README draft

README.md

@@ -2,2607 +2,114 @@
 pipeline_tag: sentence-similarity
 tags:
   - finetuner
+  - mteb
   - sentence-transformers
   - feature-extraction
   - sentence-similarity
-  - mteb
+  - alibi
 datasets:
-  - jinaai/negation-dataset
+  - allenai/c4
 language: en
 license: apache-2.0
 model-index:
-- name: jina-embedding-s-en-v2
-  results:
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/amazon_counterfactual
-      name: MTEB AmazonCounterfactualClassification (en)
-      config: en
-      split: test
-      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
-    metrics:
-    - type: accuracy
-      value: 69.70149253731343
-    - type: ap
-      value: 32.22528779918184
-    - type: f1
-      value: 63.66857824618267
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/amazon_polarity
-      name: MTEB AmazonPolarityClassification
-      config: default
-      split: test
-      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
-    metrics:
-    - type: accuracy
-      value: 79.55879999999999
-    - type: ap
-      value: 73.97885664972738
-    - type: f1
-      value: 79.4849322624122
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/amazon_reviews_multi
-      name: MTEB AmazonReviewsClassification (en)
-      config: en
-      split: test
-      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
-    metrics:
-    - type: accuracy
-      value: 38.69
-    - type: f1
-      value: 37.17512734389121
-  - task:
-      type: Retrieval
-    dataset:
-      type: arguana
-      name: MTEB ArguAna
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 23.684
-    - type: map_at_10
-      value: 39.086999999999996
-    - type: map_at_100
-      value: 40.222
-    - type: map_at_1000
-      value: 40.231
-    - type: map_at_3
-      value: 34.282000000000004
-    - type: map_at_5
-      value: 36.689
-    - type: mrr_at_1
-      value: 23.826
-    - type: mrr_at_10
-      value: 39.147
-    - type: mrr_at_100
-      value: 40.282000000000004
-    - type: mrr_at_1000
-      value: 40.291
-    - type: mrr_at_3
-      value: 34.353
-    - type: mrr_at_5
-      value: 36.739
-    - type: ndcg_at_1
-      value: 23.684
-    - type: ndcg_at_10
-      value: 48.081
-    - type: ndcg_at_100
-      value: 52.902
-    - type: ndcg_at_1000
-      value: 53.111
-    - type: ndcg_at_3
-      value: 37.937
-    - type: ndcg_at_5
-      value: 42.32
-    - type: precision_at_1
-      value: 23.684
-    - type: precision_at_10
-      value: 7.703
-    - type: precision_at_100
-      value: 0.98
-    - type: precision_at_1000
-      value: 0.1
-    - type: precision_at_3
-      value: 16.192999999999998
-    - type: precision_at_5
-      value: 11.863
-    - type: recall_at_1
-      value: 23.684
-    - type: recall_at_10
-      value: 77.027
-    - type: recall_at_100
-      value: 98.009
-    - type: recall_at_1000
-      value: 99.57300000000001
-    - type: recall_at_3
-      value: 48.577999999999996
-    - type: recall_at_5
-      value: 59.317
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/arxiv-clustering-p2p
-      name: MTEB ArxivClusteringP2P
-      config: default
-      split: test
-      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
-    metrics:
-    - type: v_measure
-      value: 44.249612940073035
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/arxiv-clustering-s2s
-      name: MTEB ArxivClusteringS2S
-      config: default
-      split: test
-      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
-    metrics:
-    - type: v_measure
-      value: 35.39423011105325
-  - task:
-      type: Reranking
-    dataset:
-      type: mteb/askubuntudupquestions-reranking
-      name: MTEB AskUbuntuDupQuestions
-      config: default
-      split: test
-      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
-    metrics:
-    - type: map
-      value: 59.89078304869791
-    - type: mrr
-      value: 73.5045948203843
-  - task:
-      type: STS
-    dataset:
-      type: mteb/biosses-sts
-      name: MTEB BIOSSES
-      config: default
-      split: test
-      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
-    metrics:
-    - type: cos_sim_pearson
-      value: 82.49373811125967
-    - type: cos_sim_spearman
-      value: 81.0446177409314
-    - type: euclidean_pearson
-      value: 82.1327844624042
-    - type: euclidean_spearman
-      value: 81.0446177409314
-    - type: manhattan_pearson
-      value: 81.88575541723692
-    - type: manhattan_spearman
-      value: 81.0705219456341
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/banking77
-      name: MTEB Banking77Classification
-      config: default
-      split: test
-      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
-    metrics:
-    - type: accuracy
-      value: 78.27272727272728
-    - type: f1
-      value: 77.36583416688741
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/biorxiv-clustering-p2p
-      name: MTEB BiorxivClusteringP2P
-      config: default
-      split: test
-      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
-    metrics:
-    - type: v_measure
-      value: 36.12447585258704
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/biorxiv-clustering-s2s
-      name: MTEB BiorxivClusteringS2S
-      config: default
-      split: test
-      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
-    metrics:
-    - type: v_measure
-      value: 29.305990951348743
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackAndroidRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 31.458000000000002
-    - type: map_at_10
-      value: 42.132
-    - type: map_at_100
-      value: 43.47
-    - type: map_at_1000
-      value: 43.612
-    - type: map_at_3
-      value: 38.718
-    - type: map_at_5
-      value: 40.556
-    - type: mrr_at_1
-      value: 38.627
-    - type: mrr_at_10
-      value: 47.998000000000005
-    - type: mrr_at_100
-      value: 48.726
-    - type: mrr_at_1000
-      value: 48.778
-    - type: mrr_at_3
-      value: 45.255
-    - type: mrr_at_5
-      value: 46.893
-    - type: ndcg_at_1
-      value: 38.627
-    - type: ndcg_at_10
-      value: 48.229
-    - type: ndcg_at_100
-      value: 53.108999999999995
-    - type: ndcg_at_1000
-      value: 55.385
-    - type: ndcg_at_3
-      value: 43.191
-    - type: ndcg_at_5
-      value: 45.385999999999996
-    - type: precision_at_1
-      value: 38.627
-    - type: precision_at_10
-      value: 9.142
-    - type: precision_at_100
-      value: 1.462
-    - type: precision_at_1000
-      value: 0.19499999999999998
-    - type: precision_at_3
-      value: 20.552999999999997
-    - type: precision_at_5
-      value: 14.677999999999999
-    - type: recall_at_1
-      value: 31.458000000000002
-    - type: recall_at_10
-      value: 59.619
-    - type: recall_at_100
-      value: 79.953
-    - type: recall_at_1000
-      value: 94.921
-    - type: recall_at_3
-      value: 44.744
-    - type: recall_at_5
-      value: 51.010999999999996
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackEnglishRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 26.762000000000004
-    - type: map_at_10
-      value: 35.366
-    - type: map_at_100
-      value: 36.481
-    - type: map_at_1000
-      value: 36.614999999999995
-    - type: map_at_3
-      value: 33.071
-    - type: map_at_5
-      value: 34.495
-    - type: mrr_at_1
-      value: 33.312000000000005
-    - type: mrr_at_10
-      value: 40.841
-    - type: mrr_at_100
-      value: 41.54
-    - type: mrr_at_1000
-      value: 41.592
-    - type: mrr_at_3
-      value: 38.928000000000004
-    - type: mrr_at_5
-      value: 40.119
-    - type: ndcg_at_1
-      value: 33.312000000000005
-    - type: ndcg_at_10
-      value: 40.238
-    - type: ndcg_at_100
-      value: 44.647
-    - type: ndcg_at_1000
-      value: 47.010999999999996
-    - type: ndcg_at_3
-      value: 36.991
-    - type: ndcg_at_5
-      value: 38.721
-    - type: precision_at_1
-      value: 33.312000000000005
-    - type: precision_at_10
-      value: 7.4079999999999995
-    - type: precision_at_100
-      value: 1.253
-    - type: precision_at_1000
-      value: 0.17500000000000002
-    - type: precision_at_3
-      value: 17.898
-    - type: precision_at_5
-      value: 12.687999999999999
-    - type: recall_at_1
-      value: 26.762000000000004
-    - type: recall_at_10
-      value: 48.41
-    - type: recall_at_100
-      value: 67.523
-    - type: recall_at_1000
-      value: 82.91199999999999
-    - type: recall_at_3
-      value: 38.6
-    - type: recall_at_5
-      value: 43.477
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackGamingRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 37.578
-    - type: map_at_10
-      value: 49.415
-    - type: map_at_100
-      value: 50.339
-    - type: map_at_1000
-      value: 50.402
-    - type: map_at_3
-      value: 46.412
-    - type: map_at_5
-      value: 48.183
-    - type: mrr_at_1
-      value: 43.072
-    - type: mrr_at_10
-      value: 52.82599999999999
-    - type: mrr_at_100
-      value: 53.456
-    - type: mrr_at_1000
-      value: 53.493
-    - type: mrr_at_3
-      value: 50.407999999999994
-    - type: mrr_at_5
-      value: 51.922000000000004
-    - type: ndcg_at_1
-      value: 43.072
-    - type: ndcg_at_10
-      value: 54.949000000000005
-    - type: ndcg_at_100
-      value: 58.744
-    - type: ndcg_at_1000
-      value: 60.150000000000006
-    - type: ndcg_at_3
-      value: 49.864000000000004
-    - type: ndcg_at_5
-      value: 52.503
-    - type: precision_at_1
-      value: 43.072
-    - type: precision_at_10
-      value: 8.734
-    - type: precision_at_100
-      value: 1.1520000000000001
-    - type: precision_at_1000
-      value: 0.132
-    - type: precision_at_3
-      value: 22.131999999999998
-    - type: precision_at_5
-      value: 15.21
-    - type: recall_at_1
-      value: 37.578
-    - type: recall_at_10
-      value: 67.918
-    - type: recall_at_100
-      value: 84.373
-    - type: recall_at_1000
-      value: 94.529
-    - type: recall_at_3
-      value: 54.457
-    - type: recall_at_5
-      value: 60.941
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackGisRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 23.394000000000002
-    - type: map_at_10
-      value: 31.791000000000004
-    - type: map_at_100
-      value: 32.64
-    - type: map_at_1000
-      value: 32.727000000000004
-    - type: map_at_3
-      value: 29.557
-    - type: map_at_5
-      value: 30.858999999999998
-    - type: mrr_at_1
-      value: 25.085
-    - type: mrr_at_10
-      value: 33.721000000000004
-    - type: mrr_at_100
-      value: 34.492
-    - type: mrr_at_1000
-      value: 34.564
-    - type: mrr_at_3
-      value: 31.619999999999997
-    - type: mrr_at_5
-      value: 32.896
-    - type: ndcg_at_1
-      value: 25.085
-    - type: ndcg_at_10
-      value: 36.370000000000005
-    - type: ndcg_at_100
-      value: 40.96
-    - type: ndcg_at_1000
-      value: 43.171
-    - type: ndcg_at_3
-      value: 32.104
-    - type: ndcg_at_5
-      value: 34.300000000000004
-    - type: precision_at_1
-      value: 25.085
-    - type: precision_at_10
-      value: 5.537
-    - type: precision_at_100
-      value: 0.8340000000000001
-    - type: precision_at_1000
-      value: 0.105
-    - type: precision_at_3
-      value: 13.71
-    - type: precision_at_5
-      value: 9.514
-    - type: recall_at_1
-      value: 23.394000000000002
-    - type: recall_at_10
-      value: 48.549
-    - type: recall_at_100
-      value: 70.341
-    - type: recall_at_1000
-      value: 87.01299999999999
-    - type: recall_at_3
-      value: 36.947
-    - type: recall_at_5
-      value: 42.365
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackMathematicaRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 14.818000000000001
-    - type: map_at_10
-      value: 21.773999999999997
-    - type: map_at_100
-      value: 22.787
-    - type: map_at_1000
-      value: 22.915
-    - type: map_at_3
-      value: 19.414
-    - type: map_at_5
-      value: 20.651
-    - type: mrr_at_1
-      value: 18.657
-    - type: mrr_at_10
-      value: 25.794
-    - type: mrr_at_100
-      value: 26.695999999999998
-    - type: mrr_at_1000
-      value: 26.776
-    - type: mrr_at_3
-      value: 23.279
-    - type: mrr_at_5
-      value: 24.598
-    - type: ndcg_at_1
-      value: 18.657
-    - type: ndcg_at_10
-      value: 26.511000000000003
-    - type: ndcg_at_100
-      value: 31.447999999999997
-    - type: ndcg_at_1000
-      value: 34.71
-    - type: ndcg_at_3
-      value: 21.92
-    - type: ndcg_at_5
-      value: 23.938000000000002
-    - type: precision_at_1
-      value: 18.657
-    - type: precision_at_10
-      value: 4.9
-    - type: precision_at_100
-      value: 0.851
-    - type: precision_at_1000
-      value: 0.127
-    - type: precision_at_3
-      value: 10.488999999999999
-    - type: precision_at_5
-      value: 7.710999999999999
-    - type: recall_at_1
-      value: 14.818000000000001
-    - type: recall_at_10
-      value: 37.408
-    - type: recall_at_100
-      value: 58.81999999999999
-    - type: recall_at_1000
-      value: 82.612
-    - type: recall_at_3
-      value: 24.561
-    - type: recall_at_5
-      value: 29.685
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackPhysicsRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 26.332
-    - type: map_at_10
-      value: 35.366
-    - type: map_at_100
-      value: 36.569
-    - type: map_at_1000
-      value: 36.689
-    - type: map_at_3
-      value: 32.582
-    - type: map_at_5
-      value: 34.184
-    - type: mrr_at_1
-      value: 32.05
-    - type: mrr_at_10
-      value: 40.902
-    - type: mrr_at_100
-      value: 41.754000000000005
-    - type: mrr_at_1000
-      value: 41.811
-    - type: mrr_at_3
-      value: 38.547
-    - type: mrr_at_5
-      value: 40.019
-    - type: ndcg_at_1
-      value: 32.05
-    - type: ndcg_at_10
-      value: 40.999
-    - type: ndcg_at_100
-      value: 46.284
-    - type: ndcg_at_1000
-      value: 48.698
-    - type: ndcg_at_3
-      value: 36.39
-    - type: ndcg_at_5
-      value: 38.699
-    - type: precision_at_1
-      value: 32.05
-    - type: precision_at_10
-      value: 7.315
-    - type: precision_at_100
-      value: 1.172
-    - type: precision_at_1000
-      value: 0.156
-    - type: precision_at_3
-      value: 17.036
-    - type: precision_at_5
-      value: 12.089
-    - type: recall_at_1
-      value: 26.332
-    - type: recall_at_10
-      value: 52.410000000000004
-    - type: recall_at_100
-      value: 74.763
-    - type: recall_at_1000
-      value: 91.03
-    - type: recall_at_3
-      value: 39.527
-    - type: recall_at_5
-      value: 45.517
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackProgrammersRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 22.849
-    - type: map_at_10
-      value: 31.502000000000002
-    - type: map_at_100
-      value: 32.854
-    - type: map_at_1000
-      value: 32.975
-    - type: map_at_3
-      value: 28.997
-    - type: map_at_5
-      value: 30.508999999999997
-    - type: mrr_at_1
-      value: 28.195999999999998
-    - type: mrr_at_10
-      value: 36.719
-    - type: mrr_at_100
-      value: 37.674
-    - type: mrr_at_1000
-      value: 37.743
-    - type: mrr_at_3
-      value: 34.532000000000004
-    - type: mrr_at_5
-      value: 35.845
-    - type: ndcg_at_1
-      value: 28.195999999999998
-    - type: ndcg_at_10
-      value: 36.605
-    - type: ndcg_at_100
-      value: 42.524
-    - type: ndcg_at_1000
-      value: 45.171
-    - type: ndcg_at_3
-      value: 32.574
-    - type: ndcg_at_5
-      value: 34.617
-    - type: precision_at_1
-      value: 28.195999999999998
-    - type: precision_at_10
-      value: 6.598
-    - type: precision_at_100
-      value: 1.121
-    - type: precision_at_1000
-      value: 0.153
-    - type: precision_at_3
-      value: 15.601
-    - type: precision_at_5
-      value: 11.073
-    - type: recall_at_1
-      value: 22.849
-    - type: recall_at_10
-      value: 46.528000000000006
-    - type: recall_at_100
-      value: 72.09
-    - type: recall_at_1000
-      value: 90.398
-    - type: recall_at_3
-      value: 35.116
-    - type: recall_at_5
-      value: 40.778
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 24.319500000000005
-    - type: map_at_10
-      value: 32.530166666666666
-    - type: map_at_100
-      value: 33.61566666666667
-    - type: map_at_1000
-      value: 33.73808333333333
-    - type: map_at_3
-      value: 30.074583333333326
-    - type: map_at_5
-      value: 31.429666666666662
-    - type: mrr_at_1
-      value: 28.675916666666666
-    - type: mrr_at_10
-      value: 36.49308333333334
-    - type: mrr_at_100
-      value: 37.310583333333334
-    - type: mrr_at_1000
-      value: 37.37616666666666
-    - type: mrr_at_3
-      value: 34.283166666666666
-    - type: mrr_at_5
-      value: 35.54333333333334
-    - type: ndcg_at_1
-      value: 28.675916666666666
-    - type: ndcg_at_10
-      value: 37.403416666666665
-    - type: ndcg_at_100
-      value: 42.25783333333333
-    - type: ndcg_at_1000
-      value: 44.778333333333336
-    - type: ndcg_at_3
-      value: 33.17099999999999
-    - type: ndcg_at_5
-      value: 35.12666666666667
-    - type: precision_at_1
-      value: 28.675916666666666
-    - type: precision_at_10
-      value: 6.463083333333334
-    - type: precision_at_100
-      value: 1.0585
-    - type: precision_at_1000
-      value: 0.14633333333333332
-    - type: precision_at_3
-      value: 15.158999999999997
-    - type: precision_at_5
-      value: 10.673916666666667
-    - type: recall_at_1
-      value: 24.319500000000005
-    - type: recall_at_10
-      value: 47.9135
-    - type: recall_at_100
-      value: 69.40266666666666
-    - type: recall_at_1000
-      value: 87.12566666666666
-    - type: recall_at_3
-      value: 36.03149999999999
-    - type: recall_at_5
-      value: 41.12791666666668
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackStatsRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 22.997
-    - type: map_at_10
-      value: 28.754999999999995
-    - type: map_at_100
-      value: 29.555999999999997
-    - type: map_at_1000
-      value: 29.653000000000002
-    - type: map_at_3
-      value: 27.069
-    - type: map_at_5
-      value: 27.884999999999998
-    - type: mrr_at_1
-      value: 25.767
-    - type: mrr_at_10
-      value: 31.195
-    - type: mrr_at_100
-      value: 31.964
-    - type: mrr_at_1000
-      value: 32.039
-    - type: mrr_at_3
-      value: 29.601
-    - type: mrr_at_5
-      value: 30.345
-    - type: ndcg_at_1
-      value: 25.767
-    - type: ndcg_at_10
-      value: 32.234
-    - type: ndcg_at_100
-      value: 36.461
-    - type: ndcg_at_1000
-      value: 39.005
-    - type: ndcg_at_3
-      value: 29.052
-    - type: ndcg_at_5
-      value: 30.248
-    - type: precision_at_1
-      value: 25.767
-    - type: precision_at_10
-      value: 4.893
-    - type: precision_at_100
-      value: 0.761
-    - type: precision_at_1000
-      value: 0.105
-    - type: precision_at_3
-      value: 12.219
-    - type: precision_at_5
-      value: 8.19
-    - type: recall_at_1
-      value: 22.997
-    - type: recall_at_10
-      value: 40.652
-    - type: recall_at_100
-      value: 60.302
-    - type: recall_at_1000
-      value: 79.17999999999999
-    - type: recall_at_3
-      value: 31.680999999999997
-    - type: recall_at_5
-      value: 34.698
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackTexRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 16.3
-    - type: map_at_10
-      value: 22.581
-    - type: map_at_100
-      value: 23.517
-    - type: map_at_1000
-      value: 23.638
-    - type: map_at_3
-      value: 20.567
-    - type: map_at_5
-      value: 21.688
-    - type: mrr_at_1
-      value: 19.683
-    - type: mrr_at_10
-      value: 26.185000000000002
-    - type: mrr_at_100
-      value: 27.014
-    - type: mrr_at_1000
-      value: 27.092
-    - type: mrr_at_3
-      value: 24.145
-    - type: mrr_at_5
-      value: 25.308999999999997
-    - type: ndcg_at_1
-      value: 19.683
-    - type: ndcg_at_10
-      value: 26.699
-    - type: ndcg_at_100
-      value: 31.35
-    - type: ndcg_at_1000
-      value: 34.348
-    - type: ndcg_at_3
-      value: 23.026
-    - type: ndcg_at_5
-      value: 24.731
-    - type: precision_at_1
-      value: 19.683
-    - type: precision_at_10
-      value: 4.814
-    - type: precision_at_100
-      value: 0.836
-    - type: precision_at_1000
-      value: 0.126
-    - type: precision_at_3
-      value: 10.782
-    - type: precision_at_5
-      value: 7.825
-    - type: recall_at_1
-      value: 16.3
-    - type: recall_at_10
-      value: 35.521
-    - type: recall_at_100
-      value: 56.665
-    - type: recall_at_1000
-      value: 78.361
-    - type: recall_at_3
-      value: 25.223000000000003
-    - type: recall_at_5
-      value: 29.626
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackUnixRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 24.596999999999998
-    - type: map_at_10
-      value: 32.54
-    - type: map_at_100
-      value: 33.548
-    - type: map_at_1000
-      value: 33.661
-    - type: map_at_3
-      value: 30.134
-    - type: map_at_5
-      value: 31.468
-    - type: mrr_at_1
-      value: 28.825
-    - type: mrr_at_10
-      value: 36.495
-    - type: mrr_at_100
-      value: 37.329
-    - type: mrr_at_1000
-      value: 37.397999999999996
-    - type: mrr_at_3
-      value: 34.359
-    - type: mrr_at_5
-      value: 35.53
-    - type: ndcg_at_1
-      value: 28.825
-    - type: ndcg_at_10
-      value: 37.341
-    - type: ndcg_at_100
-      value: 42.221
-    - type: ndcg_at_1000
-      value: 44.799
-    - type: ndcg_at_3
-      value: 33.058
-    - type: ndcg_at_5
-      value: 34.961999999999996
-    - type: precision_at_1
-      value: 28.825
-    - type: precision_at_10
-      value: 6.175
-    - type: precision_at_100
-      value: 0.97
-    - type: precision_at_1000
-      value: 0.13
-    - type: precision_at_3
-      value: 14.924999999999999
-    - type: precision_at_5
-      value: 10.392
-    - type: recall_at_1
-      value: 24.596999999999998
-    - type: recall_at_10
-      value: 48.067
-    - type: recall_at_100
-      value: 69.736
-    - type: recall_at_1000
-      value: 87.855
-    - type: recall_at_3
-      value: 36.248999999999995
-    - type: recall_at_5
-      value: 41.086
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackWebmastersRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 24.224999999999998
-    - type: map_at_10
-      value: 31.826
-    - type: map_at_100
-      value: 33.366
-    - type: map_at_1000
-      value: 33.6
-    - type: map_at_3
-      value: 29.353
-    - type: map_at_5
-      value: 30.736
-    - type: mrr_at_1
-      value: 28.656
-    - type: mrr_at_10
-      value: 36.092
-    - type: mrr_at_100
-      value: 37.076
-    - type: mrr_at_1000
-      value: 37.141999999999996
-    - type: mrr_at_3
-      value: 33.86
-    - type: mrr_at_5
-      value: 35.144999999999996
-    - type: ndcg_at_1
-      value: 28.656
-    - type: ndcg_at_10
-      value: 37.025999999999996
-    - type: ndcg_at_100
-      value: 42.844
-    - type: ndcg_at_1000
-      value: 45.716
-    - type: ndcg_at_3
-      value: 32.98
-    - type: ndcg_at_5
-      value: 34.922
-    - type: precision_at_1
-      value: 28.656
-    - type: precision_at_10
-      value: 6.976
-    - type: precision_at_100
-      value: 1.48
-    - type: precision_at_1000
-      value: 0.23700000000000002
-    - type: precision_at_3
-      value: 15.348999999999998
-    - type: precision_at_5
-      value: 11.028
-    - type: recall_at_1
-      value: 24.224999999999998
-    - type: recall_at_10
-      value: 46.589999999999996
-    - type: recall_at_100
-      value: 72.331
-    - type: recall_at_1000
-      value: 90.891
-    - type: recall_at_3
-      value: 34.996
-    - type: recall_at_5
-      value: 40.294000000000004
-  - task:
-      type: Retrieval
-    dataset:
-      type: BeIR/cqadupstack
-      name: MTEB CQADupstackWordpressRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 20.524
-    - type: map_at_10
-      value: 27.314
-    - type: map_at_100
-      value: 28.260999999999996
-    - type: map_at_1000
-      value: 28.37
-    - type: map_at_3
-      value: 25.020999999999997
-    - type: map_at_5
-      value: 25.942
-    - type: mrr_at_1
-      value: 22.181
-    - type: mrr_at_10
-      value: 29.149
-    - type: mrr_at_100
-      value: 30.006
-    - type: mrr_at_1000
-      value: 30.086000000000002
-    - type: mrr_at_3
-      value: 26.863999999999997
-    - type: mrr_at_5
-      value: 27.899
-    - type: ndcg_at_1
-      value: 22.181
-    - type: ndcg_at_10
-      value: 31.64
-    - type: ndcg_at_100
-      value: 36.502
-    - type: ndcg_at_1000
-      value: 39.176
-    - type: ndcg_at_3
-      value: 26.901999999999997
-    - type: ndcg_at_5
-      value: 28.493000000000002
-    - type: precision_at_1
-      value: 22.181
-    - type: precision_at_10
-      value: 5.065
-    - type: precision_at_100
-      value: 0.8099999999999999
-    - type: precision_at_1000
-      value: 0.11499999999999999
-    - type: precision_at_3
-      value: 11.214
-    - type: precision_at_5
-      value: 7.689
-    - type: recall_at_1
-      value: 20.524
-    - type: recall_at_10
-      value: 43.29
-    - type: recall_at_100
-      value: 65.935
-    - type: recall_at_1000
-      value: 85.80600000000001
-    - type: recall_at_3
-      value: 30.276999999999997
-    - type: recall_at_5
-      value: 34.056999999999995
-  - task:
-      type: Retrieval
-    dataset:
-      type: climate-fever
-      name: MTEB ClimateFEVER
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 10.488999999999999
-    - type: map_at_10
-      value: 17.98
-    - type: map_at_100
-      value: 19.581
-    - type: map_at_1000
-      value: 19.739
-    - type: map_at_3
-      value: 15.054
-    - type: map_at_5
-      value: 16.439999999999998
-    - type: mrr_at_1
-      value: 23.192
-    - type: mrr_at_10
-      value: 33.831
-    - type: mrr_at_100
-      value: 34.833
-    - type: mrr_at_1000
-      value: 34.881
-    - type: mrr_at_3
-      value: 30.793
-    - type: mrr_at_5
-      value: 32.535
-    - type: ndcg_at_1
-      value: 23.192
-    - type: ndcg_at_10
-      value: 25.446
-    - type: ndcg_at_100
-      value: 31.948
-    - type: ndcg_at_1000
-      value: 35.028
-    - type: ndcg_at_3
-      value: 20.744
-    - type: ndcg_at_5
-      value: 22.233
-    - type: precision_at_1
-      value: 23.192
-    - type: precision_at_10
-      value: 8.026
-    - type: precision_at_100
-      value: 1.482
-    - type: precision_at_1000
-      value: 0.20500000000000002
-    - type: precision_at_3
-      value: 15.548
-    - type: precision_at_5
-      value: 11.87
-    - type: recall_at_1
-      value: 10.488999999999999
-    - type: recall_at_10
-      value: 30.865
-    - type: recall_at_100
-      value: 53.428
-    - type: recall_at_1000
-      value: 70.89
-    - type: recall_at_3
-      value: 19.245
-    - type: recall_at_5
-      value: 23.657
-  - task:
-      type: Retrieval
-    dataset:
-      type: dbpedia-entity
-      name: MTEB DBPedia
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 7.123
-    - type: map_at_10
-      value: 14.448
-    - type: map_at_100
-      value: 19.798
-    - type: map_at_1000
-      value: 21.082
-    - type: map_at_3
-      value: 10.815
-    - type: map_at_5
-      value: 12.422
-    - type: mrr_at_1
-      value: 53.5
-    - type: mrr_at_10
-      value: 63.117999999999995
-    - type: mrr_at_100
-      value: 63.617999999999995
-    - type: mrr_at_1000
-      value: 63.63799999999999
-    - type: mrr_at_3
-      value: 60.708
-    - type: mrr_at_5
-      value: 62.171
-    - type: ndcg_at_1
-      value: 42.125
-    - type: ndcg_at_10
-      value: 31.703
-    - type: ndcg_at_100
-      value: 35.935
-    - type: ndcg_at_1000
-      value: 43.173
-    - type: ndcg_at_3
-      value: 35.498000000000005
-    - type: ndcg_at_5
-      value: 33.645
-    - type: precision_at_1
-      value: 53.5
-    - type: precision_at_10
-      value: 25.025
-    - type: precision_at_100
-      value: 8.19
-    - type: precision_at_1000
-      value: 1.806
-    - type: precision_at_3
-      value: 39.083
-    - type: precision_at_5
-      value: 33.050000000000004
-    - type: recall_at_1
-      value: 7.123
-    - type: recall_at_10
-      value: 19.581
-    - type: recall_at_100
-      value: 42.061
-    - type: recall_at_1000
-      value: 65.879
-    - type: recall_at_3
-      value: 12.026
-    - type: recall_at_5
-      value: 14.846
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/emotion
-      name: MTEB EmotionClassification
-      config: default
-      split: test
-      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
-    metrics:
-    - type: accuracy
-      value: 41.24
-    - type: f1
-      value: 36.76174115773002
-  - task:
-      type: Retrieval
-    dataset:
-      type: fever
-      name: MTEB FEVER
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 47.821999999999996
-    - type: map_at_10
-      value: 59.794000000000004
-    - type: map_at_100
-      value: 60.316
-    - type: map_at_1000
-      value: 60.34
-    - type: map_at_3
-      value: 57.202
-    - type: map_at_5
-      value: 58.823
-    - type: mrr_at_1
-      value: 51.485
-    - type: mrr_at_10
-      value: 63.709
-    - type: mrr_at_100
-      value: 64.144
-    - type: mrr_at_1000
-      value: 64.158
-    - type: mrr_at_3
-      value: 61.251
-    - type: mrr_at_5
-      value: 62.818
-    - type: ndcg_at_1
-      value: 51.485
-    - type: ndcg_at_10
-      value: 66.097
-    - type: ndcg_at_100
-      value: 68.37
-    - type: ndcg_at_1000
-      value: 68.916
-    - type: ndcg_at_3
-      value: 61.12800000000001
-    - type: ndcg_at_5
-      value: 63.885000000000005
-    - type: precision_at_1
-      value: 51.485
-    - type: precision_at_10
-      value: 8.956999999999999
-    - type: precision_at_100
-      value: 1.02
-    - type: precision_at_1000
-      value: 0.108
-    - type: precision_at_3
-      value: 24.807000000000002
-    - type: precision_at_5
-      value: 16.387999999999998
-    - type: recall_at_1
-      value: 47.821999999999996
-    - type: recall_at_10
-      value: 81.773
-    - type: recall_at_100
-      value: 91.731
-    - type: recall_at_1000
-      value: 95.649
-    - type: recall_at_3
-      value: 68.349
-    - type: recall_at_5
-      value: 75.093
-  - task:
-      type: Retrieval
-    dataset:
-      type: fiqa
-      name: MTEB FiQA2018
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 15.662999999999998
-    - type: map_at_10
-      value: 25.726
-    - type: map_at_100
-      value: 27.581
-    - type: map_at_1000
-      value: 27.772000000000002
-    - type: map_at_3
-      value: 21.859
-    - type: map_at_5
-      value: 24.058
-    - type: mrr_at_1
-      value: 30.247
-    - type: mrr_at_10
-      value: 39.581
-    - type: mrr_at_100
-      value: 40.594
-    - type: mrr_at_1000
-      value: 40.647
-    - type: mrr_at_3
-      value: 37.166
-    - type: mrr_at_5
-      value: 38.585
-    - type: ndcg_at_1
-      value: 30.247
-    - type: ndcg_at_10
-      value: 32.934999999999995
-    - type: ndcg_at_100
-      value: 40.062999999999995
-    - type: ndcg_at_1000
-      value: 43.492
-    - type: ndcg_at_3
-      value: 28.871000000000002
-    - type: ndcg_at_5
-      value: 30.492
-    - type: precision_at_1
-      value: 30.247
-    - type: precision_at_10
-      value: 9.522
-    - type: precision_at_100
-      value: 1.645
-    - type: precision_at_1000
-      value: 0.22499999999999998
-    - type: precision_at_3
-      value: 19.136
-    - type: precision_at_5
-      value: 14.753
-    - type: recall_at_1
-      value: 15.662999999999998
-    - type: recall_at_10
-      value: 39.595
-    - type: recall_at_100
-      value: 66.49199999999999
-    - type: recall_at_1000
-      value: 87.19
-    - type: recall_at_3
-      value: 26.346999999999998
-    - type: recall_at_5
-      value: 32.423
-  - task:
-      type: Retrieval
-    dataset:
-      type: hotpotqa
-      name: MTEB HotpotQA
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 30.176
-    - type: map_at_10
-      value: 42.684
-    - type: map_at_100
-      value: 43.582
-    - type: map_at_1000
-      value: 43.668
-    - type: map_at_3
-      value: 39.964
-    - type: map_at_5
-      value: 41.589
-    - type: mrr_at_1
-      value: 60.351
-    - type: mrr_at_10
-      value: 67.669
-    - type: mrr_at_100
-      value: 68.089
-    - type: mrr_at_1000
-      value: 68.111
-    - type: mrr_at_3
-      value: 66.144
-    - type: mrr_at_5
-      value: 67.125
-    - type: ndcg_at_1
-      value: 60.351
-    - type: ndcg_at_10
-      value: 51.602000000000004
-    - type: ndcg_at_100
-      value: 55.186
-    - type: ndcg_at_1000
-      value: 56.96
-    - type: ndcg_at_3
-      value: 47.251
-    - type: ndcg_at_5
-      value: 49.584
-    - type: precision_at_1
-      value: 60.351
-    - type: precision_at_10
-      value: 10.804
-    - type: precision_at_100
-      value: 1.3639999999999999
-    - type: precision_at_1000
-      value: 0.16
-    - type: precision_at_3
-      value: 29.561
-    - type: precision_at_5
-      value: 19.581
-    - type: recall_at_1
-      value: 30.176
-    - type: recall_at_10
-      value: 54.018
-    - type: recall_at_100
-      value: 68.22399999999999
-    - type: recall_at_1000
-      value: 79.97999999999999
-    - type: recall_at_3
-      value: 44.342
-    - type: recall_at_5
-      value: 48.953
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/imdb
-      name: MTEB ImdbClassification
-      config: default
-      split: test
-      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
-    metrics:
-    - type: accuracy
-      value: 71.28320000000001
-    - type: ap
-      value: 65.20730065157146
-    - type: f1
-      value: 71.19193683354304
-  - task:
-      type: Retrieval
-    dataset:
-      type: msmarco
-      name: MTEB MSMARCO
-      config: default
-      split: dev
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 19.686
-    - type: map_at_10
-      value: 31.189
-    - type: map_at_100
-      value: 32.368
-    - type: map_at_1000
-      value: 32.43
-    - type: map_at_3
-      value: 27.577
-    - type: map_at_5
-      value: 29.603
-    - type: mrr_at_1
-      value: 20.201
-    - type: mrr_at_10
-      value: 31.762
-    - type: mrr_at_100
-      value: 32.882
-    - type: mrr_at_1000
-      value: 32.937
-    - type: mrr_at_3
-      value: 28.177999999999997
-    - type: mrr_at_5
-      value: 30.212
-    - type: ndcg_at_1
-      value: 20.215
-    - type: ndcg_at_10
-      value: 37.730999999999995
-    - type: ndcg_at_100
-      value: 43.501
-    - type: ndcg_at_1000
-      value: 45.031
-    - type: ndcg_at_3
-      value: 30.336000000000002
-    - type: ndcg_at_5
-      value: 33.961000000000006
-    - type: precision_at_1
-      value: 20.215
-    - type: precision_at_10
-      value: 6.036
-    - type: precision_at_100
-      value: 0.895
-    - type: precision_at_1000
-      value: 0.10300000000000001
-    - type: precision_at_3
-      value: 13.028
-    - type: precision_at_5
-      value: 9.633
-    - type: recall_at_1
-      value: 19.686
-    - type: recall_at_10
-      value: 57.867999999999995
-    - type: recall_at_100
-      value: 84.758
-    - type: recall_at_1000
-      value: 96.44500000000001
-    - type: recall_at_3
-      value: 37.726
-    - type: recall_at_5
-      value: 46.415
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/mtop_domain
-      name: MTEB MTOPDomainClassification (en)
-      config: en
-      split: test
-      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
-    metrics:
-    - type: accuracy
-      value: 89.76972184222525
-    - type: f1
-      value: 89.11949030406099
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/mtop_intent
-      name: MTEB MTOPIntentClassification (en)
-      config: en
-      split: test
-      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
-    metrics:
-    - type: accuracy
-      value: 55.57455540355677
-    - type: f1
-      value: 39.344920096224506
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/amazon_massive_intent
-      name: MTEB MassiveIntentClassification (en)
-      config: en
-      split: test
-      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
-    metrics:
-    - type: accuracy
-      value: 63.772696704774724
-    - type: f1
-      value: 60.70041499812703
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/amazon_massive_scenario
-      name: MTEB MassiveScenarioClassification (en)
-      config: en
-      split: test
-      revision: 7d571f92784cd94a019292a1f45445077d0ef634
-    metrics:
-    - type: accuracy
-      value: 69.16274377942166
-    - type: f1
-      value: 68.06744012208019
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/medrxiv-clustering-p2p
-      name: MTEB MedrxivClusteringP2P
-      config: default
-      split: test
-      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
-    metrics:
-    - type: v_measure
-      value: 31.822626760555522
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/medrxiv-clustering-s2s
-      name: MTEB MedrxivClusteringS2S
-      config: default
-      split: test
-      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
-    metrics:
-    - type: v_measure
-      value: 27.98469036402807
-  - task:
-      type: Reranking
-    dataset:
-      type: mteb/mind_small
-      name: MTEB MindSmallReranking
-      config: default
-      split: test
-      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
-    metrics:
-    - type: map
-      value: 30.911144124209166
-    - type: mrr
-      value: 31.950116175672292
-  - task:
-      type: Retrieval
-    dataset:
-      type: nfcorpus
-      name: MTEB NFCorpus
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 5.157
-    - type: map_at_10
-      value: 11.086
-    - type: map_at_100
-      value: 13.927
-    - type: map_at_1000
-      value: 15.226999999999999
-    - type: map_at_3
-      value: 8.525
-    - type: map_at_5
-      value: 9.767000000000001
-    - type: mrr_at_1
-      value: 43.344
-    - type: mrr_at_10
-      value: 51.646
-    - type: mrr_at_100
-      value: 52.212
-    - type: mrr_at_1000
-      value: 52.263999999999996
-    - type: mrr_at_3
-      value: 50.052
-    - type: mrr_at_5
-      value: 51.166
-    - type: ndcg_at_1
-      value: 41.949999999999996
-    - type: ndcg_at_10
-      value: 30.552
-    - type: ndcg_at_100
-      value: 28.409000000000002
-    - type: ndcg_at_1000
-      value: 37.328
-    - type: ndcg_at_3
-      value: 37.114000000000004
-    - type: ndcg_at_5
-      value: 34.117999999999995
-    - type: precision_at_1
-      value: 43.344
-    - type: precision_at_10
-      value: 22.198
-    - type: precision_at_100
-      value: 7.234999999999999
-    - type: precision_at_1000
-      value: 2.013
-    - type: precision_at_3
-      value: 34.675
-    - type: precision_at_5
-      value: 29.04
-    - type: recall_at_1
-      value: 5.157
-    - type: recall_at_10
-      value: 13.999
-    - type: recall_at_100
-      value: 28.796
-    - type: recall_at_1000
-      value: 60.84
-    - type: recall_at_3
-      value: 9.603
-    - type: recall_at_5
-      value: 11.638
-  - task:
-      type: Retrieval
-    dataset:
-      type: nq
-      name: MTEB NQ
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 33.024
-    - type: map_at_10
-      value: 47.229
-    - type: map_at_100
-      value: 48.195
-    - type: map_at_1000
-      value: 48.229
-    - type: map_at_3
-      value: 43.356
-    - type: map_at_5
-      value: 45.857
-    - type: mrr_at_1
-      value: 36.848
-    - type: mrr_at_10
-      value: 49.801
-    - type: mrr_at_100
-      value: 50.532999999999994
-    - type: mrr_at_1000
-      value: 50.556
-    - type: mrr_at_3
-      value: 46.605999999999995
-    - type: mrr_at_5
-      value: 48.735
-    - type: ndcg_at_1
-      value: 36.848
-    - type: ndcg_at_10
-      value: 54.202
-    - type: ndcg_at_100
-      value: 58.436
-    - type: ndcg_at_1000
-      value: 59.252
-    - type: ndcg_at_3
-      value: 47.082
-    - type: ndcg_at_5
-      value: 51.254
-    - type: precision_at_1
-      value: 36.848
-    - type: precision_at_10
-      value: 8.636000000000001
-    - type: precision_at_100
-      value: 1.105
-    - type: precision_at_1000
-      value: 0.11800000000000001
-    - type: precision_at_3
-      value: 21.08
-    - type: precision_at_5
-      value: 15.07
-    - type: recall_at_1
-      value: 33.024
-    - type: recall_at_10
-      value: 72.699
-    - type: recall_at_100
-      value: 91.387
-    - type: recall_at_1000
-      value: 97.482
-    - type: recall_at_3
-      value: 54.604
-    - type: recall_at_5
-      value: 64.224
-  - task:
-      type: Retrieval
-    dataset:
-      type: quora
-      name: MTEB QuoraRetrieval
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 69.742
-    - type: map_at_10
-      value: 83.43
-    - type: map_at_100
-      value: 84.09400000000001
-    - type: map_at_1000
-      value: 84.113
-    - type: map_at_3
-      value: 80.464
-    - type: map_at_5
-      value: 82.356
-    - type: mrr_at_1
-      value: 80.31
-    - type: mrr_at_10
-      value: 86.629
-    - type: mrr_at_100
-      value: 86.753
-    - type: mrr_at_1000
-      value: 86.75399999999999
-    - type: mrr_at_3
-      value: 85.59
-    - type: mrr_at_5
-      value: 86.346
-    - type: ndcg_at_1
-      value: 80.28999999999999
-    - type: ndcg_at_10
-      value: 87.323
-    - type: ndcg_at_100
-      value: 88.682
-    - type: ndcg_at_1000
-      value: 88.812
-    - type: ndcg_at_3
-      value: 84.373
-    - type: ndcg_at_5
-      value: 86.065
-    - type: precision_at_1
-      value: 80.28999999999999
-    - type: precision_at_10
-      value: 13.239999999999998
-    - type: precision_at_100
-      value: 1.521
-    - type: precision_at_1000
-      value: 0.156
-    - type: precision_at_3
-      value: 36.827
-    - type: precision_at_5
-      value: 24.272
-    - type: recall_at_1
-      value: 69.742
-    - type: recall_at_10
-      value: 94.645
-    - type: recall_at_100
-      value: 99.375
-    - type: recall_at_1000
-      value: 99.97200000000001
-    - type: recall_at_3
-      value: 86.18400000000001
-    - type: recall_at_5
-      value: 90.958
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/reddit-clustering
-      name: MTEB RedditClustering
-      config: default
-      split: test
-      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
-    metrics:
-    - type: v_measure
-      value: 50.52987829115787
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/reddit-clustering-p2p
-      name: MTEB RedditClusteringP2P
-      config: default
-      split: test
-      revision: 282350215ef01743dc01b456c7f5241fa8937f16
-    metrics:
-    - type: v_measure
-      value: 56.73289360025561
-  - task:
-      type: Retrieval
-    dataset:
-      type: scidocs
-      name: MTEB SCIDOCS
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 4.473
-    - type: map_at_10
-      value: 10.953
-    - type: map_at_100
-      value: 12.842
-    - type: map_at_1000
-      value: 13.122
-    - type: map_at_3
-      value: 7.863
-    - type: map_at_5
-      value: 9.376
-    - type: mrr_at_1
-      value: 22.0
-    - type: mrr_at_10
-      value: 32.639
-    - type: mrr_at_100
-      value: 33.658
-    - type: mrr_at_1000
-      value: 33.727000000000004
-    - type: mrr_at_3
-      value: 29.232999999999997
-    - type: mrr_at_5
-      value: 31.373
-    - type: ndcg_at_1
-      value: 22.0
-    - type: ndcg_at_10
-      value: 18.736
-    - type: ndcg_at_100
-      value: 26.209
-    - type: ndcg_at_1000
-      value: 31.427
-    - type: ndcg_at_3
-      value: 17.740000000000002
-    - type: ndcg_at_5
-      value: 15.625
-    - type: precision_at_1
-      value: 22.0
-    - type: precision_at_10
-      value: 9.700000000000001
-    - type: precision_at_100
-      value: 2.052
-    - type: precision_at_1000
-      value: 0.331
-    - type: precision_at_3
-      value: 16.533
-    - type: precision_at_5
-      value: 13.74
-    - type: recall_at_1
-      value: 4.473
-    - type: recall_at_10
-      value: 19.627
-    - type: recall_at_100
-      value: 41.63
-    - type: recall_at_1000
-      value: 67.173
-    - type: recall_at_3
-      value: 10.067
-    - type: recall_at_5
-      value: 13.927
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sickr-sts
-      name: MTEB SICK-R
-      config: default
-      split: test
-      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
-    metrics:
-    - type: cos_sim_pearson
-      value: 83.27314719076216
-    - type: cos_sim_spearman
-      value: 76.39295628838427
-    - type: euclidean_pearson
-      value: 80.38849931283136
-    - type: euclidean_spearman
-      value: 76.39295685543406
-    - type: manhattan_pearson
-      value: 80.28382869912794
-    - type: manhattan_spearman
-      value: 76.28362123227473
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts12-sts
-      name: MTEB STS12
-      config: default
-      split: test
-      revision: a0d554a64d88156834ff5ae9920b964011b16384
-    metrics:
-    - type: cos_sim_pearson
-      value: 82.36858074786585
-    - type: cos_sim_spearman
-      value: 72.81528838052759
-    - type: euclidean_pearson
-      value: 78.83576324502302
-    - type: euclidean_spearman
-      value: 72.8152880167174
-    - type: manhattan_pearson
-      value: 78.81284819385367
-    - type: manhattan_spearman
-      value: 72.76091465928633
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts13-sts
-      name: MTEB STS13
-      config: default
-      split: test
-      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
-    metrics:
-    - type: cos_sim_pearson
-      value: 81.08132718998489
-    - type: cos_sim_spearman
-      value: 82.00988939015869
-    - type: euclidean_pearson
-      value: 81.02243847451692
-    - type: euclidean_spearman
-      value: 82.00992010206836
-    - type: manhattan_pearson
-      value: 80.97749306075134
-    - type: manhattan_spearman
-      value: 81.97800195109437
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts14-sts
-      name: MTEB STS14
-      config: default
-      split: test
-      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
-    metrics:
-    - type: cos_sim_pearson
-      value: 80.83442047735284
-    - type: cos_sim_spearman
-      value: 77.50930325127395
-    - type: euclidean_pearson
-      value: 79.34941050260747
-    - type: euclidean_spearman
-      value: 77.50930324686452
-    - type: manhattan_pearson
-      value: 79.28081079289419
-    - type: manhattan_spearman
-      value: 77.42311420628891
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts15-sts
-      name: MTEB STS15
-      config: default
-      split: test
-      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
-    metrics:
-    - type: cos_sim_pearson
-      value: 85.70132781546333
-    - type: cos_sim_spearman
-      value: 86.58415907086527
-    - type: euclidean_pearson
-      value: 85.63892869817083
-    - type: euclidean_spearman
-      value: 86.58415907086527
-    - type: manhattan_pearson
-      value: 85.56054168116064
-    - type: manhattan_spearman
-      value: 86.50292824173809
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts16-sts
-      name: MTEB STS16
-      config: default
-      split: test
-      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
-    metrics:
-    - type: cos_sim_pearson
-      value: 81.48780971731246
-    - type: cos_sim_spearman
-      value: 82.79818891852887
-    - type: euclidean_pearson
-      value: 81.93990926192305
-    - type: euclidean_spearman
-      value: 82.79818891852887
-    - type: manhattan_pearson
-      value: 81.97538189750966
-    - type: manhattan_spearman
-      value: 82.88761825524075
-  - task:
-      type: STS
-    dataset:
-      type: mteb/sts17-crosslingual-sts
-      name: MTEB STS17 (en-en)
-      config: en-en
-      split: test
-      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
-    metrics:
-    - type: cos_sim_pearson
-      value: 88.4989925729811
-    - type: cos_sim_spearman
-      value: 88.47370962620529
-    - type: euclidean_pearson
-      value: 88.2312980339956
-    - type: euclidean_spearman
-      value: 88.47370962620529
-    - type: manhattan_pearson
-      value: 88.15570940509707
-    - type: manhattan_spearman
-      value: 88.36900000569275
-  - 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: 63.90740805015967
-    - type: cos_sim_spearman
-      value: 63.968359064784444
-    - type: euclidean_pearson
-      value: 64.67928113832794
-    - type: euclidean_spearman
-      value: 63.968359064784444
-    - type: manhattan_pearson
-      value: 63.92597430517486
-    - type: manhattan_spearman
-      value: 63.31372007361158
-  - task:
-      type: STS
-    dataset:
-      type: mteb/stsbenchmark-sts
-      name: MTEB STSBenchmark
-      config: default
-      split: test
-      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
-    metrics:
-    - type: cos_sim_pearson
-      value: 82.56902991447632
-    - type: cos_sim_spearman
-      value: 83.16262853325924
-    - type: euclidean_pearson
-      value: 83.47693312869555
-    - type: euclidean_spearman
-      value: 83.16266829656969
-    - type: manhattan_pearson
-      value: 83.51067558632968
-    - type: manhattan_spearman
-      value: 83.25136388306153
-  - task:
-      type: Reranking
-    dataset:
-      type: mteb/scidocs-reranking
-      name: MTEB SciDocsRR
-      config: default
-      split: test
-      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
-    metrics:
-    - type: map
-      value: 80.1518040851234
-    - type: mrr
-      value: 94.49083052024228
-  - task:
-      type: Retrieval
-    dataset:
-      type: scifact
-      name: MTEB SciFact
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 50.661
-    - type: map_at_10
-      value: 59.816
-    - type: map_at_100
-      value: 60.412
-    - type: map_at_1000
-      value: 60.446999999999996
-    - type: map_at_3
-      value: 56.567
-    - type: map_at_5
-      value: 58.45
-    - type: mrr_at_1
-      value: 53.667
-    - type: mrr_at_10
-      value: 61.342
-    - type: mrr_at_100
-      value: 61.8
-    - type: mrr_at_1000
-      value: 61.836
-    - type: mrr_at_3
-      value: 59.111000000000004
-    - type: mrr_at_5
-      value: 60.411
-    - type: ndcg_at_1
-      value: 53.667
-    - type: ndcg_at_10
-      value: 64.488
-    - type: ndcg_at_100
-      value: 67.291
-    - type: ndcg_at_1000
-      value: 68.338
-    - type: ndcg_at_3
-      value: 59.101000000000006
-    - type: ndcg_at_5
-      value: 61.812999999999995
-    - type: precision_at_1
-      value: 53.667
-    - type: precision_at_10
-      value: 8.799999999999999
-    - type: precision_at_100
-      value: 1.0330000000000001
-    - type: precision_at_1000
-      value: 0.11199999999999999
-    - type: precision_at_3
-      value: 23.0
-    - type: precision_at_5
-      value: 15.6
-    - type: recall_at_1
-      value: 50.661
-    - type: recall_at_10
-      value: 77.422
-    - type: recall_at_100
-      value: 90.667
-    - type: recall_at_1000
-      value: 99.0
-    - type: recall_at_3
-      value: 63.144
-    - type: recall_at_5
-      value: 69.817
-  - task:
-      type: PairClassification
-    dataset:
-      type: mteb/sprintduplicatequestions-pairclassification
-      name: MTEB SprintDuplicateQuestions
-      config: default
-      split: test
-      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
-    metrics:
-    - type: cos_sim_accuracy
-      value: 99.81287128712871
-    - type: cos_sim_ap
-      value: 94.91998708151321
-    - type: cos_sim_f1
-      value: 90.36206017338093
-    - type: cos_sim_precision
-      value: 92.19562955254943
-    - type: cos_sim_recall
-      value: 88.6
-    - type: dot_accuracy
-      value: 99.81287128712871
-    - type: dot_ap
-      value: 94.91998708151321
-    - type: dot_f1
-      value: 90.36206017338093
-    - type: dot_precision
-      value: 92.19562955254943
-    - type: dot_recall
-      value: 88.6
-    - type: euclidean_accuracy
-      value: 99.81287128712871
-    - type: euclidean_ap
-      value: 94.9199944407842
-    - type: euclidean_f1
-      value: 90.36206017338093
-    - type: euclidean_precision
-      value: 92.19562955254943
-    - type: euclidean_recall
-      value: 88.6
-    - type: manhattan_accuracy
-      value: 99.8108910891089
-    - type: manhattan_ap
-      value: 94.83783896670839
-    - type: manhattan_f1
-      value: 90.27989821882952
-    - type: manhattan_precision
-      value: 91.91709844559585
-    - type: manhattan_recall
-      value: 88.7
-    - type: max_accuracy
-      value: 99.81287128712871
-    - type: max_ap
-      value: 94.9199944407842
-    - type: max_f1
-      value: 90.36206017338093
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/stackexchange-clustering
-      name: MTEB StackExchangeClustering
-      config: default
-      split: test
-      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
-    metrics:
-    - type: v_measure
-      value: 56.165546412944714
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/stackexchange-clustering-p2p
-      name: MTEB StackExchangeClusteringP2P
-      config: default
-      split: test
-      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
-    metrics:
-    - type: v_measure
-      value: 34.19894321136813
-  - task:
-      type: Reranking
-    dataset:
-      type: mteb/stackoverflowdupquestions-reranking
-      name: MTEB StackOverflowDupQuestions
-      config: default
-      split: test
-      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
-    metrics:
-    - type: map
-      value: 50.02944308369115
-    - type: mrr
-      value: 50.63055714710127
-  - task:
-      type: Summarization
-    dataset:
-      type: mteb/summeval
-      name: MTEB SummEval
-      config: default
-      split: test
-      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
-    metrics:
-    - type: cos_sim_pearson
-      value: 31.3377433394579
-    - type: cos_sim_spearman
-      value: 30.877807383527983
-    - type: dot_pearson
-      value: 31.337752376327405
-    - type: dot_spearman
-      value: 30.877807383527983
-  - task:
-      type: Retrieval
-    dataset:
-      type: trec-covid
-      name: MTEB TRECCOVID
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 0.20500000000000002
-    - type: map_at_10
-      value: 1.6099999999999999
-    - type: map_at_100
-      value: 8.635
-    - type: map_at_1000
-      value: 20.419999999999998
-    - type: map_at_3
-      value: 0.59
-    - type: map_at_5
-      value: 0.9249999999999999
-    - type: mrr_at_1
-      value: 80.0
-    - type: mrr_at_10
-      value: 88.452
-    - type: mrr_at_100
-      value: 88.452
-    - type: mrr_at_1000
-      value: 88.452
-    - type: mrr_at_3
-      value: 87.667
-    - type: mrr_at_5
-      value: 88.167
-    - type: ndcg_at_1
-      value: 77.0
-    - type: ndcg_at_10
-      value: 67.079
-    - type: ndcg_at_100
-      value: 49.937
-    - type: ndcg_at_1000
-      value: 44.031
-    - type: ndcg_at_3
-      value: 73.123
-    - type: ndcg_at_5
-      value: 70.435
-    - type: precision_at_1
-      value: 80.0
-    - type: precision_at_10
-      value: 70.39999999999999
-    - type: precision_at_100
-      value: 51.25999999999999
-    - type: precision_at_1000
-      value: 19.698
-    - type: precision_at_3
-      value: 78.0
-    - type: precision_at_5
-      value: 75.2
-    - type: recall_at_1
-      value: 0.20500000000000002
-    - type: recall_at_10
-      value: 1.8399999999999999
-    - type: recall_at_100
-      value: 11.971
-    - type: recall_at_1000
-      value: 41.042
-    - type: recall_at_3
-      value: 0.632
-    - type: recall_at_5
-      value: 1.008
-  - task:
-      type: Retrieval
-    dataset:
-      type: webis-touche2020
-      name: MTEB Touche2020
-      config: default
-      split: test
-      revision: None
-    metrics:
-    - type: map_at_1
-      value: 1.183
-    - type: map_at_10
-      value: 9.58
-    - type: map_at_100
-      value: 16.27
-    - type: map_at_1000
-      value: 17.977999999999998
-    - type: map_at_3
-      value: 4.521
-    - type: map_at_5
-      value: 6.567
-    - type: mrr_at_1
-      value: 12.245000000000001
-    - type: mrr_at_10
-      value: 33.486
-    - type: mrr_at_100
-      value: 34.989
-    - type: mrr_at_1000
-      value: 34.989
-    - type: mrr_at_3
-      value: 28.231
-    - type: mrr_at_5
-      value: 31.701
-    - type: ndcg_at_1
-      value: 9.184000000000001
-    - type: ndcg_at_10
-      value: 22.133
-    - type: ndcg_at_100
-      value: 36.882
-    - type: ndcg_at_1000
-      value: 48.487
-    - type: ndcg_at_3
-      value: 18.971
-    - type: ndcg_at_5
-      value: 20.107
-    - type: precision_at_1
-      value: 12.245000000000001
-    - type: precision_at_10
-      value: 21.837
-    - type: precision_at_100
-      value: 8.265
-    - type: precision_at_1000
-      value: 1.606
-    - type: precision_at_3
-      value: 22.448999999999998
-    - type: precision_at_5
-      value: 23.265
-    - type: recall_at_1
-      value: 1.183
-    - type: recall_at_10
-      value: 17.01
-    - type: recall_at_100
-      value: 51.666000000000004
-    - type: recall_at_1000
-      value: 87.56
-    - type: recall_at_3
-      value: 6.0280000000000005
-    - type: recall_at_5
-      value: 9.937999999999999
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/toxic_conversations_50k
-      name: MTEB ToxicConversationsClassification
-      config: default
-      split: test
-      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
-    metrics:
-    - type: accuracy
-      value: 70.6812
-    - type: ap
-      value: 13.776718216594006
-    - type: f1
-      value: 54.14269849375851
-  - task:
-      type: Classification
-    dataset:
-      type: mteb/tweet_sentiment_extraction
-      name: MTEB TweetSentimentExtractionClassification
-      config: default
-      split: test
-      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
-    metrics:
-    - type: accuracy
-      value: 57.3372948500283
-    - type: f1
-      value: 57.39381291375
-  - task:
-      type: Clustering
-    dataset:
-      type: mteb/twentynewsgroups-clustering
-      name: MTEB TwentyNewsgroupsClustering
-      config: default
-      split: test
-      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
-    metrics:
-    - type: v_measure
-      value: 41.49681931876514
-  - task:
-      type: PairClassification
-    dataset:
-      type: mteb/twittersemeval2015-pairclassification
-      name: MTEB TwitterSemEval2015
-      config: default
-      split: test
-      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
-    metrics:
-    - type: cos_sim_accuracy
-      value: 84.65756690707516
-    - type: cos_sim_ap
-      value: 70.06190309300052
-    - type: cos_sim_f1
-      value: 65.49254432311848
-    - type: cos_sim_precision
-      value: 59.00148085466469
-    - type: cos_sim_recall
-      value: 73.58839050131925
-    - type: dot_accuracy
-      value: 84.65756690707516
-    - type: dot_ap
-      value: 70.06187157356817
-    - type: dot_f1
-      value: 65.49254432311848
-    - type: dot_precision
-      value: 59.00148085466469
-    - type: dot_recall
-      value: 73.58839050131925
-    - type: euclidean_accuracy
-      value: 84.65756690707516
-    - type: euclidean_ap
-      value: 70.06190439203068
-    - type: euclidean_f1
-      value: 65.49254432311848
-    - type: euclidean_precision
-      value: 59.00148085466469
-    - type: euclidean_recall
-      value: 73.58839050131925
-    - type: manhattan_accuracy
-      value: 84.58604041246946
-    - type: manhattan_ap
-      value: 69.93103436414437
-    - type: manhattan_f1
-      value: 65.48780487804878
-    - type: manhattan_precision
-      value: 60.8843537414966
-    - type: manhattan_recall
-      value: 70.84432717678101
-    - type: max_accuracy
-      value: 84.65756690707516
-    - type: max_ap
-      value: 70.06190439203068
-    - type: max_f1
-      value: 65.49254432311848
-  - task:
-      type: PairClassification
-    dataset:
-      type: mteb/twitterurlcorpus-pairclassification
-      name: MTEB TwitterURLCorpus
-      config: default
-      split: test
-      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
-    metrics:
-    - type: cos_sim_accuracy
-      value: 88.78410369852912
-    - type: cos_sim_ap
-      value: 85.45825760499459
-    - type: cos_sim_f1
-      value: 77.73455035163849
-    - type: cos_sim_precision
-      value: 75.5966239813737
-    - type: cos_sim_recall
-      value: 79.9969202340622
-    - type: dot_accuracy
-      value: 88.78410369852912
-    - type: dot_ap
-      value: 85.45825790635979
-    - type: dot_f1
-      value: 77.73455035163849
-    - type: dot_precision
-      value: 75.5966239813737
-    - type: dot_recall
-      value: 79.9969202340622
-    - type: euclidean_accuracy
-      value: 88.78410369852912
-    - type: euclidean_ap
-      value: 85.45826341243391
-    - type: euclidean_f1
-      value: 77.73455035163849
-    - type: euclidean_precision
-      value: 75.5966239813737
-    - type: euclidean_recall
-      value: 79.9969202340622
-    - type: manhattan_accuracy
-      value: 88.7026041060271
-    - type: manhattan_ap
-      value: 85.43182830781821
-    - type: manhattan_f1
-      value: 77.61487303506651
-    - type: manhattan_precision
-      value: 76.20955773226477
-    - type: manhattan_recall
-      value: 79.07299045272559
-    - type: max_accuracy
-      value: 88.78410369852912
-    - type: max_ap
-      value: 85.45826341243391
-    - type: max_f1
-      value: 77.73455035163849
----
+  - name: jina-embedding-s-en-v2
+    results: []
+---
+<!-- TODO: add evaluation results here -->
+<br><br>
+
+<p align="center">
+<img src="https://github.com/jina-ai/finetuner/blob/main/docs/_static/finetuner-logo-ani.svg?raw=true" alt="Finetuner logo: Finetuner helps you to create experiments in order to improve embeddings on search tasks. It accompanies you to deliver the last mile of performance-tuning for neural search applications." width="150px">
+</p>
+
+
+<p align="center">
+<b>The text embedding set trained by <a href="https://jina.ai/"><b>Jina AI</b></a>, <a href="https://github.com/jina-ai/finetuner"><b>Finetuner</b></a> team.</b>
+</p>
+
+
+## Intended Usage & Model Info
+
+`jina-embedding-s-en-v2` is an English, monolingual embedding model supporting 8k sequence length.
+It is based on a Bert architecture that supports the symmetric bidirectional variant of ALiBi to support longer sequence length.
+The backbone Jina Bert Small model is pretrained on the C4 dataset.
+The model is further trained on Jina AI's collection of more than 40 datasets of sentence pairs and hard negatives.
+These pairs were obtained from various domains and were carefully selected through a thorough cleaning process.
+
+The embedding model was trained using 512 sequence length, but extrapolates to 8k sequence length thanks to ALiBi.
+This makes our model useful for a range of use cases, especially when processing long documents is needed, including long document retrieval, semantic textual similarity, text reranking, recommendation, RAG and LLM-based generative search,...
+
+This model has 33 million parameters, which enables lightning-fast and memory efficient inference on long documents, while still delivering impressive performance.
+Additionally, we provide the following embedding models, supporting 8k sequence length as well:
+
+- [`jina-embedding-s-en-v2`](https://huggingface.co/jinaai/jina-embedding-s-en-v2): 33 million parameters **(you are here)**.
+- [`jina-embedding-b-en-v2`](https://huggingface.co/jinaai/jina-embedding-b-en-v2): 137 million parameters.
+- [`jina-embedding-l-en-v2`](https://huggingface.co/jinaai/jina-embedding-l-en-v2): 435 million parameters.
+
+## Data & Parameters
+
+Please checkout our [technical blog](https://arxiv.org/abs/2307.11224).
+
+## Metrics
+
+We compared the model against `all-minilm-l6-v2`/`all-mpnet-base-v2` from sbert and `text-embeddings-ada-002` from OpenAI:
+
+<!-- TODO: add evaluation table here -->
+
+## Usage
+
+You can use Jina Embedding models directly from transformers package:
+```python
+!pip install transformers
+from transformers import AutoModel
+from numpy.linalg import norm
+
+cos_sim = lambda a,b: (a @ b.T) / (norm(a)*norm(b))
+model = AutoModel.from_pretrained('jinaai/jina-embedding-s-en-v2', trust_remote_code=True) # trust_remote_code is needed to use the encode method
+embeddings = model.encode(['How is the weather today?', 'What is the current weather like today?'])
+print(cos_sim(embeddings[0], embeddings[1]))
+```
+
+For long sequences, it's recommended to perform inference using Flash Attention. Using Flash Attention allows you to increase the batch size and throughput for long sequence length.
+We include an experimental implementation for Flash Attention, shipped with the model.
+Install the following triton version: 
+`pip install triton==2.0.0.dev20221202`.
+Now run the same code above, but make sure to set the parameter `with_flash` to `True` when you load the model. You also have to use either `fp16` or `bf16`:
+```python
+from transformers import AutoModel
+from numpy.linalg import norm
+import torch
+
+cos_sim = lambda a,b: (a @ b.T) / (norm(a)*norm(b))
+model = AutoModel.from_pretrained('jinaai/jina-embedding-s-en-v2', trust_remote_code=True, with_flash=True, torch_dtype=torch.float16).cuda() # trust_remote_code is needed to use the encode method
+embeddings = model.encode(['How is the weather today?', 'What is the current weather like today?'])
+print(cos_sim(embeddings[0], embeddings[1]))
+```
+
+## Fine-tuning
+
+Please consider [Finetuner](https://github.com/jina-ai/finetuner).
+
+## Plans
+The development of new multilingual models is currently underway. We will be targeting mainly the German and Spanish languages. The upcoming models will be called `jina-embedding-s/b/l-de/es-v2`.
+
+## Contact
+
+Join our [Discord community](https://discord.jina.ai) and chat with other community members about ideas.
+
+## Citation
+
+If you find Jina Embeddings useful in your research, please cite the following paper:
+
+<!-- TODO: update the paper ID once it is published on arxiv -->
+``` latex
+@misc{günther2023jina,
+      title={Beyond the 512-Token Barrier: Training General-Purpose Text
+Embeddings for Large Documents}, 
+      author={Michael Günther and Jackmin Ong and Isabelle Mohr and Alaeddine Abdessalem and Tanguy Abel and Mohammad Kalim Akram and Susana Guzman and Georgios Mastrapas and Saba Sturua and Bo Wang},
+      year={2023},
+      eprint={2307.11224},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```