Add new SentenceTransformer model.

1_Pooling/config.json

@@ -0,0 +1,10 @@
+{
+  "word_embedding_dimension": 1024,
+  "pooling_mode_cls_token": true,
+  "pooling_mode_mean_tokens": false,
+  "pooling_mode_max_tokens": false,
+  "pooling_mode_mean_sqrt_len_tokens": false,
+  "pooling_mode_weightedmean_tokens": false,
+  "pooling_mode_lasttoken": false,
+  "include_prompt": true
+}

README.md

@@ -0,0 +1,2702 @@
+---
+language:
+- en
+library_name: sentence-transformers
+license: mit
+pipeline_tag: sentence-similarity
+tags:
+  - feature-extraction
+  - mteb
+  - sentence-similarity
+  - sentence-transformers
+
+model-index:
+- name: GIST-large-Embedding-v0
+  results:
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_counterfactual
+      name: MTEB AmazonCounterfactualClassification (en)
+      config: en
+      split: test
+      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
+    metrics:
+    - type: accuracy
+      value: 75.5820895522388
+    - type: ap
+      value: 38.32190121241783
+    - type: f1
+      value: 69.44777155231054
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_polarity
+      name: MTEB AmazonPolarityClassification
+      config: default
+      split: test
+      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
+    metrics:
+    - type: accuracy
+      value: 93.40514999999998
+    - type: ap
+      value: 90.2011565132406
+    - type: f1
+      value: 93.39486246843605
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_reviews_multi
+      name: MTEB AmazonReviewsClassification (en)
+      config: en
+      split: test
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+    metrics:
+    - type: accuracy
+      value: 49.05999999999999
+    - type: f1
+      value: 48.58702718571088
+  - task:
+      type: Retrieval
+    dataset:
+      type: arguana
+      name: MTEB ArguAna
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 38.407000000000004
+    - type: map_at_10
+      value: 54.822
+    - type: map_at_100
+      value: 55.387
+    - type: map_at_1000
+      value: 55.388999999999996
+    - type: map_at_3
+      value: 50.308
+    - type: map_at_5
+      value: 53.199
+    - type: mrr_at_1
+      value: 39.900000000000006
+    - type: mrr_at_10
+      value: 55.385
+    - type: mrr_at_100
+      value: 55.936
+    - type: mrr_at_1000
+      value: 55.93900000000001
+    - type: mrr_at_3
+      value: 50.853
+    - type: mrr_at_5
+      value: 53.738
+    - type: ndcg_at_1
+      value: 38.407000000000004
+    - type: ndcg_at_10
+      value: 63.38
+    - type: ndcg_at_100
+      value: 65.52900000000001
+    - type: ndcg_at_1000
+      value: 65.58800000000001
+    - type: ndcg_at_3
+      value: 54.26
+    - type: ndcg_at_5
+      value: 59.488
+    - type: precision_at_1
+      value: 38.407000000000004
+    - type: precision_at_10
+      value: 9.04
+    - type: precision_at_100
+      value: 0.992
+    - type: precision_at_1000
+      value: 0.1
+    - type: precision_at_3
+      value: 21.906
+    - type: precision_at_5
+      value: 15.690000000000001
+    - type: recall_at_1
+      value: 38.407000000000004
+    - type: recall_at_10
+      value: 90.398
+    - type: recall_at_100
+      value: 99.21799999999999
+    - type: recall_at_1000
+      value: 99.644
+    - type: recall_at_3
+      value: 65.718
+    - type: recall_at_5
+      value: 78.45
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-p2p
+      name: MTEB ArxivClusteringP2P
+      config: default
+      split: test
+      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
+    metrics:
+    - type: v_measure
+      value: 48.49766333679089
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-s2s
+      name: MTEB ArxivClusteringS2S
+      config: default
+      split: test
+      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
+    metrics:
+    - type: v_measure
+      value: 42.57731111438094
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/askubuntudupquestions-reranking
+      name: MTEB AskUbuntuDupQuestions
+      config: default
+      split: test
+      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
+    metrics:
+    - type: map
+      value: 64.70120072857361
+    - type: mrr
+      value: 77.86714593501297
+  - task:
+      type: STS
+    dataset:
+      type: mteb/biosses-sts
+      name: MTEB BIOSSES
+      config: default
+      split: test
+      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
+    metrics:
+    - type: cos_sim_pearson
+      value: 90.73821860690765
+    - type: cos_sim_spearman
+      value: 89.17070651383446
+    - type: euclidean_pearson
+      value: 88.28303958293029
+    - type: euclidean_spearman
+      value: 88.81889126856979
+    - type: manhattan_pearson
+      value: 88.09080621828731
+    - type: manhattan_spearman
+      value: 88.55924679817751
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/banking77
+      name: MTEB Banking77Classification
+      config: default
+      split: test
+      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
+    metrics:
+    - type: accuracy
+      value: 88.10064935064933
+    - type: f1
+      value: 88.08460758973867
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-p2p
+      name: MTEB BiorxivClusteringP2P
+      config: default
+      split: test
+      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
+    metrics:
+    - type: v_measure
+      value: 39.338228337929976
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-s2s
+      name: MTEB BiorxivClusteringS2S
+      config: default
+      split: test
+      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
+    metrics:
+    - type: v_measure
+      value: 36.179156232378226
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackAndroidRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 33.440999999999995
+    - type: map_at_10
+      value: 45.495000000000005
+    - type: map_at_100
+      value: 47.132000000000005
+    - type: map_at_1000
+      value: 47.253
+    - type: map_at_3
+      value: 41.766
+    - type: map_at_5
+      value: 43.873
+    - type: mrr_at_1
+      value: 40.772999999999996
+    - type: mrr_at_10
+      value: 51.627
+    - type: mrr_at_100
+      value: 52.364
+    - type: mrr_at_1000
+      value: 52.397000000000006
+    - type: mrr_at_3
+      value: 48.951
+    - type: mrr_at_5
+      value: 50.746
+    - type: ndcg_at_1
+      value: 40.772999999999996
+    - type: ndcg_at_10
+      value: 52.306
+    - type: ndcg_at_100
+      value: 57.753
+    - type: ndcg_at_1000
+      value: 59.36900000000001
+    - type: ndcg_at_3
+      value: 47.177
+    - type: ndcg_at_5
+      value: 49.71
+    - type: precision_at_1
+      value: 40.772999999999996
+    - type: precision_at_10
+      value: 10.129000000000001
+    - type: precision_at_100
+      value: 1.617
+    - type: precision_at_1000
+      value: 0.208
+    - type: precision_at_3
+      value: 22.985
+    - type: precision_at_5
+      value: 16.652
+    - type: recall_at_1
+      value: 33.440999999999995
+    - type: recall_at_10
+      value: 65.121
+    - type: recall_at_100
+      value: 87.55199999999999
+    - type: recall_at_1000
+      value: 97.41300000000001
+    - type: recall_at_3
+      value: 49.958999999999996
+    - type: recall_at_5
+      value: 57.14900000000001
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackEnglishRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 32.126
+    - type: map_at_10
+      value: 42.856
+    - type: map_at_100
+      value: 44.134
+    - type: map_at_1000
+      value: 44.274
+    - type: map_at_3
+      value: 39.594
+    - type: map_at_5
+      value: 41.504999999999995
+    - type: mrr_at_1
+      value: 40.127
+    - type: mrr_at_10
+      value: 48.736000000000004
+    - type: mrr_at_100
+      value: 49.303999999999995
+    - type: mrr_at_1000
+      value: 49.356
+    - type: mrr_at_3
+      value: 46.263
+    - type: mrr_at_5
+      value: 47.878
+    - type: ndcg_at_1
+      value: 40.127
+    - type: ndcg_at_10
+      value: 48.695
+    - type: ndcg_at_100
+      value: 52.846000000000004
+    - type: ndcg_at_1000
+      value: 54.964
+    - type: ndcg_at_3
+      value: 44.275
+    - type: ndcg_at_5
+      value: 46.54
+    - type: precision_at_1
+      value: 40.127
+    - type: precision_at_10
+      value: 9.229
+    - type: precision_at_100
+      value: 1.473
+    - type: precision_at_1000
+      value: 0.19499999999999998
+    - type: precision_at_3
+      value: 21.444
+    - type: precision_at_5
+      value: 15.389
+    - type: recall_at_1
+      value: 32.126
+    - type: recall_at_10
+      value: 58.971
+    - type: recall_at_100
+      value: 76.115
+    - type: recall_at_1000
+      value: 89.556
+    - type: recall_at_3
+      value: 45.891
+    - type: recall_at_5
+      value: 52.242
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGamingRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 41.312
+    - type: map_at_10
+      value: 54.510000000000005
+    - type: map_at_100
+      value: 55.544000000000004
+    - type: map_at_1000
+      value: 55.593
+    - type: map_at_3
+      value: 50.859
+    - type: map_at_5
+      value: 52.839999999999996
+    - type: mrr_at_1
+      value: 47.147
+    - type: mrr_at_10
+      value: 57.678
+    - type: mrr_at_100
+      value: 58.287
+    - type: mrr_at_1000
+      value: 58.312
+    - type: mrr_at_3
+      value: 55.025999999999996
+    - type: mrr_at_5
+      value: 56.55
+    - type: ndcg_at_1
+      value: 47.147
+    - type: ndcg_at_10
+      value: 60.672000000000004
+    - type: ndcg_at_100
+      value: 64.411
+    - type: ndcg_at_1000
+      value: 65.35499999999999
+    - type: ndcg_at_3
+      value: 54.643
+    - type: ndcg_at_5
+      value: 57.461
+    - type: precision_at_1
+      value: 47.147
+    - type: precision_at_10
+      value: 9.881
+    - type: precision_at_100
+      value: 1.27
+    - type: precision_at_1000
+      value: 0.13799999999999998
+    - type: precision_at_3
+      value: 24.556
+    - type: precision_at_5
+      value: 16.814999999999998
+    - type: recall_at_1
+      value: 41.312
+    - type: recall_at_10
+      value: 75.62299999999999
+    - type: recall_at_100
+      value: 91.388
+    - type: recall_at_1000
+      value: 98.08
+    - type: recall_at_3
+      value: 59.40299999999999
+    - type: recall_at_5
+      value: 66.43900000000001
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackGisRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 27.609
+    - type: map_at_10
+      value: 37.614
+    - type: map_at_100
+      value: 38.584
+    - type: map_at_1000
+      value: 38.652
+    - type: map_at_3
+      value: 34.731
+    - type: map_at_5
+      value: 36.308
+    - type: mrr_at_1
+      value: 29.944
+    - type: mrr_at_10
+      value: 39.829
+    - type: mrr_at_100
+      value: 40.659
+    - type: mrr_at_1000
+      value: 40.709
+    - type: mrr_at_3
+      value: 37.269000000000005
+    - type: mrr_at_5
+      value: 38.625
+    - type: ndcg_at_1
+      value: 29.944
+    - type: ndcg_at_10
+      value: 43.082
+    - type: ndcg_at_100
+      value: 47.857
+    - type: ndcg_at_1000
+      value: 49.612
+    - type: ndcg_at_3
+      value: 37.578
+    - type: ndcg_at_5
+      value: 40.135
+    - type: precision_at_1
+      value: 29.944
+    - type: precision_at_10
+      value: 6.678000000000001
+    - type: precision_at_100
+      value: 0.951
+    - type: precision_at_1000
+      value: 0.11399999999999999
+    - type: precision_at_3
+      value: 16.045
+    - type: precision_at_5
+      value: 11.073
+    - type: recall_at_1
+      value: 27.609
+    - type: recall_at_10
+      value: 57.718
+    - type: recall_at_100
+      value: 79.768
+    - type: recall_at_1000
+      value: 92.868
+    - type: recall_at_3
+      value: 42.876
+    - type: recall_at_5
+      value: 49.104
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackMathematicaRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 18.071
+    - type: map_at_10
+      value: 27.471
+    - type: map_at_100
+      value: 28.71
+    - type: map_at_1000
+      value: 28.833
+    - type: map_at_3
+      value: 24.698
+    - type: map_at_5
+      value: 26.461000000000002
+    - type: mrr_at_1
+      value: 22.387999999999998
+    - type: mrr_at_10
+      value: 32.522
+    - type: mrr_at_100
+      value: 33.393
+    - type: mrr_at_1000
+      value: 33.455
+    - type: mrr_at_3
+      value: 29.830000000000002
+    - type: mrr_at_5
+      value: 31.472
+    - type: ndcg_at_1
+      value: 22.387999999999998
+    - type: ndcg_at_10
+      value: 33.278999999999996
+    - type: ndcg_at_100
+      value: 39.043
+    - type: ndcg_at_1000
+      value: 41.763
+    - type: ndcg_at_3
+      value: 28.310999999999996
+    - type: ndcg_at_5
+      value: 31.007
+    - type: precision_at_1
+      value: 22.387999999999998
+    - type: precision_at_10
+      value: 6.157
+    - type: precision_at_100
+      value: 1.042
+    - type: precision_at_1000
+      value: 0.14200000000000002
+    - type: precision_at_3
+      value: 13.972000000000001
+    - type: precision_at_5
+      value: 10.274
+    - type: recall_at_1
+      value: 18.071
+    - type: recall_at_10
+      value: 46.025
+    - type: recall_at_100
+      value: 71.153
+    - type: recall_at_1000
+      value: 90.232
+    - type: recall_at_3
+      value: 32.311
+    - type: recall_at_5
+      value: 39.296
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackPhysicsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 30.813000000000002
+    - type: map_at_10
+      value: 42.594
+    - type: map_at_100
+      value: 43.949
+    - type: map_at_1000
+      value: 44.052
+    - type: map_at_3
+      value: 39.1
+    - type: map_at_5
+      value: 41.111
+    - type: mrr_at_1
+      value: 37.824999999999996
+    - type: mrr_at_10
+      value: 48.06
+    - type: mrr_at_100
+      value: 48.91
+    - type: mrr_at_1000
+      value: 48.946
+    - type: mrr_at_3
+      value: 45.509
+    - type: mrr_at_5
+      value: 47.073
+    - type: ndcg_at_1
+      value: 37.824999999999996
+    - type: ndcg_at_10
+      value: 48.882
+    - type: ndcg_at_100
+      value: 54.330999999999996
+    - type: ndcg_at_1000
+      value: 56.120999999999995
+    - type: ndcg_at_3
+      value: 43.529
+    - type: ndcg_at_5
+      value: 46.217999999999996
+    - type: precision_at_1
+      value: 37.824999999999996
+    - type: precision_at_10
+      value: 8.845
+    - type: precision_at_100
+      value: 1.34
+    - type: precision_at_1000
+      value: 0.168
+    - type: precision_at_3
+      value: 20.757
+    - type: precision_at_5
+      value: 14.802999999999999
+    - type: recall_at_1
+      value: 30.813000000000002
+    - type: recall_at_10
+      value: 61.895999999999994
+    - type: recall_at_100
+      value: 84.513
+    - type: recall_at_1000
+      value: 95.817
+    - type: recall_at_3
+      value: 47.099000000000004
+    - type: recall_at_5
+      value: 54.031
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackProgrammersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 25.735999999999997
+    - type: map_at_10
+      value: 36.799
+    - type: map_at_100
+      value: 38.246
+    - type: map_at_1000
+      value: 38.353
+    - type: map_at_3
+      value: 33.133
+    - type: map_at_5
+      value: 34.954
+    - type: mrr_at_1
+      value: 31.849
+    - type: mrr_at_10
+      value: 41.928
+    - type: mrr_at_100
+      value: 42.846000000000004
+    - type: mrr_at_1000
+      value: 42.894
+    - type: mrr_at_3
+      value: 39.117000000000004
+    - type: mrr_at_5
+      value: 40.521
+    - type: ndcg_at_1
+      value: 31.849
+    - type: ndcg_at_10
+      value: 43.143
+    - type: ndcg_at_100
+      value: 48.963
+    - type: ndcg_at_1000
+      value: 51.041000000000004
+    - type: ndcg_at_3
+      value: 37.218
+    - type: ndcg_at_5
+      value: 39.542
+    - type: precision_at_1
+      value: 31.849
+    - type: precision_at_10
+      value: 8.231
+    - type: precision_at_100
+      value: 1.277
+    - type: precision_at_1000
+      value: 0.164
+    - type: precision_at_3
+      value: 18.037
+    - type: precision_at_5
+      value: 12.945
+    - type: recall_at_1
+      value: 25.735999999999997
+    - type: recall_at_10
+      value: 56.735
+    - type: recall_at_100
+      value: 81.04
+    - type: recall_at_1000
+      value: 94.845
+    - type: recall_at_3
+      value: 40.239999999999995
+    - type: recall_at_5
+      value: 46.378
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 27.580333333333336
+    - type: map_at_10
+      value: 37.70558333333334
+    - type: map_at_100
+      value: 38.94941666666667
+    - type: map_at_1000
+      value: 39.062083333333334
+    - type: map_at_3
+      value: 34.63333333333334
+    - type: map_at_5
+      value: 36.35241666666666
+    - type: mrr_at_1
+      value: 32.64866666666667
+    - type: mrr_at_10
+      value: 42.018499999999996
+    - type: mrr_at_100
+      value: 42.83391666666666
+    - type: mrr_at_1000
+      value: 42.884166666666665
+    - type: mrr_at_3
+      value: 39.476499999999994
+    - type: mrr_at_5
+      value: 40.96983333333334
+    - type: ndcg_at_1
+      value: 32.64866666666667
+    - type: ndcg_at_10
+      value: 43.43866666666667
+    - type: ndcg_at_100
+      value: 48.569833333333335
+    - type: ndcg_at_1000
+      value: 50.6495
+    - type: ndcg_at_3
+      value: 38.327166666666656
+    - type: ndcg_at_5
+      value: 40.76941666666667
+    - type: precision_at_1
+      value: 32.64866666666667
+    - type: precision_at_10
+      value: 7.652333333333332
+    - type: precision_at_100
+      value: 1.2066666666666666
+    - type: precision_at_1000
+      value: 0.15841666666666668
+    - type: precision_at_3
+      value: 17.75108333333333
+    - type: precision_at_5
+      value: 12.641916666666669
+    - type: recall_at_1
+      value: 27.580333333333336
+    - type: recall_at_10
+      value: 56.02591666666667
+    - type: recall_at_100
+      value: 78.317
+    - type: recall_at_1000
+      value: 92.52608333333332
+    - type: recall_at_3
+      value: 41.84283333333333
+    - type: recall_at_5
+      value: 48.105666666666664
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackStatsRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 27.876
+    - type: map_at_10
+      value: 34.521
+    - type: map_at_100
+      value: 35.581
+    - type: map_at_1000
+      value: 35.674
+    - type: map_at_3
+      value: 32.501000000000005
+    - type: map_at_5
+      value: 33.602
+    - type: mrr_at_1
+      value: 31.441999999999997
+    - type: mrr_at_10
+      value: 37.669999999999995
+    - type: mrr_at_100
+      value: 38.523
+    - type: mrr_at_1000
+      value: 38.59
+    - type: mrr_at_3
+      value: 35.762
+    - type: mrr_at_5
+      value: 36.812
+    - type: ndcg_at_1
+      value: 31.441999999999997
+    - type: ndcg_at_10
+      value: 38.46
+    - type: ndcg_at_100
+      value: 43.479
+    - type: ndcg_at_1000
+      value: 45.858
+    - type: ndcg_at_3
+      value: 34.668
+    - type: ndcg_at_5
+      value: 36.416
+    - type: precision_at_1
+      value: 31.441999999999997
+    - type: precision_at_10
+      value: 5.782
+    - type: precision_at_100
+      value: 0.91
+    - type: precision_at_1000
+      value: 0.11900000000000001
+    - type: precision_at_3
+      value: 14.417
+    - type: precision_at_5
+      value: 9.876999999999999
+    - type: recall_at_1
+      value: 27.876
+    - type: recall_at_10
+      value: 47.556
+    - type: recall_at_100
+      value: 70.39699999999999
+    - type: recall_at_1000
+      value: 87.969
+    - type: recall_at_3
+      value: 37.226
+    - type: recall_at_5
+      value: 41.43
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackTexRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 18.854000000000003
+    - type: map_at_10
+      value: 26.632
+    - type: map_at_100
+      value: 27.849
+    - type: map_at_1000
+      value: 27.977
+    - type: map_at_3
+      value: 24.089
+    - type: map_at_5
+      value: 25.477
+    - type: mrr_at_1
+      value: 22.987
+    - type: mrr_at_10
+      value: 30.781999999999996
+    - type: mrr_at_100
+      value: 31.746000000000002
+    - type: mrr_at_1000
+      value: 31.818
+    - type: mrr_at_3
+      value: 28.43
+    - type: mrr_at_5
+      value: 29.791
+    - type: ndcg_at_1
+      value: 22.987
+    - type: ndcg_at_10
+      value: 31.585
+    - type: ndcg_at_100
+      value: 37.32
+    - type: ndcg_at_1000
+      value: 40.072
+    - type: ndcg_at_3
+      value: 27.058
+    - type: ndcg_at_5
+      value: 29.137999999999998
+    - type: precision_at_1
+      value: 22.987
+    - type: precision_at_10
+      value: 5.76
+    - type: precision_at_100
+      value: 1.018
+    - type: precision_at_1000
+      value: 0.14400000000000002
+    - type: precision_at_3
+      value: 12.767000000000001
+    - type: precision_at_5
+      value: 9.257
+    - type: recall_at_1
+      value: 18.854000000000003
+    - type: recall_at_10
+      value: 42.349
+    - type: recall_at_100
+      value: 68.15299999999999
+    - type: recall_at_1000
+      value: 87.44
+    - type: recall_at_3
+      value: 29.715999999999998
+    - type: recall_at_5
+      value: 35.085
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackUnixRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 28.094
+    - type: map_at_10
+      value: 38.22
+    - type: map_at_100
+      value: 39.352
+    - type: map_at_1000
+      value: 39.452
+    - type: map_at_3
+      value: 35.339
+    - type: map_at_5
+      value: 36.78
+    - type: mrr_at_1
+      value: 33.022
+    - type: mrr_at_10
+      value: 42.466
+    - type: mrr_at_100
+      value: 43.3
+    - type: mrr_at_1000
+      value: 43.356
+    - type: mrr_at_3
+      value: 40.159
+    - type: mrr_at_5
+      value: 41.272999999999996
+    - type: ndcg_at_1
+      value: 33.022
+    - type: ndcg_at_10
+      value: 43.976
+    - type: ndcg_at_100
+      value: 49.008
+    - type: ndcg_at_1000
+      value: 51.154999999999994
+    - type: ndcg_at_3
+      value: 38.891
+    - type: ndcg_at_5
+      value: 40.897
+    - type: precision_at_1
+      value: 33.022
+    - type: precision_at_10
+      value: 7.396999999999999
+    - type: precision_at_100
+      value: 1.1199999999999999
+    - type: precision_at_1000
+      value: 0.14200000000000002
+    - type: precision_at_3
+      value: 17.724
+    - type: precision_at_5
+      value: 12.239
+    - type: recall_at_1
+      value: 28.094
+    - type: recall_at_10
+      value: 57.162
+    - type: recall_at_100
+      value: 78.636
+    - type: recall_at_1000
+      value: 93.376
+    - type: recall_at_3
+      value: 43.328
+    - type: recall_at_5
+      value: 48.252
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWebmastersRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 24.937
+    - type: map_at_10
+      value: 34.82
+    - type: map_at_100
+      value: 36.405
+    - type: map_at_1000
+      value: 36.626
+    - type: map_at_3
+      value: 31.548
+    - type: map_at_5
+      value: 33.355000000000004
+    - type: mrr_at_1
+      value: 30.435000000000002
+    - type: mrr_at_10
+      value: 39.946
+    - type: mrr_at_100
+      value: 40.873
+    - type: mrr_at_1000
+      value: 40.910000000000004
+    - type: mrr_at_3
+      value: 37.088
+    - type: mrr_at_5
+      value: 38.808
+    - type: ndcg_at_1
+      value: 30.435000000000002
+    - type: ndcg_at_10
+      value: 41.25
+    - type: ndcg_at_100
+      value: 47.229
+    - type: ndcg_at_1000
+      value: 49.395
+    - type: ndcg_at_3
+      value: 35.801
+    - type: ndcg_at_5
+      value: 38.457
+    - type: precision_at_1
+      value: 30.435000000000002
+    - type: precision_at_10
+      value: 8.083
+    - type: precision_at_100
+      value: 1.601
+    - type: precision_at_1000
+      value: 0.247
+    - type: precision_at_3
+      value: 17.061999999999998
+    - type: precision_at_5
+      value: 12.767000000000001
+    - type: recall_at_1
+      value: 24.937
+    - type: recall_at_10
+      value: 53.905
+    - type: recall_at_100
+      value: 80.607
+    - type: recall_at_1000
+      value: 93.728
+    - type: recall_at_3
+      value: 38.446000000000005
+    - type: recall_at_5
+      value: 45.188
+  - task:
+      type: Retrieval
+    dataset:
+      type: BeIR/cqadupstack
+      name: MTEB CQADupstackWordpressRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 22.095000000000002
+    - type: map_at_10
+      value: 30.935000000000002
+    - type: map_at_100
+      value: 31.907000000000004
+    - type: map_at_1000
+      value: 32.006
+    - type: map_at_3
+      value: 28.242
+    - type: map_at_5
+      value: 29.963
+    - type: mrr_at_1
+      value: 23.845
+    - type: mrr_at_10
+      value: 32.978
+    - type: mrr_at_100
+      value: 33.802
+    - type: mrr_at_1000
+      value: 33.867000000000004
+    - type: mrr_at_3
+      value: 30.314000000000004
+    - type: mrr_at_5
+      value: 32.089
+    - type: ndcg_at_1
+      value: 23.845
+    - type: ndcg_at_10
+      value: 35.934
+    - type: ndcg_at_100
+      value: 40.598
+    - type: ndcg_at_1000
+      value: 43.089
+    - type: ndcg_at_3
+      value: 30.776999999999997
+    - type: ndcg_at_5
+      value: 33.711999999999996
+    - type: precision_at_1
+      value: 23.845
+    - type: precision_at_10
+      value: 5.656
+    - type: precision_at_100
+      value: 0.861
+    - type: precision_at_1000
+      value: 0.12
+    - type: precision_at_3
+      value: 13.247
+    - type: precision_at_5
+      value: 9.612
+    - type: recall_at_1
+      value: 22.095000000000002
+    - type: recall_at_10
+      value: 49.25
+    - type: recall_at_100
+      value: 70.482
+    - type: recall_at_1000
+      value: 88.98899999999999
+    - type: recall_at_3
+      value: 35.619
+    - type: recall_at_5
+      value: 42.674
+  - task:
+      type: Retrieval
+    dataset:
+      type: climate-fever
+      name: MTEB ClimateFEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 14.154
+    - type: map_at_10
+      value: 24.654999999999998
+    - type: map_at_100
+      value: 26.723999999999997
+    - type: map_at_1000
+      value: 26.912000000000003
+    - type: map_at_3
+      value: 20.4
+    - type: map_at_5
+      value: 22.477
+    - type: mrr_at_1
+      value: 32.117000000000004
+    - type: mrr_at_10
+      value: 44.590999999999994
+    - type: mrr_at_100
+      value: 45.425
+    - type: mrr_at_1000
+      value: 45.456
+    - type: mrr_at_3
+      value: 41.281
+    - type: mrr_at_5
+      value: 43.219
+    - type: ndcg_at_1
+      value: 32.117000000000004
+    - type: ndcg_at_10
+      value: 33.994
+    - type: ndcg_at_100
+      value: 41.438
+    - type: ndcg_at_1000
+      value: 44.611000000000004
+    - type: ndcg_at_3
+      value: 27.816000000000003
+    - type: ndcg_at_5
+      value: 29.816
+    - type: precision_at_1
+      value: 32.117000000000004
+    - type: precision_at_10
+      value: 10.756
+    - type: precision_at_100
+      value: 1.8679999999999999
+    - type: precision_at_1000
+      value: 0.246
+    - type: precision_at_3
+      value: 20.803
+    - type: precision_at_5
+      value: 15.987000000000002
+    - type: recall_at_1
+      value: 14.154
+    - type: recall_at_10
+      value: 40.489999999999995
+    - type: recall_at_100
+      value: 65.635
+    - type: recall_at_1000
+      value: 83.276
+    - type: recall_at_3
+      value: 25.241000000000003
+    - type: recall_at_5
+      value: 31.211
+  - task:
+      type: Retrieval
+    dataset:
+      type: dbpedia-entity
+      name: MTEB DBPedia
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 9.332
+    - type: map_at_10
+      value: 20.462
+    - type: map_at_100
+      value: 29.473
+    - type: map_at_1000
+      value: 31.215
+    - type: map_at_3
+      value: 14.466999999999999
+    - type: map_at_5
+      value: 16.922
+    - type: mrr_at_1
+      value: 69.5
+    - type: mrr_at_10
+      value: 77.039
+    - type: mrr_at_100
+      value: 77.265
+    - type: mrr_at_1000
+      value: 77.271
+    - type: mrr_at_3
+      value: 75.5
+    - type: mrr_at_5
+      value: 76.4
+    - type: ndcg_at_1
+      value: 57.125
+    - type: ndcg_at_10
+      value: 42.958
+    - type: ndcg_at_100
+      value: 48.396
+    - type: ndcg_at_1000
+      value: 55.897
+    - type: ndcg_at_3
+      value: 47.188
+    - type: ndcg_at_5
+      value: 44.376
+    - type: precision_at_1
+      value: 69.5
+    - type: precision_at_10
+      value: 34.5
+    - type: precision_at_100
+      value: 11.18
+    - type: precision_at_1000
+      value: 2.13
+    - type: precision_at_3
+      value: 51.083
+    - type: precision_at_5
+      value: 43.1
+    - type: recall_at_1
+      value: 9.332
+    - type: recall_at_10
+      value: 26.422
+    - type: recall_at_100
+      value: 56.098000000000006
+    - type: recall_at_1000
+      value: 79.66
+    - type: recall_at_3
+      value: 15.703
+    - type: recall_at_5
+      value: 19.644000000000002
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/emotion
+      name: MTEB EmotionClassification
+      config: default
+      split: test
+      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
+    metrics:
+    - type: accuracy
+      value: 54.72
+    - type: f1
+      value: 49.67819606587526
+  - task:
+      type: Retrieval
+    dataset:
+      type: fever
+      name: MTEB FEVER
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 74.97
+    - type: map_at_10
+      value: 82.956
+    - type: map_at_100
+      value: 83.193
+    - type: map_at_1000
+      value: 83.208
+    - type: map_at_3
+      value: 81.837
+    - type: map_at_5
+      value: 82.57
+    - type: mrr_at_1
+      value: 80.783
+    - type: mrr_at_10
+      value: 87.546
+    - type: mrr_at_100
+      value: 87.627
+    - type: mrr_at_1000
+      value: 87.63
+    - type: mrr_at_3
+      value: 86.79400000000001
+    - type: mrr_at_5
+      value: 87.32799999999999
+    - type: ndcg_at_1
+      value: 80.783
+    - type: ndcg_at_10
+      value: 86.54899999999999
+    - type: ndcg_at_100
+      value: 87.355
+    - type: ndcg_at_1000
+      value: 87.629
+    - type: ndcg_at_3
+      value: 84.82
+    - type: ndcg_at_5
+      value: 85.83800000000001
+    - type: precision_at_1
+      value: 80.783
+    - type: precision_at_10
+      value: 10.327
+    - type: precision_at_100
+      value: 1.094
+    - type: precision_at_1000
+      value: 0.11299999999999999
+    - type: precision_at_3
+      value: 32.218
+    - type: precision_at_5
+      value: 20.012
+    - type: recall_at_1
+      value: 74.97
+    - type: recall_at_10
+      value: 93.072
+    - type: recall_at_100
+      value: 96.218
+    - type: recall_at_1000
+      value: 97.991
+    - type: recall_at_3
+      value: 88.357
+    - type: recall_at_5
+      value: 90.983
+  - task:
+      type: Retrieval
+    dataset:
+      type: fiqa
+      name: MTEB FiQA2018
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 21.12
+    - type: map_at_10
+      value: 35.908
+    - type: map_at_100
+      value: 37.895
+    - type: map_at_1000
+      value: 38.068000000000005
+    - type: map_at_3
+      value: 31.189
+    - type: map_at_5
+      value: 33.908
+    - type: mrr_at_1
+      value: 42.901
+    - type: mrr_at_10
+      value: 52.578
+    - type: mrr_at_100
+      value: 53.308
+    - type: mrr_at_1000
+      value: 53.342
+    - type: mrr_at_3
+      value: 50.385999999999996
+    - type: mrr_at_5
+      value: 51.62799999999999
+    - type: ndcg_at_1
+      value: 42.901
+    - type: ndcg_at_10
+      value: 44.302
+    - type: ndcg_at_100
+      value: 51.132999999999996
+    - type: ndcg_at_1000
+      value: 53.848
+    - type: ndcg_at_3
+      value: 40.464
+    - type: ndcg_at_5
+      value: 41.743
+    - type: precision_at_1
+      value: 42.901
+    - type: precision_at_10
+      value: 12.423
+    - type: precision_at_100
+      value: 1.968
+    - type: precision_at_1000
+      value: 0.246
+    - type: precision_at_3
+      value: 27.622999999999998
+    - type: precision_at_5
+      value: 20.278
+    - type: recall_at_1
+      value: 21.12
+    - type: recall_at_10
+      value: 52.091
+    - type: recall_at_100
+      value: 77.062
+    - type: recall_at_1000
+      value: 93.082
+    - type: recall_at_3
+      value: 37.223
+    - type: recall_at_5
+      value: 43.826
+  - task:
+      type: Retrieval
+    dataset:
+      type: hotpotqa
+      name: MTEB HotpotQA
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 38.940000000000005
+    - type: map_at_10
+      value: 62.239999999999995
+    - type: map_at_100
+      value: 63.141000000000005
+    - type: map_at_1000
+      value: 63.205999999999996
+    - type: map_at_3
+      value: 58.738
+    - type: map_at_5
+      value: 60.924
+    - type: mrr_at_1
+      value: 77.88000000000001
+    - type: mrr_at_10
+      value: 83.7
+    - type: mrr_at_100
+      value: 83.882
+    - type: mrr_at_1000
+      value: 83.889
+    - type: mrr_at_3
+      value: 82.748
+    - type: mrr_at_5
+      value: 83.381
+    - type: ndcg_at_1
+      value: 77.88000000000001
+    - type: ndcg_at_10
+      value: 70.462
+    - type: ndcg_at_100
+      value: 73.564
+    - type: ndcg_at_1000
+      value: 74.78099999999999
+    - type: ndcg_at_3
+      value: 65.524
+    - type: ndcg_at_5
+      value: 68.282
+    - type: precision_at_1
+      value: 77.88000000000001
+    - type: precision_at_10
+      value: 14.81
+    - type: precision_at_100
+      value: 1.7229999999999999
+    - type: precision_at_1000
+      value: 0.188
+    - type: precision_at_3
+      value: 42.083999999999996
+    - type: precision_at_5
+      value: 27.43
+    - type: recall_at_1
+      value: 38.940000000000005
+    - type: recall_at_10
+      value: 74.051
+    - type: recall_at_100
+      value: 86.158
+    - type: recall_at_1000
+      value: 94.146
+    - type: recall_at_3
+      value: 63.126000000000005
+    - type: recall_at_5
+      value: 68.575
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/imdb
+      name: MTEB ImdbClassification
+      config: default
+      split: test
+      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
+    metrics:
+    - type: accuracy
+      value: 91.23440000000001
+    - type: ap
+      value: 87.33490392265892
+    - type: f1
+      value: 91.21374626021836
+  - task:
+      type: Retrieval
+    dataset:
+      type: msmarco
+      name: MTEB MSMARCO
+      config: default
+      split: dev
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 22.137999999999998
+    - type: map_at_10
+      value: 34.471000000000004
+    - type: map_at_100
+      value: 35.634
+    - type: map_at_1000
+      value: 35.685
+    - type: map_at_3
+      value: 30.587999999999997
+    - type: map_at_5
+      value: 32.812999999999995
+    - type: mrr_at_1
+      value: 22.736
+    - type: mrr_at_10
+      value: 35.092
+    - type: mrr_at_100
+      value: 36.193999999999996
+    - type: mrr_at_1000
+      value: 36.238
+    - type: mrr_at_3
+      value: 31.28
+    - type: mrr_at_5
+      value: 33.498
+    - type: ndcg_at_1
+      value: 22.736
+    - type: ndcg_at_10
+      value: 41.388999999999996
+    - type: ndcg_at_100
+      value: 46.967999999999996
+    - type: ndcg_at_1000
+      value: 48.178
+    - type: ndcg_at_3
+      value: 33.503
+    - type: ndcg_at_5
+      value: 37.484
+    - type: precision_at_1
+      value: 22.736
+    - type: precision_at_10
+      value: 6.54
+    - type: precision_at_100
+      value: 0.9339999999999999
+    - type: precision_at_1000
+      value: 0.104
+    - type: precision_at_3
+      value: 14.249999999999998
+    - type: precision_at_5
+      value: 10.562000000000001
+    - type: recall_at_1
+      value: 22.137999999999998
+    - type: recall_at_10
+      value: 62.629999999999995
+    - type: recall_at_100
+      value: 88.375
+    - type: recall_at_1000
+      value: 97.529
+    - type: recall_at_3
+      value: 41.245
+    - type: recall_at_5
+      value: 50.808
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_domain
+      name: MTEB MTOPDomainClassification (en)
+      config: en
+      split: test
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+    metrics:
+    - type: accuracy
+      value: 95.25079799361606
+    - type: f1
+      value: 95.00726023695032
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_intent
+      name: MTEB MTOPIntentClassification (en)
+      config: en
+      split: test
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+    metrics:
+    - type: accuracy
+      value: 78.23757409940721
+    - type: f1
+      value: 58.534958803195714
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (en)
+      config: en
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 76.20040349697378
+    - type: f1
+      value: 74.31261149784696
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (en)
+      config: en
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 79.35104236718227
+    - type: f1
+      value: 79.7373049864316
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-p2p
+      name: MTEB MedrxivClusteringP2P
+      config: default
+      split: test
+      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
+    metrics:
+    - type: v_measure
+      value: 34.478828180753126
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-s2s
+      name: MTEB MedrxivClusteringS2S
+      config: default
+      split: test
+      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
+    metrics:
+    - type: v_measure
+      value: 32.25696147904426
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/mind_small
+      name: MTEB MindSmallReranking
+      config: default
+      split: test
+      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
+    metrics:
+    - type: map
+      value: 32.82488548405117
+    - type: mrr
+      value: 34.066706809031096
+  - task:
+      type: Retrieval
+    dataset:
+      type: nfcorpus
+      name: MTEB NFCorpus
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 6.557
+    - type: map_at_10
+      value: 15.055
+    - type: map_at_100
+      value: 19.575
+    - type: map_at_1000
+      value: 21.267
+    - type: map_at_3
+      value: 10.86
+    - type: map_at_5
+      value: 12.83
+    - type: mrr_at_1
+      value: 50.464
+    - type: mrr_at_10
+      value: 59.050999999999995
+    - type: mrr_at_100
+      value: 59.436
+    - type: mrr_at_1000
+      value: 59.476
+    - type: mrr_at_3
+      value: 56.811
+    - type: mrr_at_5
+      value: 58.08
+    - type: ndcg_at_1
+      value: 47.988
+    - type: ndcg_at_10
+      value: 38.645
+    - type: ndcg_at_100
+      value: 36.339
+    - type: ndcg_at_1000
+      value: 45.279
+    - type: ndcg_at_3
+      value: 43.35
+    - type: ndcg_at_5
+      value: 41.564
+    - type: precision_at_1
+      value: 49.845
+    - type: precision_at_10
+      value: 28.544999999999998
+    - type: precision_at_100
+      value: 9.322
+    - type: precision_at_1000
+      value: 2.258
+    - type: precision_at_3
+      value: 40.144000000000005
+    - type: precision_at_5
+      value: 35.913000000000004
+    - type: recall_at_1
+      value: 6.557
+    - type: recall_at_10
+      value: 19.5
+    - type: recall_at_100
+      value: 37.153999999999996
+    - type: recall_at_1000
+      value: 69.581
+    - type: recall_at_3
+      value: 12.133
+    - type: recall_at_5
+      value: 15.43
+  - task:
+      type: Retrieval
+    dataset:
+      type: nq
+      name: MTEB NQ
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 31.740000000000002
+    - type: map_at_10
+      value: 48.150999999999996
+    - type: map_at_100
+      value: 49.125
+    - type: map_at_1000
+      value: 49.149
+    - type: map_at_3
+      value: 43.645
+    - type: map_at_5
+      value: 46.417
+    - type: mrr_at_1
+      value: 35.892
+    - type: mrr_at_10
+      value: 50.524
+    - type: mrr_at_100
+      value: 51.232
+    - type: mrr_at_1000
+      value: 51.24999999999999
+    - type: mrr_at_3
+      value: 46.852
+    - type: mrr_at_5
+      value: 49.146
+    - type: ndcg_at_1
+      value: 35.892
+    - type: ndcg_at_10
+      value: 56.08800000000001
+    - type: ndcg_at_100
+      value: 60.077000000000005
+    - type: ndcg_at_1000
+      value: 60.632
+    - type: ndcg_at_3
+      value: 47.765
+    - type: ndcg_at_5
+      value: 52.322
+    - type: precision_at_1
+      value: 35.892
+    - type: precision_at_10
+      value: 9.296
+    - type: precision_at_100
+      value: 1.154
+    - type: precision_at_1000
+      value: 0.12
+    - type: precision_at_3
+      value: 21.92
+    - type: precision_at_5
+      value: 15.781999999999998
+    - type: recall_at_1
+      value: 31.740000000000002
+    - type: recall_at_10
+      value: 77.725
+    - type: recall_at_100
+      value: 94.841
+    - type: recall_at_1000
+      value: 99.003
+    - type: recall_at_3
+      value: 56.407
+    - type: recall_at_5
+      value: 66.848
+  - task:
+      type: Retrieval
+    dataset:
+      type: quora
+      name: MTEB QuoraRetrieval
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 71.429
+    - type: map_at_10
+      value: 85.42699999999999
+    - type: map_at_100
+      value: 86.063
+    - type: map_at_1000
+      value: 86.077
+    - type: map_at_3
+      value: 82.573
+    - type: map_at_5
+      value: 84.371
+    - type: mrr_at_1
+      value: 82.34
+    - type: mrr_at_10
+      value: 88.247
+    - type: mrr_at_100
+      value: 88.357
+    - type: mrr_at_1000
+      value: 88.357
+    - type: mrr_at_3
+      value: 87.38
+    - type: mrr_at_5
+      value: 87.981
+    - type: ndcg_at_1
+      value: 82.34
+    - type: ndcg_at_10
+      value: 88.979
+    - type: ndcg_at_100
+      value: 90.18599999999999
+    - type: ndcg_at_1000
+      value: 90.254
+    - type: ndcg_at_3
+      value: 86.378
+    - type: ndcg_at_5
+      value: 87.821
+    - type: precision_at_1
+      value: 82.34
+    - type: precision_at_10
+      value: 13.482
+    - type: precision_at_100
+      value: 1.537
+    - type: precision_at_1000
+      value: 0.157
+    - type: precision_at_3
+      value: 37.852999999999994
+    - type: precision_at_5
+      value: 24.798000000000002
+    - type: recall_at_1
+      value: 71.429
+    - type: recall_at_10
+      value: 95.64099999999999
+    - type: recall_at_100
+      value: 99.723
+    - type: recall_at_1000
+      value: 99.98
+    - type: recall_at_3
+      value: 88.011
+    - type: recall_at_5
+      value: 92.246
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering
+      name: MTEB RedditClustering
+      config: default
+      split: test
+      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
+    metrics:
+    - type: v_measure
+      value: 60.62148584103299
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering-p2p
+      name: MTEB RedditClusteringP2P
+      config: default
+      split: test
+      revision: 282350215ef01743dc01b456c7f5241fa8937f16
+    metrics:
+    - type: v_measure
+      value: 63.2923987272903
+  - task:
+      type: Retrieval
+    dataset:
+      type: scidocs
+      name: MTEB SCIDOCS
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 5.128
+    - type: map_at_10
+      value: 14.63
+    - type: map_at_100
+      value: 17.285
+    - type: map_at_1000
+      value: 17.676
+    - type: map_at_3
+      value: 9.993
+    - type: map_at_5
+      value: 12.286999999999999
+    - type: mrr_at_1
+      value: 25.4
+    - type: mrr_at_10
+      value: 38.423
+    - type: mrr_at_100
+      value: 39.497
+    - type: mrr_at_1000
+      value: 39.531
+    - type: mrr_at_3
+      value: 34.9
+    - type: mrr_at_5
+      value: 37.01
+    - type: ndcg_at_1
+      value: 25.4
+    - type: ndcg_at_10
+      value: 24.062
+    - type: ndcg_at_100
+      value: 33.823
+    - type: ndcg_at_1000
+      value: 39.663
+    - type: ndcg_at_3
+      value: 22.246
+    - type: ndcg_at_5
+      value: 19.761
+    - type: precision_at_1
+      value: 25.4
+    - type: precision_at_10
+      value: 12.85
+    - type: precision_at_100
+      value: 2.71
+    - type: precision_at_1000
+      value: 0.41000000000000003
+    - type: precision_at_3
+      value: 21.4
+    - type: precision_at_5
+      value: 17.86
+    - type: recall_at_1
+      value: 5.128
+    - type: recall_at_10
+      value: 26.06
+    - type: recall_at_100
+      value: 54.993
+    - type: recall_at_1000
+      value: 83.165
+    - type: recall_at_3
+      value: 13.003
+    - type: recall_at_5
+      value: 18.117
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sickr-sts
+      name: MTEB SICK-R
+      config: default
+      split: test
+      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
+    metrics:
+    - type: cos_sim_pearson
+      value: 87.5466779326323
+    - type: cos_sim_spearman
+      value: 82.79782085421951
+    - type: euclidean_pearson
+      value: 84.76929982677339
+    - type: euclidean_spearman
+      value: 82.51802536005597
+    - type: manhattan_pearson
+      value: 84.76736312526177
+    - type: manhattan_spearman
+      value: 82.50799656335593
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts12-sts
+      name: MTEB STS12
+      config: default
+      split: test
+      revision: a0d554a64d88156834ff5ae9920b964011b16384
+    metrics:
+    - type: cos_sim_pearson
+      value: 86.40486308108694
+    - type: cos_sim_spearman
+      value: 77.12670500926937
+    - type: euclidean_pearson
+      value: 85.23836845503847
+    - type: euclidean_spearman
+      value: 78.41475117006176
+    - type: manhattan_pearson
+      value: 85.24302039610805
+    - type: manhattan_spearman
+      value: 78.4053162562707
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts13-sts
+      name: MTEB STS13
+      config: default
+      split: test
+      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
+    metrics:
+    - type: cos_sim_pearson
+      value: 88.83570289087565
+    - type: cos_sim_spearman
+      value: 89.28563503553643
+    - type: euclidean_pearson
+      value: 87.77516003996445
+    - type: euclidean_spearman
+      value: 88.8656149534085
+    - type: manhattan_pearson
+      value: 87.75568872417946
+    - type: manhattan_spearman
+      value: 88.80445489340585
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts14-sts
+      name: MTEB STS14
+      config: default
+      split: test
+      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
+    metrics:
+    - type: cos_sim_pearson
+      value: 86.776406555485
+    - type: cos_sim_spearman
+      value: 83.8288465070091
+    - type: euclidean_pearson
+      value: 85.37827999808123
+    - type: euclidean_spearman
+      value: 84.11079529992739
+    - type: manhattan_pearson
+      value: 85.35336495689121
+    - type: manhattan_spearman
+      value: 84.08618492649347
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts15-sts
+      name: MTEB STS15
+      config: default
+      split: test
+      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
+    metrics:
+    - type: cos_sim_pearson
+      value: 88.57644404820684
+    - type: cos_sim_spearman
+      value: 89.69728364350713
+    - type: euclidean_pearson
+      value: 88.28202320389443
+    - type: euclidean_spearman
+      value: 88.9560567319321
+    - type: manhattan_pearson
+      value: 88.29461100044172
+    - type: manhattan_spearman
+      value: 88.96030920678558
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts16-sts
+      name: MTEB STS16
+      config: default
+      split: test
+      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
+    metrics:
+    - type: cos_sim_pearson
+      value: 85.05211938460621
+    - type: cos_sim_spearman
+      value: 86.43413865667489
+    - type: euclidean_pearson
+      value: 85.62760689259562
+    - type: euclidean_spearman
+      value: 86.28867831982394
+    - type: manhattan_pearson
+      value: 85.60828879163458
+    - type: manhattan_spearman
+      value: 86.27823731462473
+  - 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: 90.00254140466377
+    - type: cos_sim_spearman
+      value: 89.66118745178284
+    - type: euclidean_pearson
+      value: 89.46985446236553
+    - type: euclidean_spearman
+      value: 88.92649032371526
+    - type: manhattan_pearson
+      value: 89.49600028180247
+    - type: manhattan_spearman
+      value: 88.86948431519099
+  - 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: 68.93578321067938
+    - type: cos_sim_spearman
+      value: 69.60639595839257
+    - type: euclidean_pearson
+      value: 70.33485090574897
+    - type: euclidean_spearman
+      value: 69.03380379185452
+    - type: manhattan_pearson
+      value: 70.42097254943839
+    - type: manhattan_spearman
+      value: 69.25296348304255
+  - task:
+      type: STS
+    dataset:
+      type: mteb/stsbenchmark-sts
+      name: MTEB STSBenchmark
+      config: default
+      split: test
+      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
+    metrics:
+    - type: cos_sim_pearson
+      value: 87.29588700755069
+    - type: cos_sim_spearman
+      value: 88.30389489193672
+    - type: euclidean_pearson
+      value: 87.60349838180346
+    - type: euclidean_spearman
+      value: 87.91041868311692
+    - type: manhattan_pearson
+      value: 87.59373630607907
+    - type: manhattan_spearman
+      value: 87.88690174001724
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/scidocs-reranking
+      name: MTEB SciDocsRR
+      config: default
+      split: test
+      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
+    metrics:
+    - type: map
+      value: 87.8030655700857
+    - type: mrr
+      value: 96.3950637234951
+  - task:
+      type: Retrieval
+    dataset:
+      type: scifact
+      name: MTEB SciFact
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 60.028000000000006
+    - type: map_at_10
+      value: 69.855
+    - type: map_at_100
+      value: 70.257
+    - type: map_at_1000
+      value: 70.283
+    - type: map_at_3
+      value: 66.769
+    - type: map_at_5
+      value: 68.679
+    - type: mrr_at_1
+      value: 62.666999999999994
+    - type: mrr_at_10
+      value: 70.717
+    - type: mrr_at_100
+      value: 71.00800000000001
+    - type: mrr_at_1000
+      value: 71.033
+    - type: mrr_at_3
+      value: 68.389
+    - type: mrr_at_5
+      value: 69.939
+    - type: ndcg_at_1
+      value: 62.666999999999994
+    - type: ndcg_at_10
+      value: 74.715
+    - type: ndcg_at_100
+      value: 76.364
+    - type: ndcg_at_1000
+      value: 76.89399999999999
+    - type: ndcg_at_3
+      value: 69.383
+    - type: ndcg_at_5
+      value: 72.322
+    - type: precision_at_1
+      value: 62.666999999999994
+    - type: precision_at_10
+      value: 10.067
+    - type: precision_at_100
+      value: 1.09
+    - type: precision_at_1000
+      value: 0.11299999999999999
+    - type: precision_at_3
+      value: 27.111
+    - type: precision_at_5
+      value: 18.267
+    - type: recall_at_1
+      value: 60.028000000000006
+    - type: recall_at_10
+      value: 88.822
+    - type: recall_at_100
+      value: 96.167
+    - type: recall_at_1000
+      value: 100.0
+    - type: recall_at_3
+      value: 74.367
+    - type: recall_at_5
+      value: 81.661
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/sprintduplicatequestions-pairclassification
+      name: MTEB SprintDuplicateQuestions
+      config: default
+      split: test
+      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
+    metrics:
+    - type: cos_sim_accuracy
+      value: 99.84554455445544
+    - type: cos_sim_ap
+      value: 96.54482863244152
+    - type: cos_sim_f1
+      value: 92.13709677419355
+    - type: cos_sim_precision
+      value: 92.88617886178862
+    - type: cos_sim_recall
+      value: 91.4
+    - type: dot_accuracy
+      value: 99.76039603960396
+    - type: dot_ap
+      value: 93.20115278887057
+    - type: dot_f1
+      value: 87.92079207920793
+    - type: dot_precision
+      value: 87.05882352941177
+    - type: dot_recall
+      value: 88.8
+    - type: euclidean_accuracy
+      value: 99.84950495049505
+    - type: euclidean_ap
+      value: 96.53268343961348
+    - type: euclidean_f1
+      value: 92.23697650663942
+    - type: euclidean_precision
+      value: 94.258872651357
+    - type: euclidean_recall
+      value: 90.3
+    - type: manhattan_accuracy
+      value: 99.85346534653465
+    - type: manhattan_ap
+      value: 96.54495433438355
+    - type: manhattan_f1
+      value: 92.51012145748987
+    - type: manhattan_precision
+      value: 93.64754098360656
+    - type: manhattan_recall
+      value: 91.4
+    - type: max_accuracy
+      value: 99.85346534653465
+    - type: max_ap
+      value: 96.54495433438355
+    - type: max_f1
+      value: 92.51012145748987
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering
+      name: MTEB StackExchangeClustering
+      config: default
+      split: test
+      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
+    metrics:
+    - type: v_measure
+      value: 66.46940443952006
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering-p2p
+      name: MTEB StackExchangeClusteringP2P
+      config: default
+      split: test
+      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
+    metrics:
+    - type: v_measure
+      value: 36.396194493841584
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/stackoverflowdupquestions-reranking
+      name: MTEB StackOverflowDupQuestions
+      config: default
+      split: test
+      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
+    metrics:
+    - type: map
+      value: 54.881717673695555
+    - type: mrr
+      value: 55.73439224174519
+  - task:
+      type: Summarization
+    dataset:
+      type: mteb/summeval
+      name: MTEB SummEval
+      config: default
+      split: test
+      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
+    metrics:
+    - type: cos_sim_pearson
+      value: 31.438177268254087
+    - type: cos_sim_spearman
+      value: 30.96177698848688
+    - type: dot_pearson
+      value: 30.513850376431435
+    - type: dot_spearman
+      value: 29.932421046509706
+  - task:
+      type: Retrieval
+    dataset:
+      type: trec-covid
+      name: MTEB TRECCOVID
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 0.21
+    - type: map_at_10
+      value: 1.727
+    - type: map_at_100
+      value: 9.881
+    - type: map_at_1000
+      value: 24.245
+    - type: map_at_3
+      value: 0.615
+    - type: map_at_5
+      value: 0.966
+    - type: mrr_at_1
+      value: 78.0
+    - type: mrr_at_10
+      value: 87.333
+    - type: mrr_at_100
+      value: 87.333
+    - type: mrr_at_1000
+      value: 87.333
+    - type: mrr_at_3
+      value: 86.333
+    - type: mrr_at_5
+      value: 87.333
+    - type: ndcg_at_1
+      value: 74.0
+    - type: ndcg_at_10
+      value: 69.12700000000001
+    - type: ndcg_at_100
+      value: 53.893
+    - type: ndcg_at_1000
+      value: 49.639
+    - type: ndcg_at_3
+      value: 74.654
+    - type: ndcg_at_5
+      value: 73.232
+    - type: precision_at_1
+      value: 78.0
+    - type: precision_at_10
+      value: 72.8
+    - type: precision_at_100
+      value: 55.42
+    - type: precision_at_1000
+      value: 21.73
+    - type: precision_at_3
+      value: 79.333
+    - type: precision_at_5
+      value: 77.2
+    - type: recall_at_1
+      value: 0.21
+    - type: recall_at_10
+      value: 1.9709999999999999
+    - type: recall_at_100
+      value: 13.555
+    - type: recall_at_1000
+      value: 46.961999999999996
+    - type: recall_at_3
+      value: 0.66
+    - type: recall_at_5
+      value: 1.052
+  - task:
+      type: Retrieval
+    dataset:
+      type: webis-touche2020
+      name: MTEB Touche2020
+      config: default
+      split: test
+      revision: None
+    metrics:
+    - type: map_at_1
+      value: 2.456
+    - type: map_at_10
+      value: 9.426
+    - type: map_at_100
+      value: 16.066
+    - type: map_at_1000
+      value: 17.652
+    - type: map_at_3
+      value: 5.2459999999999996
+    - type: map_at_5
+      value: 6.5360000000000005
+    - type: mrr_at_1
+      value: 34.694
+    - type: mrr_at_10
+      value: 47.666
+    - type: mrr_at_100
+      value: 48.681999999999995
+    - type: mrr_at_1000
+      value: 48.681999999999995
+    - type: mrr_at_3
+      value: 43.878
+    - type: mrr_at_5
+      value: 46.224
+    - type: ndcg_at_1
+      value: 31.633
+    - type: ndcg_at_10
+      value: 23.454
+    - type: ndcg_at_100
+      value: 36.616
+    - type: ndcg_at_1000
+      value: 48.596000000000004
+    - type: ndcg_at_3
+      value: 28.267999999999997
+    - type: ndcg_at_5
+      value: 25.630999999999997
+    - type: precision_at_1
+      value: 34.694
+    - type: precision_at_10
+      value: 20.204
+    - type: precision_at_100
+      value: 7.754999999999999
+    - type: precision_at_1000
+      value: 1.5709999999999997
+    - type: precision_at_3
+      value: 29.252
+    - type: precision_at_5
+      value: 24.898
+    - type: recall_at_1
+      value: 2.456
+    - type: recall_at_10
+      value: 14.951
+    - type: recall_at_100
+      value: 48.399
+    - type: recall_at_1000
+      value: 85.077
+    - type: recall_at_3
+      value: 6.1370000000000005
+    - type: recall_at_5
+      value: 8.671
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/toxic_conversations_50k
+      name: MTEB ToxicConversationsClassification
+      config: default
+      split: test
+      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
+    metrics:
+    - type: accuracy
+      value: 71.86240000000001
+    - type: ap
+      value: 14.678570078747494
+    - type: f1
+      value: 55.295967793934445
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/tweet_sentiment_extraction
+      name: MTEB TweetSentimentExtractionClassification
+      config: default
+      split: test
+      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
+    metrics:
+    - type: accuracy
+      value: 59.17374080362195
+    - type: f1
+      value: 59.54410874861454
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/twentynewsgroups-clustering
+      name: MTEB TwentyNewsgroupsClustering
+      config: default
+      split: test
+      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
+    metrics:
+    - type: v_measure
+      value: 51.91227822485289
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twittersemeval2015-pairclassification
+      name: MTEB TwitterSemEval2015
+      config: default
+      split: test
+      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
+    metrics:
+    - type: cos_sim_accuracy
+      value: 87.12523097097217
+    - type: cos_sim_ap
+      value: 77.59606075943269
+    - type: cos_sim_f1
+      value: 71.11395646606915
+    - type: cos_sim_precision
+      value: 69.07960199004975
+    - type: cos_sim_recall
+      value: 73.27176781002639
+    - type: dot_accuracy
+      value: 84.68736961316088
+    - type: dot_ap
+      value: 68.47167450741459
+    - type: dot_f1
+      value: 64.42152354914874
+    - type: dot_precision
+      value: 60.887949260042284
+    - type: dot_recall
+      value: 68.3905013192612
+    - type: euclidean_accuracy
+      value: 86.88084878106932
+    - type: euclidean_ap
+      value: 77.27351204978599
+    - type: euclidean_f1
+      value: 70.99179716629381
+    - type: euclidean_precision
+      value: 67.10526315789474
+    - type: euclidean_recall
+      value: 75.35620052770449
+    - type: manhattan_accuracy
+      value: 86.83316445133218
+    - type: manhattan_ap
+      value: 77.21835357308716
+    - type: manhattan_f1
+      value: 71.05587004676349
+    - type: manhattan_precision
+      value: 66.58210332103322
+    - type: manhattan_recall
+      value: 76.17414248021109
+    - type: max_accuracy
+      value: 87.12523097097217
+    - type: max_ap
+      value: 77.59606075943269
+    - type: max_f1
+      value: 71.11395646606915
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twitterurlcorpus-pairclassification
+      name: MTEB TwitterURLCorpus
+      config: default
+      split: test
+      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
+    metrics:
+    - type: cos_sim_accuracy
+      value: 88.97232894787906
+    - type: cos_sim_ap
+      value: 85.9613736469497
+    - type: cos_sim_f1
+      value: 78.40216655382532
+    - type: cos_sim_precision
+      value: 72.97512437810946
+    - type: cos_sim_recall
+      value: 84.70126270403449
+    - type: dot_accuracy
+      value: 88.04866689952264
+    - type: dot_ap
+      value: 83.15465089499936
+    - type: dot_f1
+      value: 76.32698287879329
+    - type: dot_precision
+      value: 71.23223697378077
+    - type: dot_recall
+      value: 82.20665229442562
+    - type: euclidean_accuracy
+      value: 88.67543757519307
+    - type: euclidean_ap
+      value: 85.4524355531532
+    - type: euclidean_f1
+      value: 77.78729106950081
+    - type: euclidean_precision
+      value: 75.3009009009009
+    - type: euclidean_recall
+      value: 80.44348629504158
+    - type: manhattan_accuracy
+      value: 88.65991384328792
+    - type: manhattan_ap
+      value: 85.43109069046837
+    - type: manhattan_f1
+      value: 77.72639551396425
+    - type: manhattan_precision
+      value: 73.73402417962004
+    - type: manhattan_recall
+      value: 82.17585463504774
+    - type: max_accuracy
+      value: 88.97232894787906
+    - type: max_ap
+      value: 85.9613736469497
+    - type: max_f1
+      value: 78.40216655382532
+---
+<h1 align="center">GIST Large Embedding v0</h1>
+
+*GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning*
+
+The model is fine-tuned on top of the [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) using the [MEDI dataset](https://github.com/xlang-ai/instructor-embedding.git) augmented with mined triplets from the [MTEB Classification](https://huggingface.co/mteb) training dataset (excluding data from the Amazon Polarity Classification task).
+
+The model does not require any instruction for generating embeddings. This means that queries for retrieval tasks can be directly encoded without crafting instructions.
+
+Technical paper: [GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning](https://arxiv.org/abs/2402.16829)
+
+
+# Data
+
+The dataset used is a compilation of the MEDI and MTEB Classification training datasets. Third-party datasets may be subject to additional terms and conditions under their associated licenses. A HuggingFace Dataset version of the compiled dataset, and the specific revision used to train the model, is available:
+
+- Dataset: [avsolatorio/medi-data-mteb_avs_triplets](https://huggingface.co/datasets/avsolatorio/medi-data-mteb_avs_triplets)
+- Revision: 238a0499b6e6b690cc64ea56fde8461daa8341bb
+
+The dataset contains a `task_type` key, which can be used to select only the mteb classification tasks (prefixed with `mteb_`).
+
+The **MEDI Dataset** is published in the following paper: [One Embedder, Any Task: Instruction-Finetuned Text Embeddings](https://arxiv.org/abs/2212.09741).
+
+The MTEB Benchmark results of the GIST embedding model, compared with the base model, suggest that the fine-tuning dataset has perturbed the model considerably, which resulted in significant improvements in certain tasks while adversely degrading performance in some.
+
+The retrieval performance for the TRECCOVID task is of note. The fine-tuning dataset does not contain significant knowledge about COVID-19, which could have caused the observed performance degradation. We found some evidence, detailed in the paper, that thematic coverage of the fine-tuning data can affect downstream performance.
+
+# Usage
+
+The model can be easily loaded using the Sentence Transformers library.
+
+```Python
+import torch.nn.functional as F
+from sentence_transformers import SentenceTransformer
+
+revision = None  # Replace with the specific revision to ensure reproducibility if the model is updated.
+
+model = SentenceTransformer("avsolatorio/GIST-large-Embedding-v0", revision=revision)
+
+texts = [
+    "Illustration of the REaLTabFormer model. The left block shows the non-relational tabular data model using GPT-2 with a causal LM head. In contrast, the right block shows how a relational dataset's child table is modeled using a sequence-to-sequence (Seq2Seq) model. The Seq2Seq model uses the observations in the parent table to condition the generation of the observations in the child table. The trained GPT-2 model on the parent table, with weights frozen, is also used as the encoder in the Seq2Seq model.",
+    "Predicting human mobility holds significant practical value, with applications ranging from enhancing disaster risk planning to simulating epidemic spread. In this paper, we present the GeoFormer, a decoder-only transformer model adapted from the GPT architecture to forecast human mobility.",
+    "As the economies of Southeast Asia continue adopting digital technologies, policy makers increasingly ask how to prepare the workforce for emerging labor demands. However, little is known about the skills that workers need to adapt to these changes"
+]
+
+# Compute embeddings
+embeddings = model.encode(texts, convert_to_tensor=True)
+
+# Compute cosine-similarity for each pair of sentences
+scores = F.cosine_similarity(embeddings.unsqueeze(1), embeddings.unsqueeze(0), dim=-1)
+
+print(scores.cpu().numpy())
+```
+
+# Training Parameters
+
+Below are the training parameters used to fine-tune the model:
+
+```
+Epochs = 40
+Warmup ratio = 0.1
+Learning rate = 5e-6
+Batch size = 16
+Checkpoint step = 171000
+Contrastive loss temperature = 0.01
+```
+
+
+# Evaluation
+
+The model was evaluated using the [MTEB Evaluation](https://huggingface.co/mteb) suite.
+
+
+# Citation
+
+Please cite our work if you use GISTEmbed or the datasets we published in your projects or research. 🤗
+
+```
+@article{solatorio2024gistembed,
+    title={GISTEmbed: Guided In-sample Selection of Training Negatives for Text Embedding Fine-tuning},
+    author={Aivin V. Solatorio},
+    journal={arXiv preprint arXiv:2402.16829},
+    year={2024},
+    URL={https://arxiv.org/abs/2402.16829}
+    eprint={2402.16829},
+    archivePrefix={arXiv},
+    primaryClass={cs.LG}
+}
+```
+
+# Acknowledgements
+
+This work is supported by the "KCP IV - Exploring Data Use in the Development Economics Literature using Large Language Models (AI and LLMs)" project funded by the [Knowledge for Change Program (KCP)](https://www.worldbank.org/en/programs/knowledge-for-change) of the World Bank - RA-P503405-RESE-TF0C3444.
+
+The findings, interpretations, and conclusions expressed in this material are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

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+}

config_sentence_transformers.json

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+    "transformers": "4.45.2",
+    "pytorch": "2.6.0"
+  },
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+  "similarity_fn_name": null
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sentence_bert_config.json

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+}

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tokenizer_config.json

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+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}