model

README.md

@@ -1,3 +1,2656 @@
----
-license: apache-2.0
----
+---
+datasets:
+- allenai/c4
+inference: false
+language: en
+license: apache-2.0
+model-index:
+- name: jina-embedding-b-en-v2
+  results:
+  - dataset:
+      config: en
+      name: MTEB AmazonCounterfactualClassification (en)
+      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
+      split: test
+      type: mteb/amazon_counterfactual
+    metrics:
+    - type: accuracy
+      value: 74.73134328358209
+    - type: ap
+      value: 37.765427081831035
+    - type: f1
+      value: 68.79367444339518
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB AmazonPolarityClassification
+      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
+      split: test
+      type: mteb/amazon_polarity
+    metrics:
+    - type: accuracy
+      value: 88.544275
+    - type: ap
+      value: 84.61328675662887
+    - type: f1
+      value: 88.51879035862375
+    task:
+      type: Classification
+  - dataset:
+      config: en
+      name: MTEB AmazonReviewsClassification (en)
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+      split: test
+      type: mteb/amazon_reviews_multi
+    metrics:
+    - type: accuracy
+      value: 45.263999999999996
+    - type: f1
+      value: 43.778759656699435
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB ArguAna
+      revision: None
+      split: test
+      type: arguana
+    metrics:
+    - type: map_at_1
+      value: 21.693
+    - type: map_at_10
+      value: 35.487
+    - type: map_at_100
+      value: 36.862
+    - type: map_at_1000
+      value: 36.872
+    - type: map_at_3
+      value: 30.049999999999997
+    - type: map_at_5
+      value: 32.966
+    - type: mrr_at_1
+      value: 21.977
+    - type: mrr_at_10
+      value: 35.565999999999995
+    - type: mrr_at_100
+      value: 36.948
+    - type: mrr_at_1000
+      value: 36.958
+    - type: mrr_at_3
+      value: 30.121
+    - type: mrr_at_5
+      value: 33.051
+    - type: ndcg_at_1
+      value: 21.693
+    - type: ndcg_at_10
+      value: 44.181
+    - type: ndcg_at_100
+      value: 49.982
+    - type: ndcg_at_1000
+      value: 50.233000000000004
+    - type: ndcg_at_3
+      value: 32.830999999999996
+    - type: ndcg_at_5
+      value: 38.080000000000005
+    - type: precision_at_1
+      value: 21.693
+    - type: precision_at_10
+      value: 7.248
+    - type: precision_at_100
+      value: 0.9769999999999999
+    - type: precision_at_1000
+      value: 0.1
+    - type: precision_at_3
+      value: 13.632
+    - type: precision_at_5
+      value: 10.725
+    - type: recall_at_1
+      value: 21.693
+    - type: recall_at_10
+      value: 72.475
+    - type: recall_at_100
+      value: 97.653
+    - type: recall_at_1000
+      value: 99.57300000000001
+    - type: recall_at_3
+      value: 40.896
+    - type: recall_at_5
+      value: 53.627
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB ArxivClusteringP2P
+      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
+      split: test
+      type: mteb/arxiv-clustering-p2p
+    metrics:
+    - type: v_measure
+      value: 45.39242428696777
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB ArxivClusteringS2S
+      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
+      split: test
+      type: mteb/arxiv-clustering-s2s
+    metrics:
+    - type: v_measure
+      value: 36.675626784714
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB AskUbuntuDupQuestions
+      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
+      split: test
+      type: mteb/askubuntudupquestions-reranking
+    metrics:
+    - type: map
+      value: 62.247725694904034
+    - type: mrr
+      value: 74.91359978894604
+    task:
+      type: Reranking
+  - dataset:
+      config: default
+      name: MTEB BIOSSES
+      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
+      split: test
+      type: mteb/biosses-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 82.68003802970496
+    - type: cos_sim_spearman
+      value: 81.23438110096286
+    - type: euclidean_pearson
+      value: 81.87462986142582
+    - type: euclidean_spearman
+      value: 81.23438110096286
+    - type: manhattan_pearson
+      value: 81.61162566600755
+    - type: manhattan_spearman
+      value: 81.11329400456184
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB Banking77Classification
+      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
+      split: test
+      type: mteb/banking77
+    metrics:
+    - type: accuracy
+      value: 84.01298701298701
+    - type: f1
+      value: 83.31690714969382
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB BiorxivClusteringP2P
+      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
+      split: test
+      type: mteb/biorxiv-clustering-p2p
+    metrics:
+    - type: v_measure
+      value: 37.050108150972086
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB BiorxivClusteringS2S
+      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
+      split: test
+      type: mteb/biorxiv-clustering-s2s
+    metrics:
+    - type: v_measure
+      value: 30.15731442819715
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB CQADupstackAndroidRetrieval
+      revision: None
+      split: test
+      type: BeIR/cqadupstack
+    metrics:
+    - type: map_at_1
+      value: 31.391999999999996
+    - type: map_at_10
+      value: 42.597
+    - type: map_at_100
+      value: 44.07
+    - type: map_at_1000
+      value: 44.198
+    - type: map_at_3
+      value: 38.957
+    - type: map_at_5
+      value: 40.961
+    - type: mrr_at_1
+      value: 37.196
+    - type: mrr_at_10
+      value: 48.152
+    - type: mrr_at_100
+      value: 48.928
+    - type: mrr_at_1000
+      value: 48.964999999999996
+    - type: mrr_at_3
+      value: 45.446
+    - type: mrr_at_5
+      value: 47.205999999999996
+    - type: ndcg_at_1
+      value: 37.196
+    - type: ndcg_at_10
+      value: 49.089
+    - type: ndcg_at_100
+      value: 54.471000000000004
+    - type: ndcg_at_1000
+      value: 56.385
+    - type: ndcg_at_3
+      value: 43.699
+    - type: ndcg_at_5
+      value: 46.22
+    - type: precision_at_1
+      value: 37.196
+    - type: precision_at_10
+      value: 9.313
+    - type: precision_at_100
+      value: 1.478
+    - type: precision_at_1000
+      value: 0.198
+    - type: precision_at_3
+      value: 20.839
+    - type: precision_at_5
+      value: 14.936
+    - type: recall_at_1
+      value: 31.391999999999996
+    - type: recall_at_10
+      value: 61.876
+    - type: recall_at_100
+      value: 84.214
+    - type: recall_at_1000
+      value: 95.985
+    - type: recall_at_3
+      value: 46.6
+    - type: recall_at_5
+      value: 53.588
+    - type: map_at_1
+      value: 29.083
+    - type: map_at_10
+      value: 38.812999999999995
+    - type: map_at_100
+      value: 40.053
+    - type: map_at_1000
+      value: 40.188
+    - type: map_at_3
+      value: 36.111
+    - type: map_at_5
+      value: 37.519000000000005
+    - type: mrr_at_1
+      value: 36.497
+    - type: mrr_at_10
+      value: 44.85
+    - type: mrr_at_100
+      value: 45.546
+    - type: mrr_at_1000
+      value: 45.593
+    - type: mrr_at_3
+      value: 42.686
+    - type: mrr_at_5
+      value: 43.909
+    - type: ndcg_at_1
+      value: 36.497
+    - type: ndcg_at_10
+      value: 44.443
+    - type: ndcg_at_100
+      value: 48.979
+    - type: ndcg_at_1000
+      value: 51.154999999999994
+    - type: ndcg_at_3
+      value: 40.660000000000004
+    - type: ndcg_at_5
+      value: 42.193000000000005
+    - type: precision_at_1
+      value: 36.497
+    - type: precision_at_10
+      value: 8.433
+    - type: precision_at_100
+      value: 1.369
+    - type: precision_at_1000
+      value: 0.185
+    - type: precision_at_3
+      value: 19.894000000000002
+    - type: precision_at_5
+      value: 13.873
+    - type: recall_at_1
+      value: 29.083
+    - type: recall_at_10
+      value: 54.313
+    - type: recall_at_100
+      value: 73.792
+    - type: recall_at_1000
+      value: 87.629
+    - type: recall_at_3
+      value: 42.257
+    - type: recall_at_5
+      value: 47.066
+    - type: map_at_1
+      value: 38.556000000000004
+    - type: map_at_10
+      value: 50.698
+    - type: map_at_100
+      value: 51.705
+    - type: map_at_1000
+      value: 51.768
+    - type: map_at_3
+      value: 47.848
+    - type: map_at_5
+      value: 49.358000000000004
+    - type: mrr_at_1
+      value: 43.95
+    - type: mrr_at_10
+      value: 54.191
+    - type: mrr_at_100
+      value: 54.852999999999994
+    - type: mrr_at_1000
+      value: 54.885
+    - type: mrr_at_3
+      value: 51.954
+    - type: mrr_at_5
+      value: 53.13
+    - type: ndcg_at_1
+      value: 43.95
+    - type: ndcg_at_10
+      value: 56.516
+    - type: ndcg_at_100
+      value: 60.477000000000004
+    - type: ndcg_at_1000
+      value: 61.746
+    - type: ndcg_at_3
+      value: 51.601
+    - type: ndcg_at_5
+      value: 53.795
+    - type: precision_at_1
+      value: 43.95
+    - type: precision_at_10
+      value: 9.009
+    - type: precision_at_100
+      value: 1.189
+    - type: precision_at_1000
+      value: 0.135
+    - type: precision_at_3
+      value: 22.989
+    - type: precision_at_5
+      value: 15.473
+    - type: recall_at_1
+      value: 38.556000000000004
+    - type: recall_at_10
+      value: 70.159
+    - type: recall_at_100
+      value: 87.132
+    - type: recall_at_1000
+      value: 96.16
+    - type: recall_at_3
+      value: 56.906
+    - type: recall_at_5
+      value: 62.332
+    - type: map_at_1
+      value: 24.238
+    - type: map_at_10
+      value: 32.5
+    - type: map_at_100
+      value: 33.637
+    - type: map_at_1000
+      value: 33.719
+    - type: map_at_3
+      value: 30.026999999999997
+    - type: map_at_5
+      value: 31.555
+    - type: mrr_at_1
+      value: 26.328000000000003
+    - type: mrr_at_10
+      value: 34.44
+    - type: mrr_at_100
+      value: 35.455999999999996
+    - type: mrr_at_1000
+      value: 35.521
+    - type: mrr_at_3
+      value: 32.034
+    - type: mrr_at_5
+      value: 33.565
+    - type: ndcg_at_1
+      value: 26.328000000000003
+    - type: ndcg_at_10
+      value: 37.202
+    - type: ndcg_at_100
+      value: 42.728
+    - type: ndcg_at_1000
+      value: 44.792
+    - type: ndcg_at_3
+      value: 32.368
+    - type: ndcg_at_5
+      value: 35.008
+    - type: precision_at_1
+      value: 26.328000000000003
+    - type: precision_at_10
+      value: 5.7059999999999995
+    - type: precision_at_100
+      value: 0.8880000000000001
+    - type: precision_at_1000
+      value: 0.11100000000000002
+    - type: precision_at_3
+      value: 13.672
+    - type: precision_at_5
+      value: 9.74
+    - type: recall_at_1
+      value: 24.238
+    - type: recall_at_10
+      value: 49.829
+    - type: recall_at_100
+      value: 75.21
+    - type: recall_at_1000
+      value: 90.521
+    - type: recall_at_3
+      value: 36.867
+    - type: recall_at_5
+      value: 43.241
+    - type: map_at_1
+      value: 15.378
+    - type: map_at_10
+      value: 22.817999999999998
+    - type: map_at_100
+      value: 23.977999999999998
+    - type: map_at_1000
+      value: 24.108
+    - type: map_at_3
+      value: 20.719
+    - type: map_at_5
+      value: 21.889
+    - type: mrr_at_1
+      value: 19.03
+    - type: mrr_at_10
+      value: 27.022000000000002
+    - type: mrr_at_100
+      value: 28.011999999999997
+    - type: mrr_at_1000
+      value: 28.096
+    - type: mrr_at_3
+      value: 24.855
+    - type: mrr_at_5
+      value: 26.029999999999998
+    - type: ndcg_at_1
+      value: 19.03
+    - type: ndcg_at_10
+      value: 27.526
+    - type: ndcg_at_100
+      value: 33.040000000000006
+    - type: ndcg_at_1000
+      value: 36.187000000000005
+    - type: ndcg_at_3
+      value: 23.497
+    - type: ndcg_at_5
+      value: 25.334
+    - type: precision_at_1
+      value: 19.03
+    - type: precision_at_10
+      value: 4.963
+    - type: precision_at_100
+      value: 0.893
+    - type: precision_at_1000
+      value: 0.13
+    - type: precision_at_3
+      value: 11.360000000000001
+    - type: precision_at_5
+      value: 8.134
+    - type: recall_at_1
+      value: 15.378
+    - type: recall_at_10
+      value: 38.061
+    - type: recall_at_100
+      value: 61.754
+    - type: recall_at_1000
+      value: 84.259
+    - type: recall_at_3
+      value: 26.788
+    - type: recall_at_5
+      value: 31.326999999999998
+    - type: map_at_1
+      value: 27.511999999999997
+    - type: map_at_10
+      value: 37.429
+    - type: map_at_100
+      value: 38.818000000000005
+    - type: map_at_1000
+      value: 38.924
+    - type: map_at_3
+      value: 34.625
+    - type: map_at_5
+      value: 36.064
+    - type: mrr_at_1
+      value: 33.300999999999995
+    - type: mrr_at_10
+      value: 43.036
+    - type: mrr_at_100
+      value: 43.894
+    - type: mrr_at_1000
+      value: 43.936
+    - type: mrr_at_3
+      value: 40.825
+    - type: mrr_at_5
+      value: 42.028
+    - type: ndcg_at_1
+      value: 33.300999999999995
+    - type: ndcg_at_10
+      value: 43.229
+    - type: ndcg_at_100
+      value: 48.992000000000004
+    - type: ndcg_at_1000
+      value: 51.02100000000001
+    - type: ndcg_at_3
+      value: 38.794000000000004
+    - type: ndcg_at_5
+      value: 40.65
+    - type: precision_at_1
+      value: 33.300999999999995
+    - type: precision_at_10
+      value: 7.777000000000001
+    - type: precision_at_100
+      value: 1.269
+    - type: precision_at_1000
+      value: 0.163
+    - type: precision_at_3
+      value: 18.351
+    - type: precision_at_5
+      value: 12.762
+    - type: recall_at_1
+      value: 27.511999999999997
+    - type: recall_at_10
+      value: 54.788000000000004
+    - type: recall_at_100
+      value: 79.105
+    - type: recall_at_1000
+      value: 92.49199999999999
+    - type: recall_at_3
+      value: 41.924
+    - type: recall_at_5
+      value: 47.026
+    - type: map_at_1
+      value: 24.117
+    - type: map_at_10
+      value: 33.32
+    - type: map_at_100
+      value: 34.677
+    - type: map_at_1000
+      value: 34.78
+    - type: map_at_3
+      value: 30.233999999999998
+    - type: map_at_5
+      value: 31.668000000000003
+    - type: mrr_at_1
+      value: 29.566
+    - type: mrr_at_10
+      value: 38.244
+    - type: mrr_at_100
+      value: 39.245000000000005
+    - type: mrr_at_1000
+      value: 39.296
+    - type: mrr_at_3
+      value: 35.864000000000004
+    - type: mrr_at_5
+      value: 36.919999999999995
+    - type: ndcg_at_1
+      value: 29.566
+    - type: ndcg_at_10
+      value: 39.127
+    - type: ndcg_at_100
+      value: 44.989000000000004
+    - type: ndcg_at_1000
+      value: 47.189
+    - type: ndcg_at_3
+      value: 34.039
+    - type: ndcg_at_5
+      value: 35.744
+    - type: precision_at_1
+      value: 29.566
+    - type: precision_at_10
+      value: 7.385999999999999
+    - type: precision_at_100
+      value: 1.204
+    - type: precision_at_1000
+      value: 0.158
+    - type: precision_at_3
+      value: 16.286
+    - type: precision_at_5
+      value: 11.484
+    - type: recall_at_1
+      value: 24.117
+    - type: recall_at_10
+      value: 51.559999999999995
+    - type: recall_at_100
+      value: 77.104
+    - type: recall_at_1000
+      value: 91.79899999999999
+    - type: recall_at_3
+      value: 36.82
+    - type: recall_at_5
+      value: 41.453
+    - type: map_at_1
+      value: 25.17625
+    - type: map_at_10
+      value: 34.063916666666664
+    - type: map_at_100
+      value: 35.255500000000005
+    - type: map_at_1000
+      value: 35.37275
+    - type: map_at_3
+      value: 31.351666666666667
+    - type: map_at_5
+      value: 32.80608333333333
+    - type: mrr_at_1
+      value: 29.59783333333333
+    - type: mrr_at_10
+      value: 38.0925
+    - type: mrr_at_100
+      value: 38.957249999999995
+    - type: mrr_at_1000
+      value: 39.01608333333333
+    - type: mrr_at_3
+      value: 35.77625
+    - type: mrr_at_5
+      value: 37.04991666666667
+    - type: ndcg_at_1
+      value: 29.59783333333333
+    - type: ndcg_at_10
+      value: 39.343666666666664
+    - type: ndcg_at_100
+      value: 44.488249999999994
+    - type: ndcg_at_1000
+      value: 46.83358333333334
+    - type: ndcg_at_3
+      value: 34.69708333333333
+    - type: ndcg_at_5
+      value: 36.75075
+    - type: precision_at_1
+      value: 29.59783333333333
+    - type: precision_at_10
+      value: 6.884083333333332
+    - type: precision_at_100
+      value: 1.114
+    - type: precision_at_1000
+      value: 0.15108333333333332
+    - type: precision_at_3
+      value: 15.965250000000003
+    - type: precision_at_5
+      value: 11.246500000000001
+    - type: recall_at_1
+      value: 25.17625
+    - type: recall_at_10
+      value: 51.015999999999984
+    - type: recall_at_100
+      value: 73.60174999999998
+    - type: recall_at_1000
+      value: 89.849
+    - type: recall_at_3
+      value: 37.88399999999999
+    - type: recall_at_5
+      value: 43.24541666666666
+    - type: map_at_1
+      value: 24.537
+    - type: map_at_10
+      value: 31.081999999999997
+    - type: map_at_100
+      value: 32.042
+    - type: map_at_1000
+      value: 32.141
+    - type: map_at_3
+      value: 29.137
+    - type: map_at_5
+      value: 30.079
+    - type: mrr_at_1
+      value: 27.454
+    - type: mrr_at_10
+      value: 33.694
+    - type: mrr_at_100
+      value: 34.579
+    - type: mrr_at_1000
+      value: 34.649
+    - type: mrr_at_3
+      value: 32.004
+    - type: mrr_at_5
+      value: 32.794000000000004
+    - type: ndcg_at_1
+      value: 27.454
+    - type: ndcg_at_10
+      value: 34.915
+    - type: ndcg_at_100
+      value: 39.641
+    - type: ndcg_at_1000
+      value: 42.105
+    - type: ndcg_at_3
+      value: 31.276
+    - type: ndcg_at_5
+      value: 32.65
+    - type: precision_at_1
+      value: 27.454
+    - type: precision_at_10
+      value: 5.337
+    - type: precision_at_100
+      value: 0.8250000000000001
+    - type: precision_at_1000
+      value: 0.11199999999999999
+    - type: precision_at_3
+      value: 13.241
+    - type: precision_at_5
+      value: 8.895999999999999
+    - type: recall_at_1
+      value: 24.537
+    - type: recall_at_10
+      value: 44.324999999999996
+    - type: recall_at_100
+      value: 65.949
+    - type: recall_at_1000
+      value: 84.017
+    - type: recall_at_3
+      value: 33.857
+    - type: recall_at_5
+      value: 37.316
+    - type: map_at_1
+      value: 17.122
+    - type: map_at_10
+      value: 24.32
+    - type: map_at_100
+      value: 25.338
+    - type: map_at_1000
+      value: 25.462
+    - type: map_at_3
+      value: 22.064
+    - type: map_at_5
+      value: 23.322000000000003
+    - type: mrr_at_1
+      value: 20.647
+    - type: mrr_at_10
+      value: 27.858
+    - type: mrr_at_100
+      value: 28.743999999999996
+    - type: mrr_at_1000
+      value: 28.819
+    - type: mrr_at_3
+      value: 25.769
+    - type: mrr_at_5
+      value: 26.964
+    - type: ndcg_at_1
+      value: 20.647
+    - type: ndcg_at_10
+      value: 28.849999999999998
+    - type: ndcg_at_100
+      value: 33.849000000000004
+    - type: ndcg_at_1000
+      value: 36.802
+    - type: ndcg_at_3
+      value: 24.799
+    - type: ndcg_at_5
+      value: 26.682
+    - type: precision_at_1
+      value: 20.647
+    - type: precision_at_10
+      value: 5.2170000000000005
+    - type: precision_at_100
+      value: 0.906
+    - type: precision_at_1000
+      value: 0.134
+    - type: precision_at_3
+      value: 11.769
+    - type: precision_at_5
+      value: 8.486
+    - type: recall_at_1
+      value: 17.122
+    - type: recall_at_10
+      value: 38.999
+    - type: recall_at_100
+      value: 61.467000000000006
+    - type: recall_at_1000
+      value: 82.716
+    - type: recall_at_3
+      value: 27.601
+    - type: recall_at_5
+      value: 32.471
+    - type: map_at_1
+      value: 24.396
+    - type: map_at_10
+      value: 33.415
+    - type: map_at_100
+      value: 34.521
+    - type: map_at_1000
+      value: 34.631
+    - type: map_at_3
+      value: 30.703999999999997
+    - type: map_at_5
+      value: 32.166
+    - type: mrr_at_1
+      value: 28.825
+    - type: mrr_at_10
+      value: 37.397000000000006
+    - type: mrr_at_100
+      value: 38.286
+    - type: mrr_at_1000
+      value: 38.346000000000004
+    - type: mrr_at_3
+      value: 35.028
+    - type: mrr_at_5
+      value: 36.32
+    - type: ndcg_at_1
+      value: 28.825
+    - type: ndcg_at_10
+      value: 38.656
+    - type: ndcg_at_100
+      value: 43.856
+    - type: ndcg_at_1000
+      value: 46.31
+    - type: ndcg_at_3
+      value: 33.793
+    - type: ndcg_at_5
+      value: 35.909
+    - type: precision_at_1
+      value: 28.825
+    - type: precision_at_10
+      value: 6.567
+    - type: precision_at_100
+      value: 1.0330000000000001
+    - type: precision_at_1000
+      value: 0.135
+    - type: precision_at_3
+      value: 15.516
+    - type: precision_at_5
+      value: 10.914
+    - type: recall_at_1
+      value: 24.396
+    - type: recall_at_10
+      value: 50.747
+    - type: recall_at_100
+      value: 73.477
+    - type: recall_at_1000
+      value: 90.801
+    - type: recall_at_3
+      value: 37.1
+    - type: recall_at_5
+      value: 42.589
+    - type: map_at_1
+      value: 25.072
+    - type: map_at_10
+      value: 34.307
+    - type: map_at_100
+      value: 35.725
+    - type: map_at_1000
+      value: 35.943999999999996
+    - type: map_at_3
+      value: 30.906
+    - type: map_at_5
+      value: 32.818000000000005
+    - type: mrr_at_1
+      value: 29.644
+    - type: mrr_at_10
+      value: 38.673
+    - type: mrr_at_100
+      value: 39.459
+    - type: mrr_at_1000
+      value: 39.527
+    - type: mrr_at_3
+      value: 35.771
+    - type: mrr_at_5
+      value: 37.332
+    - type: ndcg_at_1
+      value: 29.644
+    - type: ndcg_at_10
+      value: 40.548
+    - type: ndcg_at_100
+      value: 45.678999999999995
+    - type: ndcg_at_1000
+      value: 48.488
+    - type: ndcg_at_3
+      value: 34.887
+    - type: ndcg_at_5
+      value: 37.543
+    - type: precision_at_1
+      value: 29.644
+    - type: precision_at_10
+      value: 7.688000000000001
+    - type: precision_at_100
+      value: 1.482
+    - type: precision_at_1000
+      value: 0.23600000000000002
+    - type: precision_at_3
+      value: 16.206
+    - type: precision_at_5
+      value: 12.016
+    - type: recall_at_1
+      value: 25.072
+    - type: recall_at_10
+      value: 53.478
+    - type: recall_at_100
+      value: 76.07300000000001
+    - type: recall_at_1000
+      value: 93.884
+    - type: recall_at_3
+      value: 37.583
+    - type: recall_at_5
+      value: 44.464
+    - type: map_at_1
+      value: 20.712
+    - type: map_at_10
+      value: 27.467999999999996
+    - type: map_at_100
+      value: 28.502
+    - type: map_at_1000
+      value: 28.610000000000003
+    - type: map_at_3
+      value: 24.887999999999998
+    - type: map_at_5
+      value: 26.273999999999997
+    - type: mrr_at_1
+      value: 22.736
+    - type: mrr_at_10
+      value: 29.553
+    - type: mrr_at_100
+      value: 30.485
+    - type: mrr_at_1000
+      value: 30.56
+    - type: mrr_at_3
+      value: 27.078999999999997
+    - type: mrr_at_5
+      value: 28.401
+    - type: ndcg_at_1
+      value: 22.736
+    - type: ndcg_at_10
+      value: 32.023
+    - type: ndcg_at_100
+      value: 37.158
+    - type: ndcg_at_1000
+      value: 39.823
+    - type: ndcg_at_3
+      value: 26.951999999999998
+    - type: ndcg_at_5
+      value: 29.281000000000002
+    - type: precision_at_1
+      value: 22.736
+    - type: precision_at_10
+      value: 5.213
+    - type: precision_at_100
+      value: 0.832
+    - type: precision_at_1000
+      value: 0.116
+    - type: precision_at_3
+      value: 11.459999999999999
+    - type: precision_at_5
+      value: 8.244
+    - type: recall_at_1
+      value: 20.712
+    - type: recall_at_10
+      value: 44.057
+    - type: recall_at_100
+      value: 67.944
+    - type: recall_at_1000
+      value: 87.925
+    - type: recall_at_3
+      value: 30.305
+    - type: recall_at_5
+      value: 36.071999999999996
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB ClimateFEVER
+      revision: None
+      split: test
+      type: climate-fever
+    metrics:
+    - type: map_at_1
+      value: 10.181999999999999
+    - type: map_at_10
+      value: 16.66
+    - type: map_at_100
+      value: 18.273
+    - type: map_at_1000
+      value: 18.45
+    - type: map_at_3
+      value: 14.141
+    - type: map_at_5
+      value: 15.455
+    - type: mrr_at_1
+      value: 22.15
+    - type: mrr_at_10
+      value: 32.062000000000005
+    - type: mrr_at_100
+      value: 33.116
+    - type: mrr_at_1000
+      value: 33.168
+    - type: mrr_at_3
+      value: 28.827
+    - type: mrr_at_5
+      value: 30.892999999999997
+    - type: ndcg_at_1
+      value: 22.15
+    - type: ndcg_at_10
+      value: 23.532
+    - type: ndcg_at_100
+      value: 30.358
+    - type: ndcg_at_1000
+      value: 33.783
+    - type: ndcg_at_3
+      value: 19.222
+    - type: ndcg_at_5
+      value: 20.919999999999998
+    - type: precision_at_1
+      value: 22.15
+    - type: precision_at_10
+      value: 7.185999999999999
+    - type: precision_at_100
+      value: 1.433
+    - type: precision_at_1000
+      value: 0.207
+    - type: precision_at_3
+      value: 13.941
+    - type: precision_at_5
+      value: 10.906
+    - type: recall_at_1
+      value: 10.181999999999999
+    - type: recall_at_10
+      value: 28.104000000000003
+    - type: recall_at_100
+      value: 51.998999999999995
+    - type: recall_at_1000
+      value: 71.311
+    - type: recall_at_3
+      value: 17.698
+    - type: recall_at_5
+      value: 22.262999999999998
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB DBPedia
+      revision: None
+      split: test
+      type: dbpedia-entity
+    metrics:
+    - type: map_at_1
+      value: 6.669
+    - type: map_at_10
+      value: 15.552
+    - type: map_at_100
+      value: 21.865000000000002
+    - type: map_at_1000
+      value: 23.268
+    - type: map_at_3
+      value: 11.309
+    - type: map_at_5
+      value: 13.084000000000001
+    - type: mrr_at_1
+      value: 55.50000000000001
+    - type: mrr_at_10
+      value: 66.46600000000001
+    - type: mrr_at_100
+      value: 66.944
+    - type: mrr_at_1000
+      value: 66.956
+    - type: mrr_at_3
+      value: 64.542
+    - type: mrr_at_5
+      value: 65.717
+    - type: ndcg_at_1
+      value: 44.75
+    - type: ndcg_at_10
+      value: 35.049
+    - type: ndcg_at_100
+      value: 39.073
+    - type: ndcg_at_1000
+      value: 46.208
+    - type: ndcg_at_3
+      value: 39.525
+    - type: ndcg_at_5
+      value: 37.156
+    - type: precision_at_1
+      value: 55.50000000000001
+    - type: precision_at_10
+      value: 27.800000000000004
+    - type: precision_at_100
+      value: 9.013
+    - type: precision_at_1000
+      value: 1.8800000000000001
+    - type: precision_at_3
+      value: 42.667
+    - type: precision_at_5
+      value: 36.0
+    - type: recall_at_1
+      value: 6.669
+    - type: recall_at_10
+      value: 21.811
+    - type: recall_at_100
+      value: 45.112
+    - type: recall_at_1000
+      value: 67.806
+    - type: recall_at_3
+      value: 13.373
+    - type: recall_at_5
+      value: 16.615
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB EmotionClassification
+      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
+      split: test
+      type: mteb/emotion
+    metrics:
+    - type: accuracy
+      value: 48.769999999999996
+    - type: f1
+      value: 42.91448356376592
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB FEVER
+      revision: None
+      split: test
+      type: fever
+    metrics:
+    - type: map_at_1
+      value: 54.013
+    - type: map_at_10
+      value: 66.239
+    - type: map_at_100
+      value: 66.62599999999999
+    - type: map_at_1000
+      value: 66.644
+    - type: map_at_3
+      value: 63.965
+    - type: map_at_5
+      value: 65.45400000000001
+    - type: mrr_at_1
+      value: 58.221000000000004
+    - type: mrr_at_10
+      value: 70.43700000000001
+    - type: mrr_at_100
+      value: 70.744
+    - type: mrr_at_1000
+      value: 70.75099999999999
+    - type: mrr_at_3
+      value: 68.284
+    - type: mrr_at_5
+      value: 69.721
+    - type: ndcg_at_1
+      value: 58.221000000000004
+    - type: ndcg_at_10
+      value: 72.327
+    - type: ndcg_at_100
+      value: 73.953
+    - type: ndcg_at_1000
+      value: 74.312
+    - type: ndcg_at_3
+      value: 68.062
+    - type: ndcg_at_5
+      value: 70.56400000000001
+    - type: precision_at_1
+      value: 58.221000000000004
+    - type: precision_at_10
+      value: 9.521
+    - type: precision_at_100
+      value: 1.045
+    - type: precision_at_1000
+      value: 0.109
+    - type: precision_at_3
+      value: 27.348
+    - type: precision_at_5
+      value: 17.794999999999998
+    - type: recall_at_1
+      value: 54.013
+    - type: recall_at_10
+      value: 86.957
+    - type: recall_at_100
+      value: 93.911
+    - type: recall_at_1000
+      value: 96.38
+    - type: recall_at_3
+      value: 75.555
+    - type: recall_at_5
+      value: 81.671
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB FiQA2018
+      revision: None
+      split: test
+      type: fiqa
+    metrics:
+    - type: map_at_1
+      value: 21.254
+    - type: map_at_10
+      value: 33.723
+    - type: map_at_100
+      value: 35.574
+    - type: map_at_1000
+      value: 35.730000000000004
+    - type: map_at_3
+      value: 29.473
+    - type: map_at_5
+      value: 31.543
+    - type: mrr_at_1
+      value: 41.358
+    - type: mrr_at_10
+      value: 49.498
+    - type: mrr_at_100
+      value: 50.275999999999996
+    - type: mrr_at_1000
+      value: 50.308
+    - type: mrr_at_3
+      value: 47.016000000000005
+    - type: mrr_at_5
+      value: 48.336
+    - type: ndcg_at_1
+      value: 41.358
+    - type: ndcg_at_10
+      value: 41.579
+    - type: ndcg_at_100
+      value: 48.455
+    - type: ndcg_at_1000
+      value: 51.165000000000006
+    - type: ndcg_at_3
+      value: 37.681
+    - type: ndcg_at_5
+      value: 38.49
+    - type: precision_at_1
+      value: 41.358
+    - type: precision_at_10
+      value: 11.543000000000001
+    - type: precision_at_100
+      value: 1.87
+    - type: precision_at_1000
+      value: 0.23600000000000002
+    - type: precision_at_3
+      value: 24.743000000000002
+    - type: precision_at_5
+      value: 17.994
+    - type: recall_at_1
+      value: 21.254
+    - type: recall_at_10
+      value: 48.698
+    - type: recall_at_100
+      value: 74.588
+    - type: recall_at_1000
+      value: 91.00200000000001
+    - type: recall_at_3
+      value: 33.939
+    - type: recall_at_5
+      value: 39.367000000000004
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB HotpotQA
+      revision: None
+      split: test
+      type: hotpotqa
+    metrics:
+    - type: map_at_1
+      value: 35.922
+    - type: map_at_10
+      value: 52.32599999999999
+    - type: map_at_100
+      value: 53.18000000000001
+    - type: map_at_1000
+      value: 53.245
+    - type: map_at_3
+      value: 49.294
+    - type: map_at_5
+      value: 51.202999999999996
+    - type: mrr_at_1
+      value: 71.843
+    - type: mrr_at_10
+      value: 78.24600000000001
+    - type: mrr_at_100
+      value: 78.515
+    - type: mrr_at_1000
+      value: 78.527
+    - type: mrr_at_3
+      value: 77.17500000000001
+    - type: mrr_at_5
+      value: 77.852
+    - type: ndcg_at_1
+      value: 71.843
+    - type: ndcg_at_10
+      value: 61.379
+    - type: ndcg_at_100
+      value: 64.535
+    - type: ndcg_at_1000
+      value: 65.888
+    - type: ndcg_at_3
+      value: 56.958
+    - type: ndcg_at_5
+      value: 59.434
+    - type: precision_at_1
+      value: 71.843
+    - type: precision_at_10
+      value: 12.686
+    - type: precision_at_100
+      value: 1.517
+    - type: precision_at_1000
+      value: 0.16999999999999998
+    - type: precision_at_3
+      value: 35.778
+    - type: precision_at_5
+      value: 23.422
+    - type: recall_at_1
+      value: 35.922
+    - type: recall_at_10
+      value: 63.43
+    - type: recall_at_100
+      value: 75.868
+    - type: recall_at_1000
+      value: 84.88900000000001
+    - type: recall_at_3
+      value: 53.666000000000004
+    - type: recall_at_5
+      value: 58.555
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB ImdbClassification
+      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
+      split: test
+      type: mteb/imdb
+    metrics:
+    - type: accuracy
+      value: 79.4408
+    - type: ap
+      value: 73.52820871620366
+    - type: f1
+      value: 79.36240238685001
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB MSMARCO
+      revision: None
+      split: dev
+      type: msmarco
+    metrics:
+    - type: map_at_1
+      value: 21.826999999999998
+    - type: map_at_10
+      value: 34.04
+    - type: map_at_100
+      value: 35.226
+    - type: map_at_1000
+      value: 35.275
+    - type: map_at_3
+      value: 30.165999999999997
+    - type: map_at_5
+      value: 32.318000000000005
+    - type: mrr_at_1
+      value: 22.464000000000002
+    - type: mrr_at_10
+      value: 34.631
+    - type: mrr_at_100
+      value: 35.752
+    - type: mrr_at_1000
+      value: 35.795
+    - type: mrr_at_3
+      value: 30.798
+    - type: mrr_at_5
+      value: 32.946999999999996
+    - type: ndcg_at_1
+      value: 22.464000000000002
+    - type: ndcg_at_10
+      value: 40.919
+    - type: ndcg_at_100
+      value: 46.632
+    - type: ndcg_at_1000
+      value: 47.833
+    - type: ndcg_at_3
+      value: 32.992
+    - type: ndcg_at_5
+      value: 36.834
+    - type: precision_at_1
+      value: 22.464000000000002
+    - type: precision_at_10
+      value: 6.494
+    - type: precision_at_100
+      value: 0.9369999999999999
+    - type: precision_at_1000
+      value: 0.104
+    - type: precision_at_3
+      value: 14.021
+    - type: precision_at_5
+      value: 10.347000000000001
+    - type: recall_at_1
+      value: 21.826999999999998
+    - type: recall_at_10
+      value: 62.132
+    - type: recall_at_100
+      value: 88.55199999999999
+    - type: recall_at_1000
+      value: 97.707
+    - type: recall_at_3
+      value: 40.541
+    - type: recall_at_5
+      value: 49.739
+    task:
+      type: Retrieval
+  - dataset:
+      config: en
+      name: MTEB MTOPDomainClassification (en)
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+      split: test
+      type: mteb/mtop_domain
+    metrics:
+    - type: accuracy
+      value: 95.68399452804377
+    - type: f1
+      value: 95.25490609832268
+    task:
+      type: Classification
+  - dataset:
+      config: en
+      name: MTEB MTOPIntentClassification (en)
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+      split: test
+      type: mteb/mtop_intent
+    metrics:
+    - type: accuracy
+      value: 83.15321477428182
+    - type: f1
+      value: 60.35476439087966
+    task:
+      type: Classification
+  - dataset:
+      config: en
+      name: MTEB MassiveIntentClassification (en)
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+      split: test
+      type: mteb/amazon_massive_intent
+    metrics:
+    - type: accuracy
+      value: 71.92669804976462
+    - type: f1
+      value: 69.22815107207565
+    task:
+      type: Classification
+  - dataset:
+      config: en
+      name: MTEB MassiveScenarioClassification (en)
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+      split: test
+      type: mteb/amazon_massive_scenario
+    metrics:
+    - type: accuracy
+      value: 74.4855413584398
+    - type: f1
+      value: 72.92107516103387
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB MedrxivClusteringP2P
+      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
+      split: test
+      type: mteb/medrxiv-clustering-p2p
+    metrics:
+    - type: v_measure
+      value: 32.412679360205544
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB MedrxivClusteringS2S
+      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
+      split: test
+      type: mteb/medrxiv-clustering-s2s
+    metrics:
+    - type: v_measure
+      value: 28.09211869875204
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB MindSmallReranking
+      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
+      split: test
+      type: mteb/mind_small
+    metrics:
+    - type: map
+      value: 30.540919056982545
+    - type: mrr
+      value: 31.529904607063536
+    task:
+      type: Reranking
+  - dataset:
+      config: default
+      name: MTEB NFCorpus
+      revision: None
+      split: test
+      type: nfcorpus
+    metrics:
+    - type: map_at_1
+      value: 5.745
+    - type: map_at_10
+      value: 12.013
+    - type: map_at_100
+      value: 15.040000000000001
+    - type: map_at_1000
+      value: 16.427
+    - type: map_at_3
+      value: 8.841000000000001
+    - type: map_at_5
+      value: 10.289
+    - type: mrr_at_1
+      value: 45.201
+    - type: mrr_at_10
+      value: 53.483999999999995
+    - type: mrr_at_100
+      value: 54.20700000000001
+    - type: mrr_at_1000
+      value: 54.252
+    - type: mrr_at_3
+      value: 51.29
+    - type: mrr_at_5
+      value: 52.73
+    - type: ndcg_at_1
+      value: 43.808
+    - type: ndcg_at_10
+      value: 32.445
+    - type: ndcg_at_100
+      value: 30.031000000000002
+    - type: ndcg_at_1000
+      value: 39.007
+    - type: ndcg_at_3
+      value: 37.204
+    - type: ndcg_at_5
+      value: 35.07
+    - type: precision_at_1
+      value: 45.201
+    - type: precision_at_10
+      value: 23.684
+    - type: precision_at_100
+      value: 7.600999999999999
+    - type: precision_at_1000
+      value: 2.043
+    - type: precision_at_3
+      value: 33.953
+    - type: precision_at_5
+      value: 29.412
+    - type: recall_at_1
+      value: 5.745
+    - type: recall_at_10
+      value: 16.168
+    - type: recall_at_100
+      value: 30.875999999999998
+    - type: recall_at_1000
+      value: 62.686
+    - type: recall_at_3
+      value: 9.75
+    - type: recall_at_5
+      value: 12.413
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB NQ
+      revision: None
+      split: test
+      type: nq
+    metrics:
+    - type: map_at_1
+      value: 37.828
+    - type: map_at_10
+      value: 53.239000000000004
+    - type: map_at_100
+      value: 54.035999999999994
+    - type: map_at_1000
+      value: 54.067
+    - type: map_at_3
+      value: 49.289
+    - type: map_at_5
+      value: 51.784
+    - type: mrr_at_1
+      value: 42.497
+    - type: mrr_at_10
+      value: 55.916999999999994
+    - type: mrr_at_100
+      value: 56.495
+    - type: mrr_at_1000
+      value: 56.516999999999996
+    - type: mrr_at_3
+      value: 52.800000000000004
+    - type: mrr_at_5
+      value: 54.722
+    - type: ndcg_at_1
+      value: 42.468
+    - type: ndcg_at_10
+      value: 60.437
+    - type: ndcg_at_100
+      value: 63.731
+    - type: ndcg_at_1000
+      value: 64.41799999999999
+    - type: ndcg_at_3
+      value: 53.230999999999995
+    - type: ndcg_at_5
+      value: 57.26
+    - type: precision_at_1
+      value: 42.468
+    - type: precision_at_10
+      value: 9.47
+    - type: precision_at_100
+      value: 1.1360000000000001
+    - type: precision_at_1000
+      value: 0.12
+    - type: precision_at_3
+      value: 23.724999999999998
+    - type: precision_at_5
+      value: 16.593
+    - type: recall_at_1
+      value: 37.828
+    - type: recall_at_10
+      value: 79.538
+    - type: recall_at_100
+      value: 93.646
+    - type: recall_at_1000
+      value: 98.72999999999999
+    - type: recall_at_3
+      value: 61.134
+    - type: recall_at_5
+      value: 70.377
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB QuoraRetrieval
+      revision: None
+      split: test
+      type: quora
+    metrics:
+    - type: map_at_1
+      value: 70.548
+    - type: map_at_10
+      value: 84.466
+    - type: map_at_100
+      value: 85.10600000000001
+    - type: map_at_1000
+      value: 85.123
+    - type: map_at_3
+      value: 81.57600000000001
+    - type: map_at_5
+      value: 83.399
+    - type: mrr_at_1
+      value: 81.24
+    - type: mrr_at_10
+      value: 87.457
+    - type: mrr_at_100
+      value: 87.574
+    - type: mrr_at_1000
+      value: 87.575
+    - type: mrr_at_3
+      value: 86.507
+    - type: mrr_at_5
+      value: 87.205
+    - type: ndcg_at_1
+      value: 81.25
+    - type: ndcg_at_10
+      value: 88.203
+    - type: ndcg_at_100
+      value: 89.457
+    - type: ndcg_at_1000
+      value: 89.563
+    - type: ndcg_at_3
+      value: 85.465
+    - type: ndcg_at_5
+      value: 87.007
+    - type: precision_at_1
+      value: 81.25
+    - type: precision_at_10
+      value: 13.373
+    - type: precision_at_100
+      value: 1.5270000000000001
+    - type: precision_at_1000
+      value: 0.157
+    - type: precision_at_3
+      value: 37.417
+    - type: precision_at_5
+      value: 24.556
+    - type: recall_at_1
+      value: 70.548
+    - type: recall_at_10
+      value: 95.208
+    - type: recall_at_100
+      value: 99.514
+    - type: recall_at_1000
+      value: 99.988
+    - type: recall_at_3
+      value: 87.214
+    - type: recall_at_5
+      value: 91.696
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB RedditClustering
+      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
+      split: test
+      type: mteb/reddit-clustering
+    metrics:
+    - type: v_measure
+      value: 53.04822095496839
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB RedditClusteringP2P
+      revision: 282350215ef01743dc01b456c7f5241fa8937f16
+      split: test
+      type: mteb/reddit-clustering-p2p
+    metrics:
+    - type: v_measure
+      value: 60.30778476474675
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB SCIDOCS
+      revision: None
+      split: test
+      type: scidocs
+    metrics:
+    - type: map_at_1
+      value: 4.692
+    - type: map_at_10
+      value: 11.766
+    - type: map_at_100
+      value: 13.904
+    - type: map_at_1000
+      value: 14.216999999999999
+    - type: map_at_3
+      value: 8.245
+    - type: map_at_5
+      value: 9.92
+    - type: mrr_at_1
+      value: 23.0
+    - type: mrr_at_10
+      value: 33.78
+    - type: mrr_at_100
+      value: 34.922
+    - type: mrr_at_1000
+      value: 34.973
+    - type: mrr_at_3
+      value: 30.2
+    - type: mrr_at_5
+      value: 32.565
+    - type: ndcg_at_1
+      value: 23.0
+    - type: ndcg_at_10
+      value: 19.863
+    - type: ndcg_at_100
+      value: 28.141
+    - type: ndcg_at_1000
+      value: 33.549
+    - type: ndcg_at_3
+      value: 18.434
+    - type: ndcg_at_5
+      value: 16.384
+    - type: precision_at_1
+      value: 23.0
+    - type: precision_at_10
+      value: 10.39
+    - type: precision_at_100
+      value: 2.235
+    - type: precision_at_1000
+      value: 0.35300000000000004
+    - type: precision_at_3
+      value: 17.133000000000003
+    - type: precision_at_5
+      value: 14.44
+    - type: recall_at_1
+      value: 4.692
+    - type: recall_at_10
+      value: 21.025
+    - type: recall_at_100
+      value: 45.324999999999996
+    - type: recall_at_1000
+      value: 71.675
+    - type: recall_at_3
+      value: 10.440000000000001
+    - type: recall_at_5
+      value: 14.64
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB SICK-R
+      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
+      split: test
+      type: mteb/sickr-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 84.96178184892842
+    - type: cos_sim_spearman
+      value: 79.6487740813199
+    - type: euclidean_pearson
+      value: 82.06661161625023
+    - type: euclidean_spearman
+      value: 79.64876769031183
+    - type: manhattan_pearson
+      value: 82.07061164575131
+    - type: manhattan_spearman
+      value: 79.65197039464537
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STS12
+      revision: a0d554a64d88156834ff5ae9920b964011b16384
+      split: test
+      type: mteb/sts12-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 84.15305604100027
+    - type: cos_sim_spearman
+      value: 74.27447427941591
+    - type: euclidean_pearson
+      value: 80.52737337565307
+    - type: euclidean_spearman
+      value: 74.27416077132192
+    - type: manhattan_pearson
+      value: 80.53728571140387
+    - type: manhattan_spearman
+      value: 74.28853605753457
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STS13
+      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
+      split: test
+      type: mteb/sts13-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 83.44386080639279
+    - type: cos_sim_spearman
+      value: 84.17947648159536
+    - type: euclidean_pearson
+      value: 83.34145388129387
+    - type: euclidean_spearman
+      value: 84.17947648159536
+    - type: manhattan_pearson
+      value: 83.30699061927966
+    - type: manhattan_spearman
+      value: 84.18125737380451
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STS14
+      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
+      split: test
+      type: mteb/sts14-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 81.57392220985612
+    - type: cos_sim_spearman
+      value: 78.80745014464101
+    - type: euclidean_pearson
+      value: 80.01660371487199
+    - type: euclidean_spearman
+      value: 78.80741240102256
+    - type: manhattan_pearson
+      value: 79.96810779507953
+    - type: manhattan_spearman
+      value: 78.75600400119448
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STS15
+      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
+      split: test
+      type: mteb/sts15-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 86.85421063026625
+    - type: cos_sim_spearman
+      value: 87.55320285299192
+    - type: euclidean_pearson
+      value: 86.69750143323517
+    - type: euclidean_spearman
+      value: 87.55320284326378
+    - type: manhattan_pearson
+      value: 86.63379169960379
+    - type: manhattan_spearman
+      value: 87.4815029877984
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STS16
+      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
+      split: test
+      type: mteb/sts16-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 84.31314130411842
+    - type: cos_sim_spearman
+      value: 85.3489588181433
+    - type: euclidean_pearson
+      value: 84.13240933463535
+    - type: euclidean_spearman
+      value: 85.34902871403281
+    - type: manhattan_pearson
+      value: 84.01183086503559
+    - type: manhattan_spearman
+      value: 85.19316703166102
+    task:
+      type: STS
+  - dataset:
+      config: en-en
+      name: MTEB STS17 (en-en)
+      revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
+      split: test
+      type: mteb/sts17-crosslingual-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 89.09979781689536
+    - type: cos_sim_spearman
+      value: 88.87813323759015
+    - type: euclidean_pearson
+      value: 88.65413031123792
+    - type: euclidean_spearman
+      value: 88.87813323759015
+    - type: manhattan_pearson
+      value: 88.61818758256024
+    - type: manhattan_spearman
+      value: 88.81044100494604
+    task:
+      type: STS
+  - dataset:
+      config: en
+      name: MTEB STS22 (en)
+      revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
+      split: test
+      type: mteb/sts22-crosslingual-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 62.30693258111531
+    - type: cos_sim_spearman
+      value: 62.195516523251946
+    - type: euclidean_pearson
+      value: 62.951283701049476
+    - type: euclidean_spearman
+      value: 62.195516523251946
+    - type: manhattan_pearson
+      value: 63.068322281439535
+    - type: manhattan_spearman
+      value: 62.10621171028406
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STSBenchmark
+      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
+      split: test
+      type: mteb/stsbenchmark-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 84.27092833763909
+    - type: cos_sim_spearman
+      value: 84.84429717949759
+    - type: euclidean_pearson
+      value: 84.8516966060792
+    - type: euclidean_spearman
+      value: 84.84429717949759
+    - type: manhattan_pearson
+      value: 84.82203139242881
+    - type: manhattan_spearman
+      value: 84.8358503952945
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB SciDocsRR
+      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
+      split: test
+      type: mteb/scidocs-reranking
+    metrics:
+    - type: map
+      value: 83.10290863981409
+    - type: mrr
+      value: 95.31168450286097
+    task:
+      type: Reranking
+  - dataset:
+      config: default
+      name: MTEB SciFact
+      revision: None
+      split: test
+      type: scifact
+    metrics:
+    - type: map_at_1
+      value: 52.161
+    - type: map_at_10
+      value: 62.138000000000005
+    - type: map_at_100
+      value: 62.769
+    - type: map_at_1000
+      value: 62.812
+    - type: map_at_3
+      value: 59.111000000000004
+    - type: map_at_5
+      value: 60.995999999999995
+    - type: mrr_at_1
+      value: 55.333
+    - type: mrr_at_10
+      value: 63.504000000000005
+    - type: mrr_at_100
+      value: 64.036
+    - type: mrr_at_1000
+      value: 64.08
+    - type: mrr_at_3
+      value: 61.278
+    - type: mrr_at_5
+      value: 62.778
+    - type: ndcg_at_1
+      value: 55.333
+    - type: ndcg_at_10
+      value: 66.678
+    - type: ndcg_at_100
+      value: 69.415
+    - type: ndcg_at_1000
+      value: 70.453
+    - type: ndcg_at_3
+      value: 61.755
+    - type: ndcg_at_5
+      value: 64.546
+    - type: precision_at_1
+      value: 55.333
+    - type: precision_at_10
+      value: 9.033
+    - type: precision_at_100
+      value: 1.043
+    - type: precision_at_1000
+      value: 0.11199999999999999
+    - type: precision_at_3
+      value: 24.221999999999998
+    - type: precision_at_5
+      value: 16.333000000000002
+    - type: recall_at_1
+      value: 52.161
+    - type: recall_at_10
+      value: 79.156
+    - type: recall_at_100
+      value: 91.333
+    - type: recall_at_1000
+      value: 99.333
+    - type: recall_at_3
+      value: 66.43299999999999
+    - type: recall_at_5
+      value: 73.272
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB SprintDuplicateQuestions
+      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
+      split: test
+      type: mteb/sprintduplicatequestions-pairclassification
+    metrics:
+    - type: cos_sim_accuracy
+      value: 99.81287128712871
+    - type: cos_sim_ap
+      value: 95.30034785910676
+    - type: cos_sim_f1
+      value: 90.28629856850716
+    - type: cos_sim_precision
+      value: 92.36401673640168
+    - type: cos_sim_recall
+      value: 88.3
+    - type: dot_accuracy
+      value: 99.81287128712871
+    - type: dot_ap
+      value: 95.30034785910676
+    - type: dot_f1
+      value: 90.28629856850716
+    - type: dot_precision
+      value: 92.36401673640168
+    - type: dot_recall
+      value: 88.3
+    - type: euclidean_accuracy
+      value: 99.81287128712871
+    - type: euclidean_ap
+      value: 95.30034785910676
+    - type: euclidean_f1
+      value: 90.28629856850716
+    - type: euclidean_precision
+      value: 92.36401673640168
+    - type: euclidean_recall
+      value: 88.3
+    - type: manhattan_accuracy
+      value: 99.80990099009901
+    - type: manhattan_ap
+      value: 95.26880751950654
+    - type: manhattan_f1
+      value: 90.22177419354838
+    - type: manhattan_precision
+      value: 90.95528455284553
+    - type: manhattan_recall
+      value: 89.5
+    - type: max_accuracy
+      value: 99.81287128712871
+    - type: max_ap
+      value: 95.30034785910676
+    - type: max_f1
+      value: 90.28629856850716
+    task:
+      type: PairClassification
+  - dataset:
+      config: default
+      name: MTEB StackExchangeClustering
+      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
+      split: test
+      type: mteb/stackexchange-clustering
+    metrics:
+    - type: v_measure
+      value: 58.518662504351184
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB StackExchangeClusteringP2P
+      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
+      split: test
+      type: mteb/stackexchange-clustering-p2p
+    metrics:
+    - type: v_measure
+      value: 34.96168178378587
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB StackOverflowDupQuestions
+      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
+      split: test
+      type: mteb/stackoverflowdupquestions-reranking
+    metrics:
+    - type: map
+      value: 52.04862593471896
+    - type: mrr
+      value: 52.97238402936932
+    task:
+      type: Reranking
+  - dataset:
+      config: default
+      name: MTEB SummEval
+      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
+      split: test
+      type: mteb/summeval
+    metrics:
+    - type: cos_sim_pearson
+      value: 30.092545236479946
+    - type: cos_sim_spearman
+      value: 31.599851000175498
+    - type: dot_pearson
+      value: 30.092542723901676
+    - type: dot_spearman
+      value: 31.599851000175498
+    task:
+      type: Summarization
+  - dataset:
+      config: default
+      name: MTEB TRECCOVID
+      revision: None
+      split: test
+      type: trec-covid
+    metrics:
+    - type: map_at_1
+      value: 0.189
+    - type: map_at_10
+      value: 1.662
+    - type: map_at_100
+      value: 9.384
+    - type: map_at_1000
+      value: 22.669
+    - type: map_at_3
+      value: 0.5559999999999999
+    - type: map_at_5
+      value: 0.9039999999999999
+    - type: mrr_at_1
+      value: 68.0
+    - type: mrr_at_10
+      value: 81.01899999999999
+    - type: mrr_at_100
+      value: 81.01899999999999
+    - type: mrr_at_1000
+      value: 81.01899999999999
+    - type: mrr_at_3
+      value: 79.333
+    - type: mrr_at_5
+      value: 80.733
+    - type: ndcg_at_1
+      value: 63.0
+    - type: ndcg_at_10
+      value: 65.913
+    - type: ndcg_at_100
+      value: 51.895
+    - type: ndcg_at_1000
+      value: 46.967
+    - type: ndcg_at_3
+      value: 65.49199999999999
+    - type: ndcg_at_5
+      value: 66.69699999999999
+    - type: precision_at_1
+      value: 68.0
+    - type: precision_at_10
+      value: 71.6
+    - type: precision_at_100
+      value: 53.66
+    - type: precision_at_1000
+      value: 21.124000000000002
+    - type: precision_at_3
+      value: 72.667
+    - type: precision_at_5
+      value: 74.0
+    - type: recall_at_1
+      value: 0.189
+    - type: recall_at_10
+      value: 1.913
+    - type: recall_at_100
+      value: 12.601999999999999
+    - type: recall_at_1000
+      value: 44.296
+    - type: recall_at_3
+      value: 0.605
+    - type: recall_at_5
+      value: 1.018
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB Touche2020
+      revision: None
+      split: test
+      type: webis-touche2020
+    metrics:
+    - type: map_at_1
+      value: 2.701
+    - type: map_at_10
+      value: 10.445
+    - type: map_at_100
+      value: 17.324
+    - type: map_at_1000
+      value: 19.161
+    - type: map_at_3
+      value: 5.497
+    - type: map_at_5
+      value: 7.278
+    - type: mrr_at_1
+      value: 30.612000000000002
+    - type: mrr_at_10
+      value: 45.534
+    - type: mrr_at_100
+      value: 45.792
+    - type: mrr_at_1000
+      value: 45.806999999999995
+    - type: mrr_at_3
+      value: 37.755
+    - type: mrr_at_5
+      value: 43.469
+    - type: ndcg_at_1
+      value: 26.531
+    - type: ndcg_at_10
+      value: 26.235000000000003
+    - type: ndcg_at_100
+      value: 39.17
+    - type: ndcg_at_1000
+      value: 51.038
+    - type: ndcg_at_3
+      value: 23.625
+    - type: ndcg_at_5
+      value: 24.338
+    - type: precision_at_1
+      value: 30.612000000000002
+    - type: precision_at_10
+      value: 24.285999999999998
+    - type: precision_at_100
+      value: 8.224
+    - type: precision_at_1000
+      value: 1.6179999999999999
+    - type: precision_at_3
+      value: 24.490000000000002
+    - type: precision_at_5
+      value: 24.898
+    - type: recall_at_1
+      value: 2.701
+    - type: recall_at_10
+      value: 17.997
+    - type: recall_at_100
+      value: 51.766999999999996
+    - type: recall_at_1000
+      value: 87.863
+    - type: recall_at_3
+      value: 6.295000000000001
+    - type: recall_at_5
+      value: 9.993
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB ToxicConversationsClassification
+      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
+      split: test
+      type: mteb/toxic_conversations_50k
+    metrics:
+    - type: accuracy
+      value: 73.3474
+    - type: ap
+      value: 15.393431414459924
+    - type: f1
+      value: 56.466681887882416
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB TweetSentimentExtractionClassification
+      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
+      split: test
+      type: mteb/tweet_sentiment_extraction
+    metrics:
+    - type: accuracy
+      value: 62.062818336163
+    - type: f1
+      value: 62.11230840463252
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB TwentyNewsgroupsClustering
+      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
+      split: test
+      type: mteb/twentynewsgroups-clustering
+    metrics:
+    - type: v_measure
+      value: 42.464892820845115
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB TwitterSemEval2015
+      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
+      split: test
+      type: mteb/twittersemeval2015-pairclassification
+    metrics:
+    - type: cos_sim_accuracy
+      value: 86.15962329379508
+    - type: cos_sim_ap
+      value: 74.73674057919256
+    - type: cos_sim_f1
+      value: 68.81245642574947
+    - type: cos_sim_precision
+      value: 61.48255813953488
+    - type: cos_sim_recall
+      value: 78.12664907651715
+    - type: dot_accuracy
+      value: 86.15962329379508
+    - type: dot_ap
+      value: 74.7367634988281
+    - type: dot_f1
+      value: 68.81245642574947
+    - type: dot_precision
+      value: 61.48255813953488
+    - type: dot_recall
+      value: 78.12664907651715
+    - type: euclidean_accuracy
+      value: 86.15962329379508
+    - type: euclidean_ap
+      value: 74.7367761466634
+    - type: euclidean_f1
+      value: 68.81245642574947
+    - type: euclidean_precision
+      value: 61.48255813953488
+    - type: euclidean_recall
+      value: 78.12664907651715
+    - type: manhattan_accuracy
+      value: 86.21326816474935
+    - type: manhattan_ap
+      value: 74.64416473733951
+    - type: manhattan_f1
+      value: 68.80924855491331
+    - type: manhattan_precision
+      value: 61.23456790123457
+    - type: manhattan_recall
+      value: 78.52242744063325
+    - type: max_accuracy
+      value: 86.21326816474935
+    - type: max_ap
+      value: 74.7367761466634
+    - type: max_f1
+      value: 68.81245642574947
+    task:
+      type: PairClassification
+  - dataset:
+      config: default
+      name: MTEB TwitterURLCorpus
+      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
+      split: test
+      type: mteb/twitterurlcorpus-pairclassification
+    metrics:
+    - type: cos_sim_accuracy
+      value: 88.97620988085536
+    - type: cos_sim_ap
+      value: 86.08680845745758
+    - type: cos_sim_f1
+      value: 78.02793637114438
+    - type: cos_sim_precision
+      value: 73.11082699683736
+    - type: cos_sim_recall
+      value: 83.65414228518632
+    - type: dot_accuracy
+      value: 88.97620988085536
+    - type: dot_ap
+      value: 86.08681149437946
+    - type: dot_f1
+      value: 78.02793637114438
+    - type: dot_precision
+      value: 73.11082699683736
+    - type: dot_recall
+      value: 83.65414228518632
+    - type: euclidean_accuracy
+      value: 88.97620988085536
+    - type: euclidean_ap
+      value: 86.08681215460771
+    - type: euclidean_f1
+      value: 78.02793637114438
+    - type: euclidean_precision
+      value: 73.11082699683736
+    - type: euclidean_recall
+      value: 83.65414228518632
+    - type: manhattan_accuracy
+      value: 88.88888888888889
+    - type: manhattan_ap
+      value: 86.02916327562438
+    - type: manhattan_f1
+      value: 78.02063045516843
+    - type: manhattan_precision
+      value: 73.38851947346994
+    - type: manhattan_recall
+      value: 83.2768709578072
+    - type: max_accuracy
+      value: 88.97620988085536
+    - type: max_ap
+      value: 86.08681215460771
+    - type: max_f1
+      value: 78.02793637114438
+    task:
+      type: PairClassification
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- mteb
+- onnx
+- teradata
+
+---
+# A Teradata Vantage compatible Embeddings Model
+
+# jinaai/jina-embeddings-v2-base-en
+
+## Overview of this Model
+
+An Embedding Model which maps text (sentence/ paragraphs) into a vector.  The [jinaai/jina-embeddings-v2-base-en](https://huggingface.co/jinaai/jina-embeddings-v2-base-en) model well known for its effectiveness in capturing semantic meanings in text data. It's a state-of-the-art model trained on a large corpus, capable of generating high-quality text embeddings.
+
+- 137.37M params (Sizes in ONNX format - "fp32": 522.03MB, "int8": 131.14MB, "uint8": 131.14MB)
+- 8192 maximum input tokens 
+- 768 dimensions of output vector
+- Licence: apache-2.0. The released models can be used for commercial purposes free of charge.
+- Reference to Original Model: https://huggingface.co/jinaai/jina-embeddings-v2-base-en
+
+
+## Quickstart: Deploying this Model in Teradata Vantage
+
+We have pre-converted the model into the ONNX format compatible with BYOM 6.0, eliminating the need for manual conversion. 
+
+**Note:** Ensure you have access to a Teradata Database with BYOM 6.0 installed.
+
+To get started, clone the pre-converted model directly from the Teradata HuggingFace repository.
+
+
+```python
+
+import teradataml as tdml
+import getpass
+from huggingface_hub import hf_hub_download
+
+model_name = "jina-embeddings-v2-base-en"
+number_dimensions_output = 768
+model_file_name = "model.onnx"
+
+# Step 1: Download Model from Teradata HuggingFace Page
+
+hf_hub_download(repo_id=f"Teradata/{model_name}", filename=f"onnx/{model_file_name}", local_dir="./")
+hf_hub_download(repo_id=f"Teradata/{model_name}", filename=f"tokenizer.json", local_dir="./")
+
+# Step 2: Create Connection to Vantage
+
+tdml.create_context(host = input('enter your hostname'), 
+                    username=input('enter your username'), 
+                    password = getpass.getpass("enter your password"))
+
+# Step 3: Load Models into Vantage
+# a) Embedding model
+tdml.save_byom(model_id = model_name, # must be unique in the models table
+               model_file = model_file_name,
+               table_name = 'embeddings_models' )
+# b) Tokenizer
+tdml.save_byom(model_id = model_name, # must be unique in the models table
+              model_file = 'tokenizer.json',
+              table_name = 'embeddings_tokenizers') 
+
+# Step 4: Test ONNXEmbeddings Function
+# Note that ONNXEmbeddings expects the 'payload' column to be 'txt'. 
+# If it has got a different name, just rename it in a subquery/CTE.
+input_table = "emails.emails"
+embeddings_query = f"""
+SELECT 
+        *
+from mldb.ONNXEmbeddings(
+        on {input_table} as InputTable
+        on (select * from embeddings_models where model_id = '{model_name}') as ModelTable DIMENSION
+        on (select model as tokenizer from embeddings_tokenizers where model_id = '{model_name}') as TokenizerTable DIMENSION
+        using
+            Accumulate('id', 'txt') 
+            ModelOutputTensor('sentence_embedding')
+            EnableMemoryCheck('false')
+            OutputFormat('FLOAT32({number_dimensions_output})')
+            OverwriteCachedModel('true')
+    ) a 
+"""
+DF_embeddings = tdml.DataFrame.from_query(embeddings_query)
+DF_embeddings
+```
+
+
+
+## What Can I Do with the Embeddings?
+
+Teradata Vantage includes pre-built in-database functions to process embeddings further. Explore the following examples:
+
+- **Semantic Clustering with TD_KMeans:** [Semantic Clustering Python Notebook](https://github.com/Teradata/jupyter-demos/blob/main/UseCases/Language_Models_InVantage/Semantic_Clustering_Python.ipynb)
+- **Semantic Distance with TD_VectorDistance:** [Semantic Similarity Python Notebook](https://github.com/Teradata/jupyter-demos/blob/main/UseCases/Language_Models_InVantage/Semantic_Similarity_Python.ipynb)
+- **RAG-Based Application with TD_VectorDistance:** [RAG and Bedrock Query PDF Notebook](https://github.com/Teradata/jupyter-demos/blob/main/UseCases/Language_Models_InVantage/RAG_and_Bedrock_QueryPDF.ipynb)
+
+
+## Deep Dive into Model Conversion to ONNX
+
+**The steps below outline how we converted the open-source Hugging Face model into an ONNX file compatible with the in-database ONNXEmbeddings function.** 
+
+You do not need to perform these steps—they are provided solely for documentation and transparency. However, they may be helpful if you wish to convert another model to the required format.
+
+
+### Part 1. Importing and Converting Model using optimum
+
+We start by importing the pre-trained [jinaai/jina-embeddings-v2-base-en](https://huggingface.co/jinaai/jina-embeddings-v2-base-en) model from Hugging Face.
+
+We are downloading the ONNX files from the repository prepared by the model authors.
+
+After downloading, we are fixing the opset in the ONNX file for compatibility with ONNX runtime used in Teradata Vantage
+
+Also we adding the man pooling and normalization layers to the ONNX file
+
+We are generating ONNX files for multiple different precisions: fp32, int8, uint8
+
+You can find the detailed conversion steps in the file [convert.py](./convert.py)
+
+### Part 2. Running the model in Python with onnxruntime & compare results
+
+Once the fixes are applied, we proceed to test the correctness of the ONNX model by calculating cosine similarity between two texts using native SentenceTransformers and ONNX runtime, comparing the results.
+
+If the results are identical, it confirms that the ONNX model gives the same result as the native models, validating its correctness and suitability for further use in the database.
+
+
+```python
+import onnxruntime as rt
+
+from sentence_transformers.util import cos_sim
+from sentence_transformers import SentenceTransformer
+
+import transformers
+
+
+sentences_1 = 'How is the weather today?'
+sentences_2 = 'What is the current weather like today?'
+
+# Calculate ONNX result
+tokenizer = transformers.AutoTokenizer.from_pretrained("jinaai/jina-embeddings-v2-base-en")
+predef_sess = rt.InferenceSession("onnx/model.onnx")
+
+enc1 = tokenizer(sentences_1)
+embeddings_1_onnx = predef_sess.run(None,     {"input_ids": [enc1.input_ids], 
+     "attention_mask": [enc1.attention_mask]})
+
+enc2 = tokenizer(sentences_2)
+embeddings_2_onnx = predef_sess.run(None,     {"input_ids": [enc2.input_ids], 
+     "attention_mask": [enc2.attention_mask]})
+
+
+# Calculate embeddings with SentenceTransformer
+model = SentenceTransformer(model_id, trust_remote_code=True)
+embeddings_1_sentence_transformer = model.encode(sentences_1, normalize_embeddings=True, trust_remote_code=True)
+embeddings_2_sentence_transformer = model.encode(sentences_2, normalize_embeddings=True, trust_remote_code=True)
+
+# Compare results
+print("Cosine similiarity for embeddings calculated with ONNX:" + str(cos_sim(embeddings_1_onnx[1][0], embeddings_2_onnx[1][0])))
+print("Cosine similiarity for embeddings calculated with SentenceTransformer:" + str(cos_sim(embeddings_1_sentence_transformer, embeddings_2_sentence_transformer)))
+```
+
+You can find the detailed ONNX vs. SentenceTransformer result comparison steps in the file [test_local.py](./test_local.py)
+

config.json

@@ -0,0 +1,38 @@
+{
+  "_attn_implementation_autoset": true,
+  "_name_or_path": "jinaai/jina-embeddings-v2-base-en",
+  "architectures": [
+    "JinaBertForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "attn_implementation": null,
+  "auto_map": {
+    "AutoConfig": "jinaai/jina-bert-implementation--configuration_bert.JinaBertConfig",
+    "AutoModel": "jinaai/jina-bert-implementation--modeling_bert.JinaBertModel",
+    "AutoModelForMaskedLM": "jinaai/jina-bert-implementation--modeling_bert.JinaBertForMaskedLM",
+    "AutoModelForSequenceClassification": "jinaai/jina-bert-implementation--modeling_bert.JinaBertForSequenceClassification"
+  },
+  "classifier_dropout": null,
+  "emb_pooler": "mean",
+  "export_model_type": "transformer",
+  "feed_forward_type": "geglu",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 8192,
+  "model_max_length": 8192,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "alibi",
+  "torch_dtype": "float32",
+  "transformers_version": "4.47.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30528
+}

conversion_config.json

@@ -0,0 +1,12 @@
+{
+    "model_id": "jinaai/jina-embeddings-v2-base-en",
+    "number_of_generated_embeddings": 768,
+    "precision_to_filename_map": {
+    	"fp32": "onnx/model.onnx",
+        "int8": "onnx/model_int8.onnx",
+        "uint8": "onnx/model_uint8.onnx"
+        
+    },
+    "opset": 16,
+    "IR": 8
+}

convert.py

@@ -0,0 +1,198 @@
+import os
+import json
+import shutil
+
+from optimum.exporters.onnx import main_export
+import onnx
+from onnxconverter_common import float16
+import onnxruntime as rt
+from onnxruntime.tools.onnx_model_utils import *
+from onnxruntime.quantization import quantize_dynamic, QuantType
+import huggingface_hub
+
+def add_mean_pooling(input_model, output_model, op, IR, output_embeddings_number):
+    model = onnx.load(input_model)
+    model_ir8 = onnx.helper.make_model(model.graph, ir_version = IR, opset_imports = [op]) #to be sure that we have compatible opset and IR version
+    
+    minus_one_axis = onnx.helper.make_tensor(
+        name = "minus_one_axis", 
+        data_type = onnx.TensorProto.INT64, 
+        dims = [1], 
+        vals = [-1])
+    
+    model_ir8.graph.initializer.append(minus_one_axis)
+    
+    mask_clip_lower_limit = onnx.helper.make_tensor(
+        name = "mask_clip_lower_limit", 
+        data_type = onnx.TensorProto.FLOAT, 
+        dims = [1], 
+        vals = [1e-9])
+    
+    model_ir8.graph.initializer.append(mask_clip_lower_limit)
+    
+    sum_one_axis = onnx.helper.make_tensor(
+        name = "sum_one_axis", 
+        data_type = onnx.TensorProto.INT64, 
+        dims = [1], 
+        vals = [1])
+    
+    model_ir8.graph.initializer.append(sum_one_axis)
+    
+    attention_mask_cast_op = onnx.helper.make_node(
+        "Cast",
+        inputs=["attention_mask"],
+        outputs=["attention_mask_fp32"],
+        to=onnx.TensorProto.FLOAT
+    )
+    
+    model_ir8.graph.node.append(attention_mask_cast_op)
+    
+    expand_dims_op = onnx.helper.make_node(
+        "Unsqueeze",
+        inputs=["attention_mask_fp32", "minus_one_axis"],
+        outputs=["unsqueezed_attention_mask"],
+    )
+    
+    model_ir8.graph.node.append(expand_dims_op)
+    
+    shape_op = onnx.helper.make_node(
+        "Shape",
+        inputs = ["last_hidden_state"],
+        outputs = ["last_hidden_state_shape"]
+    )
+    
+    model_ir8.graph.node.append(shape_op)
+        
+    broadcast_to_op = onnx.helper.make_node(
+        "Expand",
+        inputs=["unsqueezed_attention_mask", "last_hidden_state_shape"],
+        outputs=["expanded_attention_mask"],
+    )
+    
+    model_ir8.graph.node.append(broadcast_to_op)
+    
+    multiply_op = onnx.helper.make_node(
+        "Mul",
+        inputs=["last_hidden_state", "expanded_attention_mask"],
+        outputs=["last_hidden_state_x_expanded_attention_mask"],
+    )
+    
+    model_ir8.graph.node.append(multiply_op)
+    
+    sum_embeddings_op = onnx.helper.make_node(
+        "ReduceSum",
+        inputs=["last_hidden_state_x_expanded_attention_mask", "sum_one_axis"],
+        outputs=["sum_last_hidden_state_x_expanded_attention_mask"],
+    )
+    
+    model_ir8.graph.node.append(sum_embeddings_op)
+    
+    sum_mask_op = onnx.helper.make_node(
+        "ReduceSum",
+        inputs=["expanded_attention_mask", "sum_one_axis"],
+        outputs=["sum_expanded_attention_mask"],
+    )
+    
+    model_ir8.graph.node.append(sum_mask_op)
+    
+    clip_mask_op = onnx.helper.make_node(
+        "Clip",
+        inputs=["sum_expanded_attention_mask", "mask_clip_lower_limit"],
+        outputs=["clipped_sum_expanded_attention_mask"],
+    )
+    
+    model_ir8.graph.node.append(clip_mask_op)
+    
+    pooled_embeddings_op = onnx.helper.make_node(
+        "Div",
+        inputs=["sum_last_hidden_state_x_expanded_attention_mask", "clipped_sum_expanded_attention_mask"],
+        outputs=["pooled_embeddings"],
+        # outputs=["sentence_embeddings"]
+    )
+    
+    model_ir8.graph.node.append(pooled_embeddings_op)
+    
+    squeeze_pooled_embeddings_op = onnx.helper.make_node(
+        "Squeeze",
+        inputs=["pooled_embeddings", "sum_one_axis"],
+        outputs=["squeezed_pooled_embeddings"]
+        
+    )
+    
+    model_ir8.graph.node.append(squeeze_pooled_embeddings_op)
+    
+    normalized_pooled_embeddings_op = onnx.helper.make_node(
+        "Normalizer",
+        domain="ai.onnx.ml",
+        inputs=["squeezed_pooled_embeddings"],
+        outputs=["sentence_embedding"],
+        norm = "L2"
+    )
+    
+    
+    model_ir8.graph.node.append(normalized_pooled_embeddings_op)
+    
+    sentence_embeddings_output = onnx.helper.make_tensor_value_info(
+        "sentence_embedding", 
+        onnx.TensorProto.FLOAT, 
+        shape=["batch_size", output_embeddings_number]
+    )
+    
+    model_ir8.graph.output.append(sentence_embeddings_output)
+    
+    for node in model_ir8.graph.output:
+        if node.name == "last_hidden_state":
+            model_ir8.graph.output.remove(node)
+    
+    model_ir8 = onnx.helper.make_model(model_ir8.graph, ir_version = 8, opset_imports = [op]) #to be sure that we have compatible opset and IR version
+    
+    onnx.save(model_ir8, output_model, save_as_external_data = False)
+
+    
+
+with open('conversion_config.json') as json_file:
+    conversion_config = json.load(json_file)
+
+
+    model_id = conversion_config["model_id"]
+    number_of_generated_embeddings = conversion_config["number_of_generated_embeddings"]
+    precision_to_filename_map = conversion_config["precision_to_filename_map"]
+    opset = conversion_config["opset"]
+    IR = conversion_config["IR"]
+
+    
+    op = onnx.OperatorSetIdProto()
+    op.version = opset
+    
+    
+    if not os.path.exists("onnx"):
+        os.makedirs("onnx")
+
+    print("Exporting the main model version")
+    try:
+        main_export(model_name_or_path=model_id, output="./",  opset=opset, trust_remote_code=True, task="feature-extraction", dtype="fp32")
+    except:
+        huggingface_hub.hf_hub_download(repo_id=model_id, filename="model.onnx", local_dir="./")
+        
+    
+    if "fp32" in precision_to_filename_map:
+        print("Exporting the fp32 onnx file...")
+        
+        shutil.copyfile('model.onnx', precision_to_filename_map["fp32"]) 
+        add_mean_pooling("model.onnx", precision_to_filename_map["fp32"], op, IR, number_of_generated_embeddings)
+        
+        print("Done\n\n")
+
+    if "int8" in precision_to_filename_map:
+        print("Quantizing fp32 model to int8...")
+        quantize_dynamic("model.onnx",  precision_to_filename_map["int8"], weight_type=QuantType.QInt8)
+        add_mean_pooling( precision_to_filename_map["int8"], precision_to_filename_map["int8"], op, IR, number_of_generated_embeddings)
+        print("Done\n\n")
+        
+    if "uint8" in precision_to_filename_map:
+        print("Quantizing fp32 model to uint8...")
+        quantize_dynamic("model.onnx", precision_to_filename_map["uint8"], weight_type=QuantType.QUInt8)
+        add_mean_pooling( precision_to_filename_map["uint8"], precision_to_filename_map["uint8"], op, IR, number_of_generated_embeddings)
+        print("Done\n\n")
+        
+    os.remove("model.onnx")

onnx/model.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1f60cdfa2271dcc9a63ef1a6a24bbbd91e9c6360926225dc6977763f043d991b
+size 547391321

onnx/model_int8.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a710cd487b92474378d06c630d5562f5dce9804199b3cca12aeba51a465c8098
+size 137512014

onnx/model_uint8.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fe0d28b40aa940d30fe785f07c4c53462870368265a490e9fa7b6ff64ebe8430
+size 137512051

special_tokens_map.json

@@ -0,0 +1,37 @@
+{
+  "cls_token": {
+    "content": "[CLS]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "mask_token": {
+    "content": "[MASK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "[PAD]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "sep_token": {
+    "content": "[SEP]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "[UNK]",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

test_local.py

@@ -0,0 +1,49 @@
+import onnxruntime as rt
+
+from sentence_transformers.util import cos_sim
+from sentence_transformers import SentenceTransformer
+
+import transformers
+
+import gc
+import json
+
+
+with open('conversion_config.json') as json_file:
+    conversion_config = json.load(json_file)
+
+
+    model_id = conversion_config["model_id"]
+    number_of_generated_embeddings = conversion_config["number_of_generated_embeddings"]
+    precision_to_filename_map = conversion_config["precision_to_filename_map"]
+    
+    sentences_1 = 'How is the weather today?'
+    sentences_2 = 'What is the current weather like today?'
+    
+    print(f"Testing on cosine similiarity between sentences: \n'{sentences_1}'\n'{sentences_2}'\n\n\n")
+    
+    tokenizer = transformers.AutoTokenizer.from_pretrained("./", trust_remote_code=True)
+    enc1 = tokenizer(sentences_1)
+    enc2 = tokenizer(sentences_2)
+    
+    for precision, file_name in precision_to_filename_map.items():
+    
+        
+        onnx_session = rt.InferenceSession(file_name)
+        embeddings_1_onnx = onnx_session.run(None,     {"input_ids": [enc1.input_ids], 
+             "attention_mask": [enc1.attention_mask], "token_type_ids": [enc1.token_type_ids] })[0][0]
+        
+    
+        embeddings_2_onnx = onnx_session.run(None,     {"input_ids": [enc2.input_ids], 
+             "attention_mask": [enc2.attention_mask], "token_type_ids": [enc2.token_type_ids]})[0][0]
+        del onnx_session
+        gc.collect()
+        print(f'Cosine similiarity for ONNX model with precision "{precision}" is {str(cos_sim(embeddings_1_onnx, embeddings_2_onnx))}')
+    
+    
+    
+    
+    model = SentenceTransformer(model_id, trust_remote_code=True)
+    embeddings_1_sentence_transformer = model.encode(sentences_1, normalize_embeddings=True, trust_remote_code=True)
+    embeddings_2_sentence_transformer = model.encode(sentences_2, normalize_embeddings=True, trust_remote_code=True)
+    print('Cosine similiarity for original sentence transformer model is '+str(cos_sim(embeddings_1_sentence_transformer, embeddings_2_sentence_transformer)))

test_teradata.py

@@ -0,0 +1,106 @@
+import sys
+import teradataml as tdml
+from tabulate import tabulate
+
+import json
+
+
+with open('conversion_config.json') as json_file:
+    conversion_config = json.load(json_file)
+
+
+    model_id = conversion_config["model_id"]
+    number_of_generated_embeddings = conversion_config["number_of_generated_embeddings"]
+    precision_to_filename_map = conversion_config["precision_to_filename_map"]
+    
+    host = sys.argv[1]
+    username = sys.argv[2]
+    password = sys.argv[3]
+    
+    print("Setting up connection to teradata...")
+    tdml.create_context(host = host, username = username, password = password)
+    print("Done\n\n")
+    
+    
+    print("Deploying tokenizer...")
+    try:
+        tdml.db_drop_table('tokenizer_table')
+    except:
+        print("Can't drop tokenizers table - it's not existing")
+    tdml.save_byom('tokenizer',
+                  'tokenizer.json',
+                  'tokenizer_table')
+    print("Done\n\n")
+    
+    print("Testing models...")
+    try:
+        tdml.db_drop_table('model_table')
+    except:
+        print("Can't drop models table - it's not existing")
+    
+    for precision, file_name in precision_to_filename_map.items():
+        print(f"Deploying {precision} model...")
+        tdml.save_byom(precision,
+                      file_name,
+                      'model_table')
+        print(f"Model {precision} is deployed\n")
+    
+        print(f"Calculating embeddings with {precision} model...")
+        try:
+            tdml.db_drop_table('emails_embeddings_store')
+        except:
+            print("Can't drop embeddings table - it's not existing")
+        
+        tdml.execute_sql(f"""
+            create volatile table emails_embeddings_store as (
+                select 
+                    *
+            from mldb.ONNXEmbeddings(
+                    on emails.emails as InputTable
+                    on (select * from model_table where model_id = '{precision}') as ModelTable DIMENSION
+                    on (select model as tokenizer from tokenizer_table where model_id = 'tokenizer') as TokenizerTable DIMENSION
+               
+                    using
+                        Accumulate('id', 'txt') 
+                        ModelOutputTensor('sentence_embedding')
+                        EnableMemoryCheck('false')
+                        OutputFormat('FLOAT32({number_of_generated_embeddings})')
+                        OverwriteCachedModel('true')
+                ) a 
+        ) with data on commit preserve rows
+        
+        """)
+        print("Embeddings calculated")
+        print(f"Testing semantic search with cosine similiarity on the output of the model with precision '{precision}'...")
+        tdf_embeddings_store = tdml.DataFrame('emails_embeddings_store')
+        tdf_embeddings_store_tgt = tdf_embeddings_store[tdf_embeddings_store.id == 3]
+        
+        tdf_embeddings_store_ref = tdf_embeddings_store[tdf_embeddings_store.id != 3]
+        
+        cos_sim_pd = tdml.DataFrame.from_query(f"""
+            SELECT 
+                dt.target_id, 
+                dt.reference_id,
+                e_tgt.txt as target_txt,
+                e_ref.txt as reference_txt,
+                (1.0 - dt.distance) as similiarity 
+            FROM
+                TD_VECTORDISTANCE (
+                    ON ({tdf_embeddings_store_tgt.show_query()}) AS TargetTable
+                    ON ({tdf_embeddings_store_ref.show_query()}) AS ReferenceTable DIMENSION
+                    USING
+                        TargetIDColumn('id')
+                        TargetFeatureColumns('[emb_0:emb_{number_of_generated_embeddings - 1}]')
+                        RefIDColumn('id')
+                        RefFeatureColumns('[emb_0:emb_{number_of_generated_embeddings - 1}]')
+                        DistanceMeasure('cosine')
+                        topk(3)
+                ) AS dt
+            JOIN emails.emails e_tgt on e_tgt.id = dt.target_id
+            JOIN emails.emails e_ref on e_ref.id = dt.reference_id;
+            """).to_pandas()
+        print(tabulate(cos_sim_pd, headers='keys', tablefmt='fancy_grid'))
+        print("Done\n\n")
+    
+    
+    tdml.remove_context()

tokenizer_config.json

@@ -0,0 +1,58 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 2147483648,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}