_at_1
+      value: 51.0
+    - type: mrr_at_10
+      value: 60.575
+    - type: mrr_at_100
+      value: 61.144
+    - type: mrr_at_1000
+      value: 61.175000000000004
+    - type: mrr_at_3
+      value: 58.667
+    - type: mrr_at_5
+      value: 59.599999999999994
+    - type: ndcg_at_1
+      value: 51.0
+    - type: ndcg_at_10
+      value: 64.398
+    - type: ndcg_at_100
+      value: 67.581
+    - type: ndcg_at_1000
+      value: 68.551
+    - type: ndcg_at_3
+      value: 59.928000000000004
+    - type: ndcg_at_5
+      value: 61.986
+    - type: precision_at_1
+      value: 51.0
+    - type: precision_at_10
+      value: 8.7
+    - type: precision_at_100
+      value: 1.047
+    - type: precision_at_1000
+      value: 0.11299999999999999
+    - type: precision_at_3
+      value: 23.666999999999998
+    - type: precision_at_5
+      value: 15.6
+    - type: recall_at_1
+      value: 49.167
+    - type: recall_at_10
+      value: 77.333
+    - type: recall_at_100
+      value: 91.833
+    - type: recall_at_1000
+      value: 99.667
+    - type: recall_at_3
+      value: 65.594
+    - type: recall_at_5
+      value: 70.52199999999999
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB SprintDuplicateQuestions
+      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
+      split: test
+      type: mteb/sprintduplicatequestions-pairclassification
+    metrics:
+    - type: cos_sim_accuracy
+      value: 99.77227722772277
+    - type: cos_sim_ap
+      value: 94.14261011689366
+    - type: cos_sim_f1
+      value: 88.37209302325581
+    - type: cos_sim_precision
+      value: 89.36605316973414
+    - type: cos_sim_recall
+      value: 87.4
+    - type: dot_accuracy
+      value: 99.07128712871287
+    - type: dot_ap
+      value: 27.325649239129486
+    - type: dot_f1
+      value: 33.295838020247466
+    - type: dot_precision
+      value: 38.04627249357326
+    - type: dot_recall
+      value: 29.599999999999998
+    - type: euclidean_accuracy
+      value: 99.74158415841585
+    - type: euclidean_ap
+      value: 92.32695359979576
+    - type: euclidean_f1
+      value: 86.90534575772439
+    - type: euclidean_precision
+      value: 85.27430221366699
+    - type: euclidean_recall
+      value: 88.6
+    - type: manhattan_accuracy
+      value: 99.74257425742574
+    - type: manhattan_ap
+      value: 92.40335687760499
+    - type: manhattan_f1
+      value: 86.96507624200687
+    - type: manhattan_precision
+      value: 85.57599225556632
+    - type: manhattan_recall
+      value: 88.4
+    - type: max_accuracy
+      value: 99.77227722772277
+    - type: max_ap
+      value: 94.14261011689366
+    - type: max_f1
+      value: 88.37209302325581
+    task:
+      type: PairClassification
+  - dataset:
+      config: default
+      name: MTEB StackExchangeClustering
+      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
+      split: test
+      type: mteb/stackexchange-clustering
+    metrics:
+    - type: v_measure
+      value: 53.113809982945035
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB StackExchangeClusteringP2P
+      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
+      split: test
+      type: mteb/stackexchange-clustering-p2p
+    metrics:
+    - type: v_measure
+      value: 33.90915908471812
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB StackOverflowDupQuestions
+      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
+      split: test
+      type: mteb/stackoverflowdupquestions-reranking
+    metrics:
+    - type: map
+      value: 50.36481271702464
+    - type: mrr
+      value: 51.05628236142942
+    task:
+      type: Reranking
+  - dataset:
+      config: default
+      name: MTEB SummEval
+      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
+      split: test
+      type: mteb/summeval
+    metrics:
+    - type: cos_sim_pearson
+      value: 30.311305530381826
+    - type: cos_sim_spearman
+      value: 31.22029657606254
+    - type: dot_pearson
+      value: 12.157032445910177
+    - type: dot_spearman
+      value: 13.275185888551805
+    task:
+      type: Summarization
+  - dataset:
+      config: default
+      name: MTEB TRECCOVID
+      revision: None
+      split: test
+      type: trec-covid
+    metrics:
+    - type: map_at_1
+      value: 0.167
+    - type: map_at_10
+      value: 1.113
+    - type: map_at_100
+      value: 5.926
+    - type: map_at_1000
+      value: 15.25
+    - type: map_at_3
+      value: 0.414
+    - type: map_at_5
+      value: 0.633
+    - type: mrr_at_1
+      value: 64.0
+    - type: mrr_at_10
+      value: 74.444
+    - type: mrr_at_100
+      value: 74.667
+    - type: mrr_at_1000
+      value: 74.679
+    - type: mrr_at_3
+      value: 72.0
+    - type: mrr_at_5
+      value: 74.0
+    - type: ndcg_at_1
+      value: 59.0
+    - type: ndcg_at_10
+      value: 51.468
+    - type: ndcg_at_100
+      value: 38.135000000000005
+    - type: ndcg_at_1000
+      value: 36.946
+    - type: ndcg_at_3
+      value: 55.827000000000005
+    - type: ndcg_at_5
+      value: 53.555
+    - type: precision_at_1
+      value: 64.0
+    - type: precision_at_10
+      value: 54.400000000000006
+    - type: precision_at_100
+      value: 39.08
+    - type: precision_at_1000
+      value: 16.618
+    - type: precision_at_3
+      value: 58.667
+    - type: precision_at_5
+      value: 56.8
+    - type: recall_at_1
+      value: 0.167
+    - type: recall_at_10
+      value: 1.38
+    - type: recall_at_100
+      value: 9.189
+    - type: recall_at_1000
+      value: 35.737
+    - type: recall_at_3
+      value: 0.455
+    - type: recall_at_5
+      value: 0.73
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB Touche2020
+      revision: None
+      split: test
+      type: webis-touche2020
+    metrics:
+    - type: map_at_1
+      value: 2.4299999999999997
+    - type: map_at_10
+      value: 8.539
+    - type: map_at_100
+      value: 14.155999999999999
+    - type: map_at_1000
+      value: 15.684999999999999
+    - type: map_at_3
+      value: 3.857
+    - type: map_at_5
+      value: 5.583
+    - type: mrr_at_1
+      value: 26.531
+    - type: mrr_at_10
+      value: 40.489999999999995
+    - type: mrr_at_100
+      value: 41.772999999999996
+    - type: mrr_at_1000
+      value: 41.772999999999996
+    - type: mrr_at_3
+      value: 35.034
+    - type: mrr_at_5
+      value: 38.81
+    - type: ndcg_at_1
+      value: 21.429000000000002
+    - type: ndcg_at_10
+      value: 20.787
+    - type: ndcg_at_100
+      value: 33.202
+    - type: ndcg_at_1000
+      value: 45.167
+    - type: ndcg_at_3
+      value: 18.233
+    - type: ndcg_at_5
+      value: 19.887
+    - type: precision_at_1
+      value: 26.531
+    - type: precision_at_10
+      value: 19.796
+    - type: precision_at_100
+      value: 7.4079999999999995
+    - type: precision_at_1000
+      value: 1.5310000000000001
+    - type: precision_at_3
+      value: 19.728
+    - type: precision_at_5
+      value: 21.633
+    - type: recall_at_1
+      value: 2.4299999999999997
+    - type: recall_at_10
+      value: 14.901
+    - type: recall_at_100
+      value: 46.422000000000004
+    - type: recall_at_1000
+      value: 82.83500000000001
+    - type: recall_at_3
+      value: 4.655
+    - type: recall_at_5
+      value: 8.092
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB ToxicConversationsClassification
+      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
+      split: test
+      type: mteb/toxic_conversations_50k
+    metrics:
+    - type: accuracy
+      value: 72.90140000000001
+    - type: ap
+      value: 15.138716624430662
+    - type: f1
+      value: 56.08803013269606
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB TweetSentimentExtractionClassification
+      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
+      split: test
+      type: mteb/tweet_sentiment_extraction
+    metrics:
+    - type: accuracy
+      value: 59.85285795132994
+    - type: f1
+      value: 60.17575819903709
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB TwentyNewsgroupsClustering
+      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
+      split: test
+      type: mteb/twentynewsgroups-clustering
+    metrics:
+    - type: v_measure
+      value: 41.125150148437065
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB TwitterSemEval2015
+      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
+      split: test
+      type: mteb/twittersemeval2015-pairclassification
+    metrics:
+    - type: cos_sim_accuracy
+      value: 84.96751505036657
+    - type: cos_sim_ap
+      value: 70.45642872444971
+    - type: cos_sim_f1
+      value: 65.75274793133259
+    - type: cos_sim_precision
+      value: 61.806361736707686
+    - type: cos_sim_recall
+      value: 70.23746701846966
+    - type: dot_accuracy
+      value: 77.84466829588126
+    - type: dot_ap
+      value: 32.49904328313596
+    - type: dot_f1
+      value: 37.903122189387126
+    - type: dot_precision
+      value: 25.050951086956523
+    - type: dot_recall
+      value: 77.83641160949868
+    - type: euclidean_accuracy
+      value: 84.5920009536866
+    - type: euclidean_ap
+      value: 68.83700633574043
+    - type: euclidean_f1
+      value: 64.92803542871202
+    - type: euclidean_precision
+      value: 60.820465545056464
+    - type: euclidean_recall
+      value: 69.63060686015831
+    - type: manhattan_accuracy
+      value: 84.52643500029802
+    - type: manhattan_ap
+      value: 68.63286046599892
+    - type: manhattan_f1
+      value: 64.7476540705047
+    - type: manhattan_precision
+      value: 62.3291015625
+    - type: manhattan_recall
+      value: 67.36147757255937
+    - type: max_accuracy
+      value: 84.96751505036657
+    - type: max_ap
+      value: 70.45642872444971
+    - type: max_f1
+      value: 65.75274793133259
+    task:
+      type: PairClassification
+  - dataset:
+      config: default
+      name: MTEB TwitterURLCorpus
+      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
+      split: test
+      type: mteb/twitterurlcorpus-pairclassification
+    metrics:
+    - type: cos_sim_accuracy
+      value: 88.65603291031164
+    - type: cos_sim_ap
+      value: 85.58148320880878
+    - type: cos_sim_f1
+      value: 77.63202920041064
+    - type: cos_sim_precision
+      value: 76.68444377675957
+    - type: cos_sim_recall
+      value: 78.60332614721281
+    - type: dot_accuracy
+      value: 79.71048239996895
+    - type: dot_ap
+      value: 59.31114839296281
+    - type: dot_f1
+      value: 57.13895527483783
+    - type: dot_precision
+      value: 51.331125015335545
+    - type: dot_recall
+      value: 64.4287034185402
+    - type: euclidean_accuracy
+      value: 86.99305312997244
+    - type: euclidean_ap
+      value: 81.87075965254876
+    - type: euclidean_f1
+      value: 73.53543008715421
+    - type: euclidean_precision
+      value: 72.39964184450082
+    - type: euclidean_recall
+      value: 74.70742223591007
+    - type: manhattan_accuracy
+      value: 87.04156479217605
+    - type: manhattan_ap
+      value: 81.7850497283247
+    - type: manhattan_f1
+      value: 73.52951955143475
+    - type: manhattan_precision
+      value: 70.15875236030492
+    - type: manhattan_recall
+      value: 77.2405297197413
+    - type: max_accuracy
+      value: 88.65603291031164
+    - type: max_ap
+      value: 85.58148320880878
+    - type: max_f1
+      value: 77.63202920041064
+    task:
+      type: PairClassification
+model_creator: avsolatorio
+model_name: GIST-all-MiniLM-L6-v2
+pipeline_tag: text-generation
+quantized_by: afrideva
+tags:
+- feature-extraction
+- mteb
+- sentence-similarity
+- sentence-transformers
+- gguf
+- ggml
+- quantized
+---
+
+# GIST-all-MiniLM-L6-v2-GGUF
+
+Quantized GGUF model files for [GIST-all-MiniLM-L6-v2](https://huggingface.co/avsolatorio/GIST-all-MiniLM-L6-v2) from [avsolatorio](https://huggingface.co/avsolatorio)
+
+## Original Model Card:
+
+<h1 align="center">GIST Embedding v0 - all-MiniLM-L6-v2</h1>
+
+*GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning*
+
+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 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-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
+```
+
+
+# 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.