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README.md

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+---
+base_model:
+- Linq-AI-Research/Linq-Embed-Mistral
+tags:
+- bnb-my-repo
+- mteb
+- transformers
+- sentence-transformers
+model-index:
+- name: Linq-Embed-Mistral
+  results:
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_counterfactual
+      name: MTEB AmazonCounterfactualClassification (en)
+      config: en
+      split: test
+      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
+    metrics:
+    - type: accuracy
+      value: 84.43283582089552
+    - type: ap
+      value: 50.39222584035829
+    - type: f1
+      value: 78.47906270064071
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_polarity
+      name: MTEB AmazonPolarityClassification
+      config: default
+      split: test
+      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
+    metrics:
+    - type: accuracy
+      value: 95.70445
+    - type: ap
+      value: 94.28273900595173
+    - type: f1
+      value: 95.70048412173735
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_reviews_multi
+      name: MTEB AmazonReviewsClassification (en)
+      config: en
+      split: test
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+    metrics:
+    - type: accuracy
+      value: 57.644000000000005
+    - type: f1
+      value: 56.993648296704876
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/arguana
+      name: MTEB ArguAna
+      config: default
+      split: test
+      revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
+    metrics:
+    - type: map_at_1
+      value: 45.804
+    - type: map_at_10
+      value: 61.742
+    - type: map_at_100
+      value: 62.07899999999999
+    - type: map_at_1000
+      value: 62.08
+    - type: map_at_3
+      value: 57.717
+    - type: map_at_5
+      value: 60.27
+    - type: mrr_at_1
+      value: 47.226
+    - type: mrr_at_10
+      value: 62.256
+    - type: mrr_at_100
+      value: 62.601
+    - type: mrr_at_1000
+      value: 62.601
+    - type: mrr_at_3
+      value: 58.203
+    - type: mrr_at_5
+      value: 60.767
+    - type: ndcg_at_1
+      value: 45.804
+    - type: ndcg_at_10
+      value: 69.649
+    - type: ndcg_at_100
+      value: 70.902
+    - type: ndcg_at_1000
+      value: 70.91199999999999
+    - type: ndcg_at_3
+      value: 61.497
+    - type: ndcg_at_5
+      value: 66.097
+    - type: precision_at_1
+      value: 45.804
+    - type: precision_at_10
+      value: 9.452
+    - type: precision_at_100
+      value: 0.996
+    - type: precision_at_1000
+      value: 0.1
+    - type: precision_at_3
+      value: 24.135
+    - type: precision_at_5
+      value: 16.714000000000002
+    - type: recall_at_1
+      value: 45.804
+    - type: recall_at_10
+      value: 94.523
+    - type: recall_at_100
+      value: 99.57300000000001
+    - type: recall_at_1000
+      value: 99.644
+    - type: recall_at_3
+      value: 72.404
+    - type: recall_at_5
+      value: 83.57
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-p2p
+      name: MTEB ArxivClusteringP2P
+      config: default
+      split: test
+      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
+    metrics:
+    - type: v_measure
+      value: 51.47612678878609
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/arxiv-clustering-s2s
+      name: MTEB ArxivClusteringS2S
+      config: default
+      split: test
+      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
+    metrics:
+    - type: v_measure
+      value: 47.2977392340418
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/askubuntudupquestions-reranking
+      name: MTEB AskUbuntuDupQuestions
+      config: default
+      split: test
+      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
+    metrics:
+    - type: map
+      value: 66.82016765243456
+    - type: mrr
+      value: 79.55227982236292
+  - task:
+      type: STS
+    dataset:
+      type: mteb/biosses-sts
+      name: MTEB BIOSSES
+      config: default
+      split: test
+      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
+    metrics:
+    - type: cos_sim_pearson
+      value: 89.15068664186332
+    - type: cos_sim_spearman
+      value: 86.4013663041054
+    - type: euclidean_pearson
+      value: 87.36391302921588
+    - type: euclidean_spearman
+      value: 86.4013663041054
+    - type: manhattan_pearson
+      value: 87.46116676558589
+    - type: manhattan_spearman
+      value: 86.78149544753352
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/banking77
+      name: MTEB Banking77Classification
+      config: default
+      split: test
+      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
+    metrics:
+    - type: accuracy
+      value: 87.88311688311688
+    - type: f1
+      value: 87.82368154811464
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-p2p
+      name: MTEB BiorxivClusteringP2P
+      config: default
+      split: test
+      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
+    metrics:
+    - type: v_measure
+      value: 42.72860396750569
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/biorxiv-clustering-s2s
+      name: MTEB BiorxivClusteringS2S
+      config: default
+      split: test
+      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
+    metrics:
+    - type: v_measure
+      value: 39.58412067938718
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/cqadupstack
+      name: MTEB CQADupstackRetrieval
+      config: default
+      split: test
+      revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
+    metrics:
+    - type: map_at_1
+      value: 30.082666666666665
+    - type: map_at_10
+      value: 41.13875
+    - type: map_at_100
+      value: 42.45525
+    - type: map_at_1000
+      value: 42.561249999999994
+    - type: map_at_3
+      value: 37.822750000000006
+    - type: map_at_5
+      value: 39.62658333333333
+    - type: mrr_at_1
+      value: 35.584
+    - type: mrr_at_10
+      value: 45.4675
+    - type: mrr_at_100
+      value: 46.31016666666667
+    - type: mrr_at_1000
+      value: 46.35191666666666
+    - type: mrr_at_3
+      value: 42.86674999999999
+    - type: mrr_at_5
+      value: 44.31341666666666
+    - type: ndcg_at_1
+      value: 35.584
+    - type: ndcg_at_10
+      value: 47.26516666666667
+    - type: ndcg_at_100
+      value: 52.49108333333332
+    - type: ndcg_at_1000
+      value: 54.24575
+    - type: ndcg_at_3
+      value: 41.83433333333334
+    - type: ndcg_at_5
+      value: 44.29899999999999
+    - type: precision_at_1
+      value: 35.584
+    - type: precision_at_10
+      value: 8.390333333333334
+    - type: precision_at_100
+      value: 1.2941666666666667
+    - type: precision_at_1000
+      value: 0.16308333333333336
+    - type: precision_at_3
+      value: 19.414583333333333
+    - type: precision_at_5
+      value: 13.751
+    - type: recall_at_1
+      value: 30.082666666666665
+    - type: recall_at_10
+      value: 60.88875
+    - type: recall_at_100
+      value: 83.35141666666667
+    - type: recall_at_1000
+      value: 95.0805
+    - type: recall_at_3
+      value: 45.683749999999996
+    - type: recall_at_5
+      value: 52.08208333333333
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/climate-fever
+      name: MTEB ClimateFEVER
+      config: default
+      split: test
+      revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
+    metrics:
+    - type: map_at_1
+      value: 16.747
+    - type: map_at_10
+      value: 29.168
+    - type: map_at_100
+      value: 31.304
+    - type: map_at_1000
+      value: 31.496000000000002
+    - type: map_at_3
+      value: 24.57
+    - type: map_at_5
+      value: 26.886
+    - type: mrr_at_1
+      value: 37.524
+    - type: mrr_at_10
+      value: 50.588
+    - type: mrr_at_100
+      value: 51.28
+    - type: mrr_at_1000
+      value: 51.29899999999999
+    - type: mrr_at_3
+      value: 47.438
+    - type: mrr_at_5
+      value: 49.434
+    - type: ndcg_at_1
+      value: 37.524
+    - type: ndcg_at_10
+      value: 39.11
+    - type: ndcg_at_100
+      value: 46.373999999999995
+    - type: ndcg_at_1000
+      value: 49.370999999999995
+    - type: ndcg_at_3
+      value: 32.964
+    - type: ndcg_at_5
+      value: 35.028
+    - type: precision_at_1
+      value: 37.524
+    - type: precision_at_10
+      value: 12.137
+    - type: precision_at_100
+      value: 1.9929999999999999
+    - type: precision_at_1000
+      value: 0.256
+    - type: precision_at_3
+      value: 24.886
+    - type: precision_at_5
+      value: 18.762
+    - type: recall_at_1
+      value: 16.747
+    - type: recall_at_10
+      value: 45.486
+    - type: recall_at_100
+      value: 69.705
+    - type: recall_at_1000
+      value: 86.119
+    - type: recall_at_3
+      value: 30.070999999999998
+    - type: recall_at_5
+      value: 36.565
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/dbpedia
+      name: MTEB DBPedia
+      config: default
+      split: test
+      revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
+    metrics:
+    - type: map_at_1
+      value: 10.495000000000001
+    - type: map_at_10
+      value: 24.005000000000003
+    - type: map_at_100
+      value: 34.37
+    - type: map_at_1000
+      value: 36.268
+    - type: map_at_3
+      value: 16.694
+    - type: map_at_5
+      value: 19.845
+    - type: mrr_at_1
+      value: 75.5
+    - type: mrr_at_10
+      value: 82.458
+    - type: mrr_at_100
+      value: 82.638
+    - type: mrr_at_1000
+      value: 82.64
+    - type: mrr_at_3
+      value: 81.25
+    - type: mrr_at_5
+      value: 82.125
+    - type: ndcg_at_1
+      value: 64.625
+    - type: ndcg_at_10
+      value: 51.322
+    - type: ndcg_at_100
+      value: 55.413999999999994
+    - type: ndcg_at_1000
+      value: 62.169
+    - type: ndcg_at_3
+      value: 56.818999999999996
+    - type: ndcg_at_5
+      value: 54.32900000000001
+    - type: precision_at_1
+      value: 75.5
+    - type: precision_at_10
+      value: 40.849999999999994
+    - type: precision_at_100
+      value: 12.882
+    - type: precision_at_1000
+      value: 2.394
+    - type: precision_at_3
+      value: 59.667
+    - type: precision_at_5
+      value: 52.2
+    - type: recall_at_1
+      value: 10.495000000000001
+    - type: recall_at_10
+      value: 29.226000000000003
+    - type: recall_at_100
+      value: 59.614
+    - type: recall_at_1000
+      value: 81.862
+    - type: recall_at_3
+      value: 17.97
+    - type: recall_at_5
+      value: 22.438
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/emotion
+      name: MTEB EmotionClassification
+      config: default
+      split: test
+      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
+    metrics:
+    - type: accuracy
+      value: 51.82
+    - type: f1
+      value: 47.794956731921054
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/fever
+      name: MTEB FEVER
+      config: default
+      split: test
+      revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
+    metrics:
+    - type: map_at_1
+      value: 82.52199999999999
+    - type: map_at_10
+      value: 89.794
+    - type: map_at_100
+      value: 89.962
+    - type: map_at_1000
+      value: 89.972
+    - type: map_at_3
+      value: 88.95100000000001
+    - type: map_at_5
+      value: 89.524
+    - type: mrr_at_1
+      value: 88.809
+    - type: mrr_at_10
+      value: 93.554
+    - type: mrr_at_100
+      value: 93.577
+    - type: mrr_at_1000
+      value: 93.577
+    - type: mrr_at_3
+      value: 93.324
+    - type: mrr_at_5
+      value: 93.516
+    - type: ndcg_at_1
+      value: 88.809
+    - type: ndcg_at_10
+      value: 92.419
+    - type: ndcg_at_100
+      value: 92.95
+    - type: ndcg_at_1000
+      value: 93.10000000000001
+    - type: ndcg_at_3
+      value: 91.45299999999999
+    - type: ndcg_at_5
+      value: 92.05
+    - type: precision_at_1
+      value: 88.809
+    - type: precision_at_10
+      value: 10.911999999999999
+    - type: precision_at_100
+      value: 1.143
+    - type: precision_at_1000
+      value: 0.117
+    - type: precision_at_3
+      value: 34.623
+    - type: precision_at_5
+      value: 21.343999999999998
+    - type: recall_at_1
+      value: 82.52199999999999
+    - type: recall_at_10
+      value: 96.59400000000001
+    - type: recall_at_100
+      value: 98.55699999999999
+    - type: recall_at_1000
+      value: 99.413
+    - type: recall_at_3
+      value: 94.02199999999999
+    - type: recall_at_5
+      value: 95.582
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/fiqa
+      name: MTEB FiQA2018
+      config: default
+      split: test
+      revision: 27a168819829fe9bcd655c2df245fb19452e8e06
+    metrics:
+    - type: map_at_1
+      value: 32.842
+    - type: map_at_10
+      value: 53.147
+    - type: map_at_100
+      value: 55.265
+    - type: map_at_1000
+      value: 55.37
+    - type: map_at_3
+      value: 46.495
+    - type: map_at_5
+      value: 50.214999999999996
+    - type: mrr_at_1
+      value: 61.574
+    - type: mrr_at_10
+      value: 68.426
+    - type: mrr_at_100
+      value: 68.935
+    - type: mrr_at_1000
+      value: 68.95400000000001
+    - type: mrr_at_3
+      value: 66.307
+    - type: mrr_at_5
+      value: 67.611
+    - type: ndcg_at_1
+      value: 61.574
+    - type: ndcg_at_10
+      value: 61.205
+    - type: ndcg_at_100
+      value: 67.25999999999999
+    - type: ndcg_at_1000
+      value: 68.657
+    - type: ndcg_at_3
+      value: 56.717
+    - type: ndcg_at_5
+      value: 58.196999999999996
+    - type: precision_at_1
+      value: 61.574
+    - type: precision_at_10
+      value: 16.852
+    - type: precision_at_100
+      value: 2.33
+    - type: precision_at_1000
+      value: 0.256
+    - type: precision_at_3
+      value: 37.5
+    - type: precision_at_5
+      value: 27.468999999999998
+    - type: recall_at_1
+      value: 32.842
+    - type: recall_at_10
+      value: 68.157
+    - type: recall_at_100
+      value: 89.5
+    - type: recall_at_1000
+      value: 97.68599999999999
+    - type: recall_at_3
+      value: 50.783
+    - type: recall_at_5
+      value: 58.672000000000004
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/hotpotqa
+      name: MTEB HotpotQA
+      config: default
+      split: test
+      revision: ab518f4d6fcca38d87c25209f94beba119d02014
+    metrics:
+    - type: map_at_1
+      value: 39.068000000000005
+    - type: map_at_10
+      value: 69.253
+    - type: map_at_100
+      value: 70.036
+    - type: map_at_1000
+      value: 70.081
+    - type: map_at_3
+      value: 65.621
+    - type: map_at_5
+      value: 67.976
+    - type: mrr_at_1
+      value: 78.13600000000001
+    - type: mrr_at_10
+      value: 84.328
+    - type: mrr_at_100
+      value: 84.515
+    - type: mrr_at_1000
+      value: 84.52300000000001
+    - type: mrr_at_3
+      value: 83.52199999999999
+    - type: mrr_at_5
+      value: 84.019
+    - type: ndcg_at_1
+      value: 78.13600000000001
+    - type: ndcg_at_10
+      value: 76.236
+    - type: ndcg_at_100
+      value: 78.891
+    - type: ndcg_at_1000
+      value: 79.73400000000001
+    - type: ndcg_at_3
+      value: 71.258
+    - type: ndcg_at_5
+      value: 74.129
+    - type: precision_at_1
+      value: 78.13600000000001
+    - type: precision_at_10
+      value: 16.347
+    - type: precision_at_100
+      value: 1.839
+    - type: precision_at_1000
+      value: 0.19499999999999998
+    - type: precision_at_3
+      value: 47.189
+    - type: precision_at_5
+      value: 30.581999999999997
+    - type: recall_at_1
+      value: 39.068000000000005
+    - type: recall_at_10
+      value: 81.735
+    - type: recall_at_100
+      value: 91.945
+    - type: recall_at_1000
+      value: 97.44800000000001
+    - type: recall_at_3
+      value: 70.783
+    - type: recall_at_5
+      value: 76.455
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/imdb
+      name: MTEB ImdbClassification
+      config: default
+      split: test
+      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
+    metrics:
+    - type: accuracy
+      value: 94.7764
+    - type: ap
+      value: 92.67841294818406
+    - type: f1
+      value: 94.77375157383646
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/msmarco
+      name: MTEB MSMARCO
+      config: default
+      split: dev
+      revision: c5a29a104738b98a9e76336939199e264163d4a0
+    metrics:
+    - type: map_at_1
+      value: 24.624
+    - type: map_at_10
+      value: 37.861
+    - type: map_at_100
+      value: 39.011
+    - type: map_at_1000
+      value: 39.052
+    - type: map_at_3
+      value: 33.76
+    - type: map_at_5
+      value: 36.153
+    - type: mrr_at_1
+      value: 25.358000000000004
+    - type: mrr_at_10
+      value: 38.5
+    - type: mrr_at_100
+      value: 39.572
+    - type: mrr_at_1000
+      value: 39.607
+    - type: mrr_at_3
+      value: 34.491
+    - type: mrr_at_5
+      value: 36.83
+    - type: ndcg_at_1
+      value: 25.358000000000004
+    - type: ndcg_at_10
+      value: 45.214999999999996
+    - type: ndcg_at_100
+      value: 50.56
+    - type: ndcg_at_1000
+      value: 51.507999999999996
+    - type: ndcg_at_3
+      value: 36.925999999999995
+    - type: ndcg_at_5
+      value: 41.182
+    - type: precision_at_1
+      value: 25.358000000000004
+    - type: precision_at_10
+      value: 7.090000000000001
+    - type: precision_at_100
+      value: 0.9740000000000001
+    - type: precision_at_1000
+      value: 0.106
+    - type: precision_at_3
+      value: 15.697
+    - type: precision_at_5
+      value: 11.599
+    - type: recall_at_1
+      value: 24.624
+    - type: recall_at_10
+      value: 67.78699999999999
+    - type: recall_at_100
+      value: 92.11200000000001
+    - type: recall_at_1000
+      value: 99.208
+    - type: recall_at_3
+      value: 45.362
+    - type: recall_at_5
+      value: 55.58
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_domain
+      name: MTEB MTOPDomainClassification (en)
+      config: en
+      split: test
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+    metrics:
+    - type: accuracy
+      value: 96.83310533515733
+    - type: f1
+      value: 96.57069781347995
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/mtop_intent
+      name: MTEB MTOPIntentClassification (en)
+      config: en
+      split: test
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+    metrics:
+    - type: accuracy
+      value: 89.5690834473324
+    - type: f1
+      value: 73.7275204564728
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_intent
+      name: MTEB MassiveIntentClassification (en)
+      config: en
+      split: test
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+    metrics:
+    - type: accuracy
+      value: 82.67316745124411
+    - type: f1
+      value: 79.70626515721662
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/amazon_massive_scenario
+      name: MTEB MassiveScenarioClassification (en)
+      config: en
+      split: test
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+    metrics:
+    - type: accuracy
+      value: 85.01344989912575
+    - type: f1
+      value: 84.45181022816965
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-p2p
+      name: MTEB MedrxivClusteringP2P
+      config: default
+      split: test
+      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
+    metrics:
+    - type: v_measure
+      value: 37.843426126777295
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/medrxiv-clustering-s2s
+      name: MTEB MedrxivClusteringS2S
+      config: default
+      split: test
+      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
+    metrics:
+    - type: v_measure
+      value: 36.651728547241476
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/mind_small
+      name: MTEB MindSmallReranking
+      config: default
+      split: test
+      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
+    metrics:
+    - type: map
+      value: 32.05750522793288
+    - type: mrr
+      value: 33.28067556869468
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/nfcorpus
+      name: MTEB NFCorpus
+      config: default
+      split: test
+      revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
+    metrics:
+    - type: map_at_1
+      value: 6.744
+    - type: map_at_10
+      value: 16.235
+    - type: map_at_100
+      value: 20.767
+    - type: map_at_1000
+      value: 22.469
+    - type: map_at_3
+      value: 11.708
+    - type: map_at_5
+      value: 13.924
+    - type: mrr_at_1
+      value: 55.728
+    - type: mrr_at_10
+      value: 63.869
+    - type: mrr_at_100
+      value: 64.322
+    - type: mrr_at_1000
+      value: 64.342
+    - type: mrr_at_3
+      value: 62.022999999999996
+    - type: mrr_at_5
+      value: 63.105999999999995
+    - type: ndcg_at_1
+      value: 53.096
+    - type: ndcg_at_10
+      value: 41.618
+    - type: ndcg_at_100
+      value: 38.562999999999995
+    - type: ndcg_at_1000
+      value: 47.006
+    - type: ndcg_at_3
+      value: 47.657
+    - type: ndcg_at_5
+      value: 45.562999999999995
+    - type: precision_at_1
+      value: 55.108000000000004
+    - type: precision_at_10
+      value: 30.464000000000002
+    - type: precision_at_100
+      value: 9.737
+    - type: precision_at_1000
+      value: 2.2720000000000002
+    - type: precision_at_3
+      value: 44.376
+    - type: precision_at_5
+      value: 39.505
+    - type: recall_at_1
+      value: 6.744
+    - type: recall_at_10
+      value: 21.11
+    - type: recall_at_100
+      value: 39.69
+    - type: recall_at_1000
+      value: 70.44
+    - type: recall_at_3
+      value: 13.120000000000001
+    - type: recall_at_5
+      value: 16.669
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/nq
+      name: MTEB NQ
+      config: default
+      split: test
+      revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
+    metrics:
+    - type: map_at_1
+      value: 46.263
+    - type: map_at_10
+      value: 63.525
+    - type: map_at_100
+      value: 64.142
+    - type: map_at_1000
+      value: 64.14800000000001
+    - type: map_at_3
+      value: 59.653
+    - type: map_at_5
+      value: 62.244
+    - type: mrr_at_1
+      value: 51.796
+    - type: mrr_at_10
+      value: 65.764
+    - type: mrr_at_100
+      value: 66.155
+    - type: mrr_at_1000
+      value: 66.158
+    - type: mrr_at_3
+      value: 63.05500000000001
+    - type: mrr_at_5
+      value: 64.924
+    - type: ndcg_at_1
+      value: 51.766999999999996
+    - type: ndcg_at_10
+      value: 70.626
+    - type: ndcg_at_100
+      value: 72.905
+    - type: ndcg_at_1000
+      value: 73.021
+    - type: ndcg_at_3
+      value: 63.937999999999995
+    - type: ndcg_at_5
+      value: 68.00699999999999
+    - type: precision_at_1
+      value: 51.766999999999996
+    - type: precision_at_10
+      value: 10.768
+    - type: precision_at_100
+      value: 1.203
+    - type: precision_at_1000
+      value: 0.121
+    - type: precision_at_3
+      value: 28.409000000000002
+    - type: precision_at_5
+      value: 19.502
+    - type: recall_at_1
+      value: 46.263
+    - type: recall_at_10
+      value: 89.554
+    - type: recall_at_100
+      value: 98.914
+    - type: recall_at_1000
+      value: 99.754
+    - type: recall_at_3
+      value: 72.89999999999999
+    - type: recall_at_5
+      value: 82.1
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/quora
+      name: MTEB QuoraRetrieval
+      config: default
+      split: test
+      revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
+    metrics:
+    - type: map_at_1
+      value: 72.748
+    - type: map_at_10
+      value: 86.87700000000001
+    - type: map_at_100
+      value: 87.46199999999999
+    - type: map_at_1000
+      value: 87.47399999999999
+    - type: map_at_3
+      value: 83.95700000000001
+    - type: map_at_5
+      value: 85.82300000000001
+    - type: mrr_at_1
+      value: 83.62
+    - type: mrr_at_10
+      value: 89.415
+    - type: mrr_at_100
+      value: 89.484
+    - type: mrr_at_1000
+      value: 89.484
+    - type: mrr_at_3
+      value: 88.633
+    - type: mrr_at_5
+      value: 89.176
+    - type: ndcg_at_1
+      value: 83.62
+    - type: ndcg_at_10
+      value: 90.27
+    - type: ndcg_at_100
+      value: 91.23599999999999
+    - type: ndcg_at_1000
+      value: 91.293
+    - type: ndcg_at_3
+      value: 87.69500000000001
+    - type: ndcg_at_5
+      value: 89.171
+    - type: precision_at_1
+      value: 83.62
+    - type: precision_at_10
+      value: 13.683
+    - type: precision_at_100
+      value: 1.542
+    - type: precision_at_1000
+      value: 0.157
+    - type: precision_at_3
+      value: 38.363
+    - type: precision_at_5
+      value: 25.196
+    - type: recall_at_1
+      value: 72.748
+    - type: recall_at_10
+      value: 96.61699999999999
+    - type: recall_at_100
+      value: 99.789
+    - type: recall_at_1000
+      value: 99.997
+    - type: recall_at_3
+      value: 89.21
+    - type: recall_at_5
+      value: 93.418
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering
+      name: MTEB RedditClustering
+      config: default
+      split: test
+      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
+    metrics:
+    - type: v_measure
+      value: 61.51909029379199
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/reddit-clustering-p2p
+      name: MTEB RedditClusteringP2P
+      config: default
+      split: test
+      revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
+    metrics:
+    - type: v_measure
+      value: 68.24483162045645
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/scidocs
+      name: MTEB SCIDOCS
+      config: default
+      split: test
+      revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
+    metrics:
+    - type: map_at_1
+      value: 4.793
+    - type: map_at_10
+      value: 13.092
+    - type: map_at_100
+      value: 15.434000000000001
+    - type: map_at_1000
+      value: 15.748999999999999
+    - type: map_at_3
+      value: 9.139
+    - type: map_at_5
+      value: 11.033
+    - type: mrr_at_1
+      value: 23.599999999999998
+    - type: mrr_at_10
+      value: 35.892
+    - type: mrr_at_100
+      value: 36.962
+    - type: mrr_at_1000
+      value: 37.009
+    - type: mrr_at_3
+      value: 32.550000000000004
+    - type: mrr_at_5
+      value: 34.415
+    - type: ndcg_at_1
+      value: 23.599999999999998
+    - type: ndcg_at_10
+      value: 21.932
+    - type: ndcg_at_100
+      value: 30.433
+    - type: ndcg_at_1000
+      value: 35.668
+    - type: ndcg_at_3
+      value: 20.483999999999998
+    - type: ndcg_at_5
+      value: 17.964
+    - type: precision_at_1
+      value: 23.599999999999998
+    - type: precision_at_10
+      value: 11.63
+    - type: precision_at_100
+      value: 2.383
+    - type: precision_at_1000
+      value: 0.363
+    - type: precision_at_3
+      value: 19.567
+    - type: precision_at_5
+      value: 16.06
+    - type: recall_at_1
+      value: 4.793
+    - type: recall_at_10
+      value: 23.558
+    - type: recall_at_100
+      value: 48.376999999999995
+    - type: recall_at_1000
+      value: 73.75699999999999
+    - type: recall_at_3
+      value: 11.903
+    - type: recall_at_5
+      value: 16.278000000000002
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sickr-sts
+      name: MTEB SICK-R
+      config: default
+      split: test
+      revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
+    metrics:
+    - type: cos_sim_pearson
+      value: 87.31937967632581
+    - type: cos_sim_spearman
+      value: 84.30523596401186
+    - type: euclidean_pearson
+      value: 84.19537987069458
+    - type: euclidean_spearman
+      value: 84.30522052876
+    - type: manhattan_pearson
+      value: 84.16420807244911
+    - type: manhattan_spearman
+      value: 84.28515410219309
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts12-sts
+      name: MTEB STS12
+      config: default
+      split: test
+      revision: a0d554a64d88156834ff5ae9920b964011b16384
+    metrics:
+    - type: cos_sim_pearson
+      value: 86.17180810119646
+    - type: cos_sim_spearman
+      value: 78.44413657529002
+    - type: euclidean_pearson
+      value: 81.69054139101816
+    - type: euclidean_spearman
+      value: 78.44412412142488
+    - type: manhattan_pearson
+      value: 82.04975789626462
+    - type: manhattan_spearman
+      value: 78.78390856857253
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts13-sts
+      name: MTEB STS13
+      config: default
+      split: test
+      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
+    metrics:
+    - type: cos_sim_pearson
+      value: 88.35737871089687
+    - type: cos_sim_spearman
+      value: 88.26850223126127
+    - type: euclidean_pearson
+      value: 87.44100858335746
+    - type: euclidean_spearman
+      value: 88.26850223126127
+    - type: manhattan_pearson
+      value: 87.61572015772133
+    - type: manhattan_spearman
+      value: 88.56229552813319
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts14-sts
+      name: MTEB STS14
+      config: default
+      split: test
+      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
+    metrics:
+    - type: cos_sim_pearson
+      value: 86.8395966764906
+    - type: cos_sim_spearman
+      value: 84.49441798385489
+    - type: euclidean_pearson
+      value: 85.3259176121388
+    - type: euclidean_spearman
+      value: 84.49442124804686
+    - type: manhattan_pearson
+      value: 85.35153862806513
+    - type: manhattan_spearman
+      value: 84.60094577432503
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts15-sts
+      name: MTEB STS15
+      config: default
+      split: test
+      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
+    metrics:
+    - type: cos_sim_pearson
+      value: 90.14048269057345
+    - type: cos_sim_spearman
+      value: 90.27866978947013
+    - type: euclidean_pearson
+      value: 89.35308361940393
+    - type: euclidean_spearman
+      value: 90.27866978947013
+    - type: manhattan_pearson
+      value: 89.37601244066997
+    - type: manhattan_spearman
+      value: 90.42707449698062
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts16-sts
+      name: MTEB STS16
+      config: default
+      split: test
+      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
+    metrics:
+    - type: cos_sim_pearson
+      value: 86.8522678865688
+    - type: cos_sim_spearman
+      value: 87.37396401580446
+    - type: euclidean_pearson
+      value: 86.37219665505377
+    - type: euclidean_spearman
+      value: 87.37396385867791
+    - type: manhattan_pearson
+      value: 86.44628823799896
+    - type: manhattan_spearman
+      value: 87.49116026788859
+  - 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: 92.94248481968916
+    - type: cos_sim_spearman
+      value: 92.68185242943188
+    - type: euclidean_pearson
+      value: 92.33802342092979
+    - type: euclidean_spearman
+      value: 92.68185242943188
+    - type: manhattan_pearson
+      value: 92.2011323340474
+    - type: manhattan_spearman
+      value: 92.43364757640346
+  - task:
+      type: STS
+    dataset:
+      type: mteb/sts22-crosslingual-sts
+      name: MTEB STS22 (en)
+      config: en
+      split: test
+      revision: eea2b4fe26a775864c896887d910b76a8098ad3f
+    metrics:
+    - type: cos_sim_pearson
+      value: 70.2918782293091
+    - type: cos_sim_spearman
+      value: 68.61986257003369
+    - type: euclidean_pearson
+      value: 70.51920905899138
+    - type: euclidean_spearman
+      value: 68.61986257003369
+    - type: manhattan_pearson
+      value: 70.64673843811433
+    - type: manhattan_spearman
+      value: 68.86711466517345
+  - task:
+      type: STS
+    dataset:
+      type: mteb/stsbenchmark-sts
+      name: MTEB STSBenchmark
+      config: default
+      split: test
+      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
+    metrics:
+    - type: cos_sim_pearson
+      value: 88.62956838105524
+    - type: cos_sim_spearman
+      value: 88.80650007123052
+    - type: euclidean_pearson
+      value: 88.37976252122822
+    - type: euclidean_spearman
+      value: 88.80650007123052
+    - type: manhattan_pearson
+      value: 88.49866938476616
+    - type: manhattan_spearman
+      value: 89.02489665452616
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/scidocs-reranking
+      name: MTEB SciDocsRR
+      config: default
+      split: test
+      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
+    metrics:
+    - type: map
+      value: 86.40175229911527
+    - type: mrr
+      value: 96.61958230585682
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/scifact
+      name: MTEB SciFact
+      config: default
+      split: test
+      revision: 0228b52cf27578f30900b9e5271d331663a030d7
+    metrics:
+    - type: map_at_1
+      value: 63.05
+    - type: map_at_10
+      value: 73.844
+    - type: map_at_100
+      value: 74.313
+    - type: map_at_1000
+      value: 74.321
+    - type: map_at_3
+      value: 71.17999999999999
+    - type: map_at_5
+      value: 72.842
+    - type: mrr_at_1
+      value: 65.667
+    - type: mrr_at_10
+      value: 74.772
+    - type: mrr_at_100
+      value: 75.087
+    - type: mrr_at_1000
+      value: 75.095
+    - type: mrr_at_3
+      value: 72.944
+    - type: mrr_at_5
+      value: 74.078
+    - type: ndcg_at_1
+      value: 65.667
+    - type: ndcg_at_10
+      value: 78.31700000000001
+    - type: ndcg_at_100
+      value: 79.969
+    - type: ndcg_at_1000
+      value: 80.25
+    - type: ndcg_at_3
+      value: 74.099
+    - type: ndcg_at_5
+      value: 76.338
+    - type: precision_at_1
+      value: 65.667
+    - type: precision_at_10
+      value: 10.233
+    - type: precision_at_100
+      value: 1.107
+    - type: precision_at_1000
+      value: 0.11299999999999999
+    - type: precision_at_3
+      value: 28.889
+    - type: precision_at_5
+      value: 19.0
+    - type: recall_at_1
+      value: 63.05
+    - type: recall_at_10
+      value: 90.822
+    - type: recall_at_100
+      value: 97.667
+    - type: recall_at_1000
+      value: 100.0
+    - type: recall_at_3
+      value: 79.489
+    - type: recall_at_5
+      value: 85.161
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/sprintduplicatequestions-pairclassification
+      name: MTEB SprintDuplicateQuestions
+      config: default
+      split: test
+      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
+    metrics:
+    - type: cos_sim_accuracy
+      value: 99.83564356435643
+    - type: cos_sim_ap
+      value: 96.10619363017767
+    - type: cos_sim_f1
+      value: 91.61225514816677
+    - type: cos_sim_precision
+      value: 92.02825428859738
+    - type: cos_sim_recall
+      value: 91.2
+    - type: dot_accuracy
+      value: 99.83564356435643
+    - type: dot_ap
+      value: 96.10619363017767
+    - type: dot_f1
+      value: 91.61225514816677
+    - type: dot_precision
+      value: 92.02825428859738
+    - type: dot_recall
+      value: 91.2
+    - type: euclidean_accuracy
+      value: 99.83564356435643
+    - type: euclidean_ap
+      value: 96.10619363017769
+    - type: euclidean_f1
+      value: 91.61225514816677
+    - type: euclidean_precision
+      value: 92.02825428859738
+    - type: euclidean_recall
+      value: 91.2
+    - type: manhattan_accuracy
+      value: 99.84158415841584
+    - type: manhattan_ap
+      value: 96.27527798658713
+    - type: manhattan_f1
+      value: 92.0
+    - type: manhattan_precision
+      value: 92.0
+    - type: manhattan_recall
+      value: 92.0
+    - type: max_accuracy
+      value: 99.84158415841584
+    - type: max_ap
+      value: 96.27527798658713
+    - type: max_f1
+      value: 92.0
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering
+      name: MTEB StackExchangeClustering
+      config: default
+      split: test
+      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
+    metrics:
+    - type: v_measure
+      value: 76.93753872885304
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/stackexchange-clustering-p2p
+      name: MTEB StackExchangeClusteringP2P
+      config: default
+      split: test
+      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
+    metrics:
+    - type: v_measure
+      value: 46.044085080870126
+  - task:
+      type: Reranking
+    dataset:
+      type: mteb/stackoverflowdupquestions-reranking
+      name: MTEB StackOverflowDupQuestions
+      config: default
+      split: test
+      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
+    metrics:
+    - type: map
+      value: 55.885129730227256
+    - type: mrr
+      value: 56.95062494694848
+  - task:
+      type: Summarization
+    dataset:
+      type: mteb/summeval
+      name: MTEB SummEval
+      config: default
+      split: test
+      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
+    metrics:
+    - type: cos_sim_pearson
+      value: 31.202047940935508
+    - type: cos_sim_spearman
+      value: 30.984832035722228
+    - type: dot_pearson
+      value: 31.20204247226978
+    - type: dot_spearman
+      value: 30.984832035722228
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/trec-covid
+      name: MTEB TRECCOVID
+      config: default
+      split: test
+      revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
+    metrics:
+    - type: map_at_1
+      value: 0.245
+    - type: map_at_10
+      value: 2.249
+    - type: map_at_100
+      value: 14.85
+    - type: map_at_1000
+      value: 36.596000000000004
+    - type: map_at_3
+      value: 0.717
+    - type: map_at_5
+      value: 1.18
+    - type: mrr_at_1
+      value: 94.0
+    - type: mrr_at_10
+      value: 96.167
+    - type: mrr_at_100
+      value: 96.167
+    - type: mrr_at_1000
+      value: 96.167
+    - type: mrr_at_3
+      value: 95.667
+    - type: mrr_at_5
+      value: 96.167
+    - type: ndcg_at_1
+      value: 91.0
+    - type: ndcg_at_10
+      value: 87.09700000000001
+    - type: ndcg_at_100
+      value: 69.637
+    - type: ndcg_at_1000
+      value: 62.257
+    - type: ndcg_at_3
+      value: 90.235
+    - type: ndcg_at_5
+      value: 89.51400000000001
+    - type: precision_at_1
+      value: 94.0
+    - type: precision_at_10
+      value: 90.60000000000001
+    - type: precision_at_100
+      value: 71.38
+    - type: precision_at_1000
+      value: 27.400000000000002
+    - type: precision_at_3
+      value: 94.0
+    - type: precision_at_5
+      value: 93.2
+    - type: recall_at_1
+      value: 0.245
+    - type: recall_at_10
+      value: 2.366
+    - type: recall_at_100
+      value: 17.491
+    - type: recall_at_1000
+      value: 58.772999999999996
+    - type: recall_at_3
+      value: 0.7270000000000001
+    - type: recall_at_5
+      value: 1.221
+  - task:
+      type: Retrieval
+    dataset:
+      type: mteb/touche2020
+      name: MTEB Touche2020
+      config: default
+      split: test
+      revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
+    metrics:
+    - type: map_at_1
+      value: 3.435
+    - type: map_at_10
+      value: 12.147
+    - type: map_at_100
+      value: 18.724
+    - type: map_at_1000
+      value: 20.426
+    - type: map_at_3
+      value: 6.526999999999999
+    - type: map_at_5
+      value: 9.198
+    - type: mrr_at_1
+      value: 48.980000000000004
+    - type: mrr_at_10
+      value: 62.970000000000006
+    - type: mrr_at_100
+      value: 63.288999999999994
+    - type: mrr_at_1000
+      value: 63.288999999999994
+    - type: mrr_at_3
+      value: 59.184000000000005
+    - type: mrr_at_5
+      value: 61.224000000000004
+    - type: ndcg_at_1
+      value: 46.939
+    - type: ndcg_at_10
+      value: 30.61
+    - type: ndcg_at_100
+      value: 41.683
+    - type: ndcg_at_1000
+      value: 53.144000000000005
+    - type: ndcg_at_3
+      value: 36.284
+    - type: ndcg_at_5
+      value: 34.345
+    - type: precision_at_1
+      value: 48.980000000000004
+    - type: precision_at_10
+      value: 26.122
+    - type: precision_at_100
+      value: 8.204
+    - type: precision_at_1000
+      value: 1.6019999999999999
+    - type: precision_at_3
+      value: 35.374
+    - type: precision_at_5
+      value: 32.653
+    - type: recall_at_1
+      value: 3.435
+    - type: recall_at_10
+      value: 18.953
+    - type: recall_at_100
+      value: 50.775000000000006
+    - type: recall_at_1000
+      value: 85.858
+    - type: recall_at_3
+      value: 7.813000000000001
+    - type: recall_at_5
+      value: 11.952
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/toxic_conversations_50k
+      name: MTEB ToxicConversationsClassification
+      config: default
+      split: test
+      revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
+    metrics:
+    - type: accuracy
+      value: 71.2938
+    - type: ap
+      value: 15.090139095602268
+    - type: f1
+      value: 55.23862650598296
+  - task:
+      type: Classification
+    dataset:
+      type: mteb/tweet_sentiment_extraction
+      name: MTEB TweetSentimentExtractionClassification
+      config: default
+      split: test
+      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
+    metrics:
+    - type: accuracy
+      value: 64.7623089983022
+    - type: f1
+      value: 65.07617131099336
+  - task:
+      type: Clustering
+    dataset:
+      type: mteb/twentynewsgroups-clustering
+      name: MTEB TwentyNewsgroupsClustering
+      config: default
+      split: test
+      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
+    metrics:
+    - type: v_measure
+      value: 57.2988222684939
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twittersemeval2015-pairclassification
+      name: MTEB TwitterSemEval2015
+      config: default
+      split: test
+      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
+    metrics:
+    - type: cos_sim_accuracy
+      value: 88.6034451928235
+    - type: cos_sim_ap
+      value: 81.51815279166863
+    - type: cos_sim_f1
+      value: 74.43794671864849
+    - type: cos_sim_precision
+      value: 73.34186939820742
+    - type: cos_sim_recall
+      value: 75.56728232189973
+    - type: dot_accuracy
+      value: 88.6034451928235
+    - type: dot_ap
+      value: 81.51816956866841
+    - type: dot_f1
+      value: 74.43794671864849
+    - type: dot_precision
+      value: 73.34186939820742
+    - type: dot_recall
+      value: 75.56728232189973
+    - type: euclidean_accuracy
+      value: 88.6034451928235
+    - type: euclidean_ap
+      value: 81.51817015121485
+    - type: euclidean_f1
+      value: 74.43794671864849
+    - type: euclidean_precision
+      value: 73.34186939820742
+    - type: euclidean_recall
+      value: 75.56728232189973
+    - type: manhattan_accuracy
+      value: 88.5736424867378
+    - type: manhattan_ap
+      value: 81.37610101292196
+    - type: manhattan_f1
+      value: 74.2504182215931
+    - type: manhattan_precision
+      value: 72.46922883697563
+    - type: manhattan_recall
+      value: 76.12137203166228
+    - type: max_accuracy
+      value: 88.6034451928235
+    - type: max_ap
+      value: 81.51817015121485
+    - type: max_f1
+      value: 74.43794671864849
+  - task:
+      type: PairClassification
+    dataset:
+      type: mteb/twitterurlcorpus-pairclassification
+      name: MTEB TwitterURLCorpus
+      config: default
+      split: test
+      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
+    metrics:
+    - type: cos_sim_accuracy
+      value: 89.53118329646446
+    - type: cos_sim_ap
+      value: 87.41972033060013
+    - type: cos_sim_f1
+      value: 79.4392523364486
+    - type: cos_sim_precision
+      value: 75.53457372951958
+    - type: cos_sim_recall
+      value: 83.7696335078534
+    - type: dot_accuracy
+      value: 89.53118329646446
+    - type: dot_ap
+      value: 87.41971646088945
+    - type: dot_f1
+      value: 79.4392523364486
+    - type: dot_precision
+      value: 75.53457372951958
+    - type: dot_recall
+      value: 83.7696335078534
+    - type: euclidean_accuracy
+      value: 89.53118329646446
+    - type: euclidean_ap
+      value: 87.41972415605997
+    - type: euclidean_f1
+      value: 79.4392523364486
+    - type: euclidean_precision
+      value: 75.53457372951958
+    - type: euclidean_recall
+      value: 83.7696335078534
+    - type: manhattan_accuracy
+      value: 89.5855163581325
+    - type: manhattan_ap
+      value: 87.51158697451964
+    - type: manhattan_f1
+      value: 79.54455087655883
+    - type: manhattan_precision
+      value: 74.96763643796416
+    - type: manhattan_recall
+      value: 84.71666153372344
+    - type: max_accuracy
+      value: 89.5855163581325
+    - type: max_ap
+      value: 87.51158697451964
+    - type: max_f1
+      value: 79.54455087655883
+language:
+  - en
+license: cc-by-nc-4.0
+---
+# Linq-AI-Research/Linq-Embed-Mistral (Quantized)
+
+## Description
+This model is a quantized version of the original model [`Linq-AI-Research/Linq-Embed-Mistral`](https://huggingface.co/Linq-AI-Research/Linq-Embed-Mistral). 
+
+It's quantized using the BitsAndBytes library to 4-bit using the [bnb-my-repo](https://huggingface.co/spaces/bnb-community/bnb-my-repo) space.
+
+## Quantization Details
+- **Quantization Type**: int4
+- **bnb_4bit_quant_type**: nf4
+- **bnb_4bit_use_double_quant**: True
+- **bnb_4bit_compute_dtype**: bfloat16
+- **bnb_4bit_quant_storage**: uint8
+
+
+
+# 📄 Original Model Information
+
+
+<h1 align="center">Linq-AI-Research/Linq-Embed-Mistral</h1>
+
+**Linq-Embed-Mistral**
+
+Linq-Embed-Mistral has been developed by building upon the foundations of the [E5-mistral-7b-instruct](https://huggingface.co/intfloat/e5-mistral-7b-instruct) and [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) models. We focus on improving text retrieval using advanced data refinement methods, including sophisticated data crafting, data filtering, and negative mining guided by teacher models, which are highly tailored to each task, to improve the quality of the synthetic data generated by LLM. These methods are applied to both existing benchmark dataset and highly tailored synthetic dataset generated via LLMs. Our efforts primarily aim to create high-quality triplet datasets (query, positive example, negative example), significantly improving text retrieval performance.
+
+Linq-Embed-Mistral performs well in the MTEB benchmarks (as of May 29, 2024). The model excels in retrieval tasks, ranking <ins>**`1st`**</ins> among all models listed on the MTEB leaderboard with a performance score of <ins>**`60.2`**</ins>. This outstanding performance underscores its superior capability in enhancing search precision and reliability. The model achieves an average score of <ins>**`68.2`**</ins> across 56 datasets in the MTEB benchmarks, making it the highest-ranking publicly accessible model and third overall. (Please note that [NV-Emb-v1](https://huggingface.co/nvidia/NV-Embed-v1) and [voyage-large-2-instruct](https://docs.voyageai.com/embeddings/), ranked 1st and 2nd on the leaderboard as of May 29, reported their performance without releasing their models.)
+
+
+This project is for research purposes only. Third-party datasets may be subject to additional terms and conditions under their associated licenses. Please refer to specific papers for more details:
+
+- [MTEB benchmark](https://arxiv.org/abs/2210.07316)
+- [Mistral](https://arxiv.org/abs/2310.06825)
+- [E5-mistral-7b-instruct](https://arxiv.org/pdf/2401.00368.pdf)
+
+For more details, refer to [this blog post](https://getlinq.com/blog/linq-embed-mistral/) and [this report](https://huggingface.co/Linq-AI-Research/Linq-Embed-Mistral/blob/main/LinqAIResearch2024_Linq-Embed-Mistral.pdf).
+
+## How to use
+
+Here is an example of how to encode queries and passages from the Mr.TyDi training dataset, both with Sentence Transformers or Transformers directly.
+
+### Sentence Transformers
+
+```python
+from sentence_transformers import SentenceTransformer
+
+# Load the model
+model = SentenceTransformer("Linq-AI-Research/Linq-Embed-Mistral")
+
+# Each query must come with a one-sentence instruction that describes the task
+task = 'Given a question, retrieve Wikipedia passages that answer the question'
+prompt = f"Instruct: {task}\nQuery: "
+queries = [
+    "최초의 원자력 발전소는 무엇인가?",
+    "Who invented Hangul?"
+]
+passages = [
+    "현재 사용되는 핵분열 방식을 이용한 전력생산은 1948년 9월 미국 테네시주 오크리지에 설치된 X-10 흑연원자로에서 전구의 불을 밝히는 데 사용되면서 시작되었다. 그리고 1954년 6월에 구소련의 오브닌스크에 건설된 흑연감속 비등경수 압력관형 원자로를 사용한 오브닌스크 원자력 발전소가 시험적으로 전력생산을 시작하였고, 최초의 상업용 원자력 엉더이로를 사용한 영국 셀라필드 원자력 단지에 위치한 콜더 홀(Calder Hall) 원자력 발전소로, 1956년 10월 17일 상업 운전을 시작하였다.",
+    "Hangul was personally created and promulgated by the fourth king of the Joseon dynasty, Sejong the Great.[1][2] Sejong's scholarly institute, the Hall of Worthies, is often credited with the work, and at least one of its scholars was heavily involved in its creation, but it appears to have also been a personal project of Sejong."
+]
+
+# Encode the queries and passages. We only use the prompt for the queries
+query_embeddings = model.encode(queries, prompt=prompt)
+passage_embeddings = model.encode(passages)
+
+# Compute the (cosine) similarity scores
+scores = model.similarity(query_embeddings, passage_embeddings) * 100
+print(scores.tolist())
+# [[73.72908782958984, 30.122787475585938], [29.15508460998535, 79.25375366210938]]
+```
+
+### Transformers
+
+```python
+import torch
+import torch.nn.functional as F
+from torch import Tensor
+from transformers import AutoTokenizer, AutoModel
+
+def last_token_pool(last_hidden_states: Tensor,
+                 attention_mask: Tensor) -> Tensor:
+    left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
+    if left_padding:
+        return last_hidden_states[:, -1]
+    else:
+        sequence_lengths = attention_mask.sum(dim=1) - 1
+        batch_size = last_hidden_states.shape[0]
+        return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]
+
+def get_detailed_instruct(task_description: str, query: str) -> str:
+    return f'Instruct: {task_description}\nQuery: {query}'
+
+# Each query must come with a one-sentence instruction that describes the task
+task = 'Given a question, retrieve Wikipedia passages that answer the question'
+queries = [
+    get_detailed_instruct(task, '최초의 원자력 발전소는 무엇인가?'),
+    get_detailed_instruct(task, 'Who invented Hangul?')
+]
+# No need to add instruction for retrieval documents
+passages = [
+    "현재 사용되는 핵분열 방식을 이용한 전력생산은 1948년 9월 미국 테네시주 오크리지에 설치된 X-10 흑연원자로에서 전구의 불을 밝히는 데 사용되면서 시작되었다. 그리고 1954년 6월에 구소련의 오브닌스크에 건설된 흑연감속 비등경수 압력관형 원자로를 사용한 오브닌스크 원자력 발전소가 시험적으로 전력생산을 시작하였고, 최초의 상업용 원자력 엉더이로를 사용한 영국 셀라필드 원자력 단지에 위치한 콜더 홀(Calder Hall) 원자력 발전소로, 1956년 10월 17일 상업 운전을 시작하였다.",
+    "Hangul was personally created and promulgated by the fourth king of the Joseon dynasty, Sejong the Great.[1][2] Sejong's scholarly institute, the Hall of Worthies, is often credited with the work, and at least one of its scholars was heavily involved in its creation, but it appears to have also been a personal project of Sejong."
+]
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained('Linq-AI-Research/Linq-Embed-Mistral')
+model = AutoModel.from_pretrained('Linq-AI-Research/Linq-Embed-Mistral')
+
+max_length = 4096
+input_texts = [*queries, *passages]
+# Tokenize the input texts
+batch_dict = tokenizer(input_texts, max_length=max_length, padding=True, truncation=True, return_tensors="pt")
+outputs = model(**batch_dict)
+embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
+
+# Normalize embeddings
+embeddings = F.normalize(embeddings, p=2, dim=1)
+scores = (embeddings[:2] @ embeddings[2:].T) * 100
+print(scores.tolist())
+# [[73.72909545898438, 30.122783660888672], [29.155078887939453, 79.25374603271484]]
+```
+
+### MTEB Benchmark Evaluation
+
+Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB](https://arxiv.org/abs/2210.07316) benchmark.
+
+## Evaluation Result
+
+### MTEB (as of May 29, 2024)
+
+|                                    Model Name                                    | Retrieval (15) | Average (56) |
+| :------------------------------------------------------------------------------: | :------------: | :----------: |
+| [Linq-Embed-Mistral](https://huggingface.co/Linq-AI-Research/Linq-Embed-Mistral) |      60.2      |     68.2     |
+|             [NV-Embed-v1](https://huggingface.co/nvidia/NV-Embed-v1)             |      59.4      |     69.3     |
+| [SFR-Embedding-Mistral](https://huggingface.co/Salesforce/SFR-Embedding-Mistral) |      59.0      |     67.6     |
+|       [voyage-large-2-instruct](https://docs.voyageai.com/docs/embeddings)       |      58.3      |     68.3     |
+|               [GritLM-7B](https://huggingface.co/GritLM/GritLM-7B)               |      57.4      |     66.8     |
+|       [voyage-lite-02-instruct](https://docs.voyageai.com/docs/embeddings)        |      56.6      |     67.1     |
+|[gte-Qwen1.5-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen1.5-7B-instruct)|      56.2      |     67.3     |
+| [e5-mistral-7b-instruct](https://huggingface.co/intfloat/e5-mistral-7b-instruct) |      56.9      |     66.6     |
+|[google-gecko.text-embedding-preview-0409](https://cloud.google.com/vertex-ai/generative-ai/docs/embeddings/get-text-embeddings?hl=ko#latest_models)|      55.7      |     66.3     |
+|[text-embedding-3-large](https://openai.com/index/new-embedding-models-and-api-updates/)|      55.4      |     64.6     |
+|[Cohere-embed-english-v3.0](https://huggingface.co/Cohere/Cohere-embed-english-v3.0)|      55.0      |     64.5     |
+
+# Linq Research Team.
+
+- [Junseong Kim](https://huggingface.co/Junseong)
+- [Seolhwa Lee](https://huggingface.co/Seolhwa)
+- [Jihoon Kwon](https://huggingface.co/Mayfull)
+- [Sangmo Gu](https://huggingface.co/karma-os)
+- Yejin Kim
+- Minkyung Cho
+- [Jy-yong Sohn](https://itml.yonsei.ac.kr/professor)
+- [Chanyeol Choi](https://www.linkedin.com/in/chanyeolchoi)
+
+# Citation
+
+```bibtex
+@misc{LinqAIResearch2024,
+  title={Linq-Embed-Mistral:Elevating Text Retrieval with Improved GPT Data Through Task-Specific Control and Quality Refinement},
+  author={Junseong Kim, Seolhwa Lee, Jihoon Kwon, Sangmo Gu, Yejin Kim, Minkyung Cho, Jy-yong Sohn, Chanyeol Choi},
+  howpublished={Linq AI Research Blog},
+  year={2024},
+  url={https://getlinq.com/blog/linq-embed-mistral/}
+}
+```

config.json

@@ -0,0 +1,43 @@
+{
+  "_name_or_path": "Linq-AI-Research/Linq-Embed-Mistral",
+  "architectures": [
+    "MistralModel"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 32768,
+  "model_type": "mistral",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pad_token_id": 2,
+  "quantization_config": {
+    "_load_in_4bit": true,
+    "_load_in_8bit": false,
+    "bnb_4bit_compute_dtype": "bfloat16",
+    "bnb_4bit_quant_storage": "uint8",
+    "bnb_4bit_quant_type": "nf4",
+    "bnb_4bit_use_double_quant": true,
+    "llm_int8_enable_fp32_cpu_offload": false,
+    "llm_int8_has_fp16_weight": false,
+    "llm_int8_skip_modules": null,
+    "llm_int8_threshold": 6.0,
+    "load_in_4bit": true,
+    "load_in_8bit": false,
+    "quant_method": "bitsandbytes"
+  },
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 10000.0,
+  "sliding_window": 4096,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.49.0",
+  "use_cache": false,
+  "vocab_size": 32000
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:14256c99f4caa01de57298e548530a6536de5ca91bd9f29284fad86b6ccdce5a
+size 3863534977

special_tokens_map.json

@@ -0,0 +1,35 @@
+{
+  "additional_special_tokens": [
+    "<unk>",
+    "<s>",
+    "</s>"
+  ],
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer_config.json

@@ -0,0 +1,48 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": true,
+  "add_prefix_space": null,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [
+    "<unk>",
+    "<s>",
+    "</s>"
+  ],
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
+  "extra_special_tokens": {},
+  "legacy": true,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "</s>",
+  "sp_model_kwargs": {},
+  "spaces_between_special_tokens": false,
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}