model bge small en v 1.5

README.md

@@ -1,3 +1,2655 @@
----
-license: mit
----
+---
+language:
+- en
+license: mit
+model-index:
+- name: bge-small-en-v1.5
+  results:
+  - dataset:
+      config: en
+      name: MTEB AmazonCounterfactualClassification (en)
+      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
+      split: test
+      type: mteb/amazon_counterfactual
+    metrics:
+    - type: accuracy
+      value: 73.79104477611939
+    - type: ap
+      value: 37.21923821573361
+    - type: f1
+      value: 68.0914945617093
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB AmazonPolarityClassification
+      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
+      split: test
+      type: mteb/amazon_polarity
+    metrics:
+    - type: accuracy
+      value: 92.75377499999999
+    - type: ap
+      value: 89.46766124546022
+    - type: f1
+      value: 92.73884001331487
+    task:
+      type: Classification
+  - dataset:
+      config: en
+      name: MTEB AmazonReviewsClassification (en)
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+      split: test
+      type: mteb/amazon_reviews_multi
+    metrics:
+    - type: accuracy
+      value: 46.986
+    - type: f1
+      value: 46.55936786727896
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB ArguAna
+      revision: None
+      split: test
+      type: arguana
+    metrics:
+    - type: map_at_1
+      value: 35.846000000000004
+    - type: map_at_10
+      value: 51.388
+    - type: map_at_100
+      value: 52.132999999999996
+    - type: map_at_1000
+      value: 52.141000000000005
+    - type: map_at_3
+      value: 47.037
+    - type: map_at_5
+      value: 49.579
+    - type: mrr_at_1
+      value: 36.558
+    - type: mrr_at_10
+      value: 51.658
+    - type: mrr_at_100
+      value: 52.402
+    - type: mrr_at_1000
+      value: 52.410000000000004
+    - type: mrr_at_3
+      value: 47.345
+    - type: mrr_at_5
+      value: 49.797999999999995
+    - type: ndcg_at_1
+      value: 35.846000000000004
+    - type: ndcg_at_10
+      value: 59.550000000000004
+    - type: ndcg_at_100
+      value: 62.596
+    - type: ndcg_at_1000
+      value: 62.759
+    - type: ndcg_at_3
+      value: 50.666999999999994
+    - type: ndcg_at_5
+      value: 55.228
+    - type: precision_at_1
+      value: 35.846000000000004
+    - type: precision_at_10
+      value: 8.542
+    - type: precision_at_100
+      value: 0.984
+    - type: precision_at_1000
+      value: 0.1
+    - type: precision_at_3
+      value: 20.389
+    - type: precision_at_5
+      value: 14.438
+    - type: recall_at_1
+      value: 35.846000000000004
+    - type: recall_at_10
+      value: 85.42
+    - type: recall_at_100
+      value: 98.43499999999999
+    - type: recall_at_1000
+      value: 99.644
+    - type: recall_at_3
+      value: 61.166
+    - type: recall_at_5
+      value: 72.191
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB ArxivClusteringP2P
+      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
+      split: test
+      type: mteb/arxiv-clustering-p2p
+    metrics:
+    - type: v_measure
+      value: 47.402770198163594
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB ArxivClusteringS2S
+      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
+      split: test
+      type: mteb/arxiv-clustering-s2s
+    metrics:
+    - type: v_measure
+      value: 40.01545436974177
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB AskUbuntuDupQuestions
+      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
+      split: test
+      type: mteb/askubuntudupquestions-reranking
+    metrics:
+    - type: map
+      value: 62.586465273207196
+    - type: mrr
+      value: 74.42169019038825
+    task:
+      type: Reranking
+  - dataset:
+      config: default
+      name: MTEB BIOSSES
+      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
+      split: test
+      type: mteb/biosses-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 85.1891186537969
+    - type: cos_sim_spearman
+      value: 83.75492046087288
+    - type: euclidean_pearson
+      value: 84.11766204805357
+    - type: euclidean_spearman
+      value: 84.01456493126516
+    - type: manhattan_pearson
+      value: 84.2132950502772
+    - type: manhattan_spearman
+      value: 83.89227298813377
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB Banking77Classification
+      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
+      split: test
+      type: mteb/banking77
+    metrics:
+    - type: accuracy
+      value: 85.74025974025975
+    - type: f1
+      value: 85.71493566466381
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB BiorxivClusteringP2P
+      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
+      split: test
+      type: mteb/biorxiv-clustering-p2p
+    metrics:
+    - type: v_measure
+      value: 38.467181385006434
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB BiorxivClusteringS2S
+      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
+      split: test
+      type: mteb/biorxiv-clustering-s2s
+    metrics:
+    - type: v_measure
+      value: 34.719496037339056
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB CQADupstackAndroidRetrieval
+      revision: None
+      split: test
+      type: BeIR/cqadupstack
+    metrics:
+    - type: map_at_1
+      value: 29.587000000000003
+    - type: map_at_10
+      value: 41.114
+    - type: map_at_100
+      value: 42.532
+    - type: map_at_1000
+      value: 42.661
+    - type: map_at_3
+      value: 37.483
+    - type: map_at_5
+      value: 39.652
+    - type: mrr_at_1
+      value: 36.338
+    - type: mrr_at_10
+      value: 46.763
+    - type: mrr_at_100
+      value: 47.393
+    - type: mrr_at_1000
+      value: 47.445
+    - type: mrr_at_3
+      value: 43.538
+    - type: mrr_at_5
+      value: 45.556000000000004
+    - type: ndcg_at_1
+      value: 36.338
+    - type: ndcg_at_10
+      value: 47.658
+    - type: ndcg_at_100
+      value: 52.824000000000005
+    - type: ndcg_at_1000
+      value: 54.913999999999994
+    - type: ndcg_at_3
+      value: 41.989
+    - type: ndcg_at_5
+      value: 44.944
+    - type: precision_at_1
+      value: 36.338
+    - type: precision_at_10
+      value: 9.156
+    - type: precision_at_100
+      value: 1.4789999999999999
+    - type: precision_at_1000
+      value: 0.196
+    - type: precision_at_3
+      value: 20.076
+    - type: precision_at_5
+      value: 14.85
+    - type: recall_at_1
+      value: 29.587000000000003
+    - type: recall_at_10
+      value: 60.746
+    - type: recall_at_100
+      value: 82.157
+    - type: recall_at_1000
+      value: 95.645
+    - type: recall_at_3
+      value: 44.821
+    - type: recall_at_5
+      value: 52.819
+    - type: map_at_1
+      value: 30.239
+    - type: map_at_10
+      value: 39.989000000000004
+    - type: map_at_100
+      value: 41.196
+    - type: map_at_1000
+      value: 41.325
+    - type: map_at_3
+      value: 37.261
+    - type: map_at_5
+      value: 38.833
+    - type: mrr_at_1
+      value: 37.516
+    - type: mrr_at_10
+      value: 46.177
+    - type: mrr_at_100
+      value: 46.806
+    - type: mrr_at_1000
+      value: 46.849000000000004
+    - type: mrr_at_3
+      value: 44.002
+    - type: mrr_at_5
+      value: 45.34
+    - type: ndcg_at_1
+      value: 37.516
+    - type: ndcg_at_10
+      value: 45.586
+    - type: ndcg_at_100
+      value: 49.897000000000006
+    - type: ndcg_at_1000
+      value: 51.955
+    - type: ndcg_at_3
+      value: 41.684
+    - type: ndcg_at_5
+      value: 43.617
+    - type: precision_at_1
+      value: 37.516
+    - type: precision_at_10
+      value: 8.522
+    - type: precision_at_100
+      value: 1.374
+    - type: precision_at_1000
+      value: 0.184
+    - type: precision_at_3
+      value: 20.105999999999998
+    - type: precision_at_5
+      value: 14.152999999999999
+    - type: recall_at_1
+      value: 30.239
+    - type: recall_at_10
+      value: 55.03
+    - type: recall_at_100
+      value: 73.375
+    - type: recall_at_1000
+      value: 86.29599999999999
+    - type: recall_at_3
+      value: 43.269000000000005
+    - type: recall_at_5
+      value: 48.878
+    - type: map_at_1
+      value: 38.338
+    - type: map_at_10
+      value: 50.468999999999994
+    - type: map_at_100
+      value: 51.553000000000004
+    - type: map_at_1000
+      value: 51.608
+    - type: map_at_3
+      value: 47.107
+    - type: map_at_5
+      value: 49.101
+    - type: mrr_at_1
+      value: 44.201
+    - type: mrr_at_10
+      value: 54.057
+    - type: mrr_at_100
+      value: 54.764
+    - type: mrr_at_1000
+      value: 54.791000000000004
+    - type: mrr_at_3
+      value: 51.56699999999999
+    - type: mrr_at_5
+      value: 53.05
+    - type: ndcg_at_1
+      value: 44.201
+    - type: ndcg_at_10
+      value: 56.379000000000005
+    - type: ndcg_at_100
+      value: 60.645
+    - type: ndcg_at_1000
+      value: 61.73499999999999
+    - type: ndcg_at_3
+      value: 50.726000000000006
+    - type: ndcg_at_5
+      value: 53.58500000000001
+    - type: precision_at_1
+      value: 44.201
+    - type: precision_at_10
+      value: 9.141
+    - type: precision_at_100
+      value: 1.216
+    - type: precision_at_1000
+      value: 0.135
+    - type: precision_at_3
+      value: 22.654
+    - type: precision_at_5
+      value: 15.723999999999998
+    - type: recall_at_1
+      value: 38.338
+    - type: recall_at_10
+      value: 70.30499999999999
+    - type: recall_at_100
+      value: 88.77199999999999
+    - type: recall_at_1000
+      value: 96.49799999999999
+    - type: recall_at_3
+      value: 55.218
+    - type: recall_at_5
+      value: 62.104000000000006
+    - type: map_at_1
+      value: 25.682
+    - type: map_at_10
+      value: 33.498
+    - type: map_at_100
+      value: 34.461000000000006
+    - type: map_at_1000
+      value: 34.544000000000004
+    - type: map_at_3
+      value: 30.503999999999998
+    - type: map_at_5
+      value: 32.216
+    - type: mrr_at_1
+      value: 27.683999999999997
+    - type: mrr_at_10
+      value: 35.467999999999996
+    - type: mrr_at_100
+      value: 36.32
+    - type: mrr_at_1000
+      value: 36.386
+    - type: mrr_at_3
+      value: 32.618
+    - type: mrr_at_5
+      value: 34.262
+    - type: ndcg_at_1
+      value: 27.683999999999997
+    - type: ndcg_at_10
+      value: 38.378
+    - type: ndcg_at_100
+      value: 43.288
+    - type: ndcg_at_1000
+      value: 45.413
+    - type: ndcg_at_3
+      value: 32.586
+    - type: ndcg_at_5
+      value: 35.499
+    - type: precision_at_1
+      value: 27.683999999999997
+    - type: precision_at_10
+      value: 5.864
+    - type: precision_at_100
+      value: 0.882
+    - type: precision_at_1000
+      value: 0.11
+    - type: precision_at_3
+      value: 13.446
+    - type: precision_at_5
+      value: 9.718
+    - type: recall_at_1
+      value: 25.682
+    - type: recall_at_10
+      value: 51.712
+    - type: recall_at_100
+      value: 74.446
+    - type: recall_at_1000
+      value: 90.472
+    - type: recall_at_3
+      value: 36.236000000000004
+    - type: recall_at_5
+      value: 43.234
+    - type: map_at_1
+      value: 16.073999999999998
+    - type: map_at_10
+      value: 24.352999999999998
+    - type: map_at_100
+      value: 25.438
+    - type: map_at_1000
+      value: 25.545
+    - type: map_at_3
+      value: 21.614
+    - type: map_at_5
+      value: 23.104
+    - type: mrr_at_1
+      value: 19.776
+    - type: mrr_at_10
+      value: 28.837000000000003
+    - type: mrr_at_100
+      value: 29.755
+    - type: mrr_at_1000
+      value: 29.817
+    - type: mrr_at_3
+      value: 26.201999999999998
+    - type: mrr_at_5
+      value: 27.714
+    - type: ndcg_at_1
+      value: 19.776
+    - type: ndcg_at_10
+      value: 29.701
+    - type: ndcg_at_100
+      value: 35.307
+    - type: ndcg_at_1000
+      value: 37.942
+    - type: ndcg_at_3
+      value: 24.764
+    - type: ndcg_at_5
+      value: 27.025
+    - type: precision_at_1
+      value: 19.776
+    - type: precision_at_10
+      value: 5.659
+    - type: precision_at_100
+      value: 0.971
+    - type: precision_at_1000
+      value: 0.133
+    - type: precision_at_3
+      value: 12.065
+    - type: precision_at_5
+      value: 8.905000000000001
+    - type: recall_at_1
+      value: 16.073999999999998
+    - type: recall_at_10
+      value: 41.647
+    - type: recall_at_100
+      value: 66.884
+    - type: recall_at_1000
+      value: 85.91499999999999
+    - type: recall_at_3
+      value: 27.916
+    - type: recall_at_5
+      value: 33.729
+    - type: map_at_1
+      value: 28.444999999999997
+    - type: map_at_10
+      value: 38.218999999999994
+    - type: map_at_100
+      value: 39.595
+    - type: map_at_1000
+      value: 39.709
+    - type: map_at_3
+      value: 35.586
+    - type: map_at_5
+      value: 36.895
+    - type: mrr_at_1
+      value: 34.841
+    - type: mrr_at_10
+      value: 44.106
+    - type: mrr_at_100
+      value: 44.98
+    - type: mrr_at_1000
+      value: 45.03
+    - type: mrr_at_3
+      value: 41.979
+    - type: mrr_at_5
+      value: 43.047999999999995
+    - type: ndcg_at_1
+      value: 34.841
+    - type: ndcg_at_10
+      value: 43.922
+    - type: ndcg_at_100
+      value: 49.504999999999995
+    - type: ndcg_at_1000
+      value: 51.675000000000004
+    - type: ndcg_at_3
+      value: 39.858
+    - type: ndcg_at_5
+      value: 41.408
+    - type: precision_at_1
+      value: 34.841
+    - type: precision_at_10
+      value: 7.872999999999999
+    - type: precision_at_100
+      value: 1.2449999999999999
+    - type: precision_at_1000
+      value: 0.161
+    - type: precision_at_3
+      value: 18.993
+    - type: precision_at_5
+      value: 13.032
+    - type: recall_at_1
+      value: 28.444999999999997
+    - type: recall_at_10
+      value: 54.984
+    - type: recall_at_100
+      value: 78.342
+    - type: recall_at_1000
+      value: 92.77
+    - type: recall_at_3
+      value: 42.842999999999996
+    - type: recall_at_5
+      value: 47.247
+    - type: map_at_1
+      value: 23.072
+    - type: map_at_10
+      value: 32.354
+    - type: map_at_100
+      value: 33.800000000000004
+    - type: map_at_1000
+      value: 33.908
+    - type: map_at_3
+      value: 29.232000000000003
+    - type: map_at_5
+      value: 31.049
+    - type: mrr_at_1
+      value: 29.110000000000003
+    - type: mrr_at_10
+      value: 38.03
+    - type: mrr_at_100
+      value: 39.032
+    - type: mrr_at_1000
+      value: 39.086999999999996
+    - type: mrr_at_3
+      value: 35.407
+    - type: mrr_at_5
+      value: 36.76
+    - type: ndcg_at_1
+      value: 29.110000000000003
+    - type: ndcg_at_10
+      value: 38.231
+    - type: ndcg_at_100
+      value: 44.425
+    - type: ndcg_at_1000
+      value: 46.771
+    - type: ndcg_at_3
+      value: 33.095
+    - type: ndcg_at_5
+      value: 35.459
+    - type: precision_at_1
+      value: 29.110000000000003
+    - type: precision_at_10
+      value: 7.215000000000001
+    - type: precision_at_100
+      value: 1.2109999999999999
+    - type: precision_at_1000
+      value: 0.157
+    - type: precision_at_3
+      value: 16.058
+    - type: precision_at_5
+      value: 11.644
+    - type: recall_at_1
+      value: 23.072
+    - type: recall_at_10
+      value: 50.285999999999994
+    - type: recall_at_100
+      value: 76.596
+    - type: recall_at_1000
+      value: 92.861
+    - type: recall_at_3
+      value: 35.702
+    - type: recall_at_5
+      value: 42.152
+    - type: map_at_1
+      value: 24.937916666666666
+    - type: map_at_10
+      value: 33.755250000000004
+    - type: map_at_100
+      value: 34.955999999999996
+    - type: map_at_1000
+      value: 35.070499999999996
+    - type: map_at_3
+      value: 30.98708333333333
+    - type: map_at_5
+      value: 32.51491666666666
+    - type: mrr_at_1
+      value: 29.48708333333333
+    - type: mrr_at_10
+      value: 37.92183333333334
+    - type: mrr_at_100
+      value: 38.76583333333333
+    - type: mrr_at_1000
+      value: 38.82466666666667
+    - type: mrr_at_3
+      value: 35.45125
+    - type: mrr_at_5
+      value: 36.827000000000005
+    - type: ndcg_at_1
+      value: 29.48708333333333
+    - type: ndcg_at_10
+      value: 39.05225
+    - type: ndcg_at_100
+      value: 44.25983333333334
+    - type: ndcg_at_1000
+      value: 46.568333333333335
+    - type: ndcg_at_3
+      value: 34.271583333333325
+    - type: ndcg_at_5
+      value: 36.483916666666666
+    - type: precision_at_1
+      value: 29.48708333333333
+    - type: precision_at_10
+      value: 6.865749999999999
+    - type: precision_at_100
+      value: 1.1195833333333332
+    - type: precision_at_1000
+      value: 0.15058333333333335
+    - type: precision_at_3
+      value: 15.742083333333333
+    - type: precision_at_5
+      value: 11.221916666666667
+    - type: recall_at_1
+      value: 24.937916666666666
+    - type: recall_at_10
+      value: 50.650416666666665
+    - type: recall_at_100
+      value: 73.55383333333334
+    - type: recall_at_1000
+      value: 89.61691666666667
+    - type: recall_at_3
+      value: 37.27808333333334
+    - type: recall_at_5
+      value: 42.99475
+    - type: map_at_1
+      value: 23.947
+    - type: map_at_10
+      value: 30.575000000000003
+    - type: map_at_100
+      value: 31.465
+    - type: map_at_1000
+      value: 31.558000000000003
+    - type: map_at_3
+      value: 28.814
+    - type: map_at_5
+      value: 29.738999999999997
+    - type: mrr_at_1
+      value: 26.994
+    - type: mrr_at_10
+      value: 33.415
+    - type: mrr_at_100
+      value: 34.18
+    - type: mrr_at_1000
+      value: 34.245
+    - type: mrr_at_3
+      value: 31.621
+    - type: mrr_at_5
+      value: 32.549
+    - type: ndcg_at_1
+      value: 26.994
+    - type: ndcg_at_10
+      value: 34.482
+    - type: ndcg_at_100
+      value: 38.915
+    - type: ndcg_at_1000
+      value: 41.355
+    - type: ndcg_at_3
+      value: 31.139
+    - type: ndcg_at_5
+      value: 32.589
+    - type: precision_at_1
+      value: 26.994
+    - type: precision_at_10
+      value: 5.322
+    - type: precision_at_100
+      value: 0.8160000000000001
+    - type: precision_at_1000
+      value: 0.11100000000000002
+    - type: precision_at_3
+      value: 13.344000000000001
+    - type: precision_at_5
+      value: 8.988
+    - type: recall_at_1
+      value: 23.947
+    - type: recall_at_10
+      value: 43.647999999999996
+    - type: recall_at_100
+      value: 63.851
+    - type: recall_at_1000
+      value: 82.0
+    - type: recall_at_3
+      value: 34.288000000000004
+    - type: recall_at_5
+      value: 38.117000000000004
+    - type: map_at_1
+      value: 16.197
+    - type: map_at_10
+      value: 22.968
+    - type: map_at_100
+      value: 24.095
+    - type: map_at_1000
+      value: 24.217
+    - type: map_at_3
+      value: 20.771
+    - type: map_at_5
+      value: 21.995
+    - type: mrr_at_1
+      value: 19.511
+    - type: mrr_at_10
+      value: 26.55
+    - type: mrr_at_100
+      value: 27.500999999999998
+    - type: mrr_at_1000
+      value: 27.578999999999997
+    - type: mrr_at_3
+      value: 24.421
+    - type: mrr_at_5
+      value: 25.604
+    - type: ndcg_at_1
+      value: 19.511
+    - type: ndcg_at_10
+      value: 27.386
+    - type: ndcg_at_100
+      value: 32.828
+    - type: ndcg_at_1000
+      value: 35.739
+    - type: ndcg_at_3
+      value: 23.405
+    - type: ndcg_at_5
+      value: 25.255
+    - type: precision_at_1
+      value: 19.511
+    - type: precision_at_10
+      value: 5.017
+    - type: precision_at_100
+      value: 0.91
+    - type: precision_at_1000
+      value: 0.133
+    - type: precision_at_3
+      value: 11.023
+    - type: precision_at_5
+      value: 8.025
+    - type: recall_at_1
+      value: 16.197
+    - type: recall_at_10
+      value: 37.09
+    - type: recall_at_100
+      value: 61.778
+    - type: recall_at_1000
+      value: 82.56599999999999
+    - type: recall_at_3
+      value: 26.034000000000002
+    - type: recall_at_5
+      value: 30.762
+    - type: map_at_1
+      value: 25.41
+    - type: map_at_10
+      value: 33.655
+    - type: map_at_100
+      value: 34.892
+    - type: map_at_1000
+      value: 34.995
+    - type: map_at_3
+      value: 30.94
+    - type: map_at_5
+      value: 32.303
+    - type: mrr_at_1
+      value: 29.477999999999998
+    - type: mrr_at_10
+      value: 37.443
+    - type: mrr_at_100
+      value: 38.383
+    - type: mrr_at_1000
+      value: 38.440000000000005
+    - type: mrr_at_3
+      value: 34.949999999999996
+    - type: mrr_at_5
+      value: 36.228
+    - type: ndcg_at_1
+      value: 29.477999999999998
+    - type: ndcg_at_10
+      value: 38.769
+    - type: ndcg_at_100
+      value: 44.245000000000005
+    - type: ndcg_at_1000
+      value: 46.593
+    - type: ndcg_at_3
+      value: 33.623
+    - type: ndcg_at_5
+      value: 35.766
+    - type: precision_at_1
+      value: 29.477999999999998
+    - type: precision_at_10
+      value: 6.455
+    - type: precision_at_100
+      value: 1.032
+    - type: precision_at_1000
+      value: 0.135
+    - type: precision_at_3
+      value: 14.893999999999998
+    - type: precision_at_5
+      value: 10.485
+    - type: recall_at_1
+      value: 25.41
+    - type: recall_at_10
+      value: 50.669
+    - type: recall_at_100
+      value: 74.084
+    - type: recall_at_1000
+      value: 90.435
+    - type: recall_at_3
+      value: 36.679
+    - type: recall_at_5
+      value: 41.94
+    - type: map_at_1
+      value: 23.339
+    - type: map_at_10
+      value: 31.852000000000004
+    - type: map_at_100
+      value: 33.411
+    - type: map_at_1000
+      value: 33.62
+    - type: map_at_3
+      value: 28.929
+    - type: map_at_5
+      value: 30.542
+    - type: mrr_at_1
+      value: 28.063
+    - type: mrr_at_10
+      value: 36.301
+    - type: mrr_at_100
+      value: 37.288
+    - type: mrr_at_1000
+      value: 37.349
+    - type: mrr_at_3
+      value: 33.663
+    - type: mrr_at_5
+      value: 35.165
+    - type: ndcg_at_1
+      value: 28.063
+    - type: ndcg_at_10
+      value: 37.462
+    - type: ndcg_at_100
+      value: 43.620999999999995
+    - type: ndcg_at_1000
+      value: 46.211
+    - type: ndcg_at_3
+      value: 32.68
+    - type: ndcg_at_5
+      value: 34.981
+    - type: precision_at_1
+      value: 28.063
+    - type: precision_at_10
+      value: 7.1739999999999995
+    - type: precision_at_100
+      value: 1.486
+    - type: precision_at_1000
+      value: 0.23500000000000001
+    - type: precision_at_3
+      value: 15.217
+    - type: precision_at_5
+      value: 11.265
+    - type: recall_at_1
+      value: 23.339
+    - type: recall_at_10
+      value: 48.376999999999995
+    - type: recall_at_100
+      value: 76.053
+    - type: recall_at_1000
+      value: 92.455
+    - type: recall_at_3
+      value: 34.735
+    - type: recall_at_5
+      value: 40.71
+    - type: map_at_1
+      value: 18.925
+    - type: map_at_10
+      value: 26.017000000000003
+    - type: map_at_100
+      value: 27.034000000000002
+    - type: map_at_1000
+      value: 27.156000000000002
+    - type: map_at_3
+      value: 23.604
+    - type: map_at_5
+      value: 24.75
+    - type: mrr_at_1
+      value: 20.333000000000002
+    - type: mrr_at_10
+      value: 27.915
+    - type: mrr_at_100
+      value: 28.788000000000004
+    - type: mrr_at_1000
+      value: 28.877999999999997
+    - type: mrr_at_3
+      value: 25.446999999999996
+    - type: mrr_at_5
+      value: 26.648
+    - type: ndcg_at_1
+      value: 20.333000000000002
+    - type: ndcg_at_10
+      value: 30.673000000000002
+    - type: ndcg_at_100
+      value: 35.618
+    - type: ndcg_at_1000
+      value: 38.517
+    - type: ndcg_at_3
+      value: 25.71
+    - type: ndcg_at_5
+      value: 27.679
+    - type: precision_at_1
+      value: 20.333000000000002
+    - type: precision_at_10
+      value: 4.9910000000000005
+    - type: precision_at_100
+      value: 0.8130000000000001
+    - type: precision_at_1000
+      value: 0.117
+    - type: precision_at_3
+      value: 11.029
+    - type: precision_at_5
+      value: 7.8740000000000006
+    - type: recall_at_1
+      value: 18.925
+    - type: recall_at_10
+      value: 43.311
+    - type: recall_at_100
+      value: 66.308
+    - type: recall_at_1000
+      value: 87.49
+    - type: recall_at_3
+      value: 29.596
+    - type: recall_at_5
+      value: 34.245
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB ClimateFEVER
+      revision: None
+      split: test
+      type: climate-fever
+    metrics:
+    - type: map_at_1
+      value: 13.714
+    - type: map_at_10
+      value: 23.194
+    - type: map_at_100
+      value: 24.976000000000003
+    - type: map_at_1000
+      value: 25.166
+    - type: map_at_3
+      value: 19.709
+    - type: map_at_5
+      value: 21.523999999999997
+    - type: mrr_at_1
+      value: 30.619000000000003
+    - type: mrr_at_10
+      value: 42.563
+    - type: mrr_at_100
+      value: 43.386
+    - type: mrr_at_1000
+      value: 43.423
+    - type: mrr_at_3
+      value: 39.555
+    - type: mrr_at_5
+      value: 41.268
+    - type: ndcg_at_1
+      value: 30.619000000000003
+    - type: ndcg_at_10
+      value: 31.836
+    - type: ndcg_at_100
+      value: 38.652
+    - type: ndcg_at_1000
+      value: 42.088
+    - type: ndcg_at_3
+      value: 26.733
+    - type: ndcg_at_5
+      value: 28.435
+    - type: precision_at_1
+      value: 30.619000000000003
+    - type: precision_at_10
+      value: 9.751999999999999
+    - type: precision_at_100
+      value: 1.71
+    - type: precision_at_1000
+      value: 0.23500000000000001
+    - type: precision_at_3
+      value: 19.935
+    - type: precision_at_5
+      value: 14.984
+    - type: recall_at_1
+      value: 13.714
+    - type: recall_at_10
+      value: 37.26
+    - type: recall_at_100
+      value: 60.546
+    - type: recall_at_1000
+      value: 79.899
+    - type: recall_at_3
+      value: 24.325
+    - type: recall_at_5
+      value: 29.725
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB DBPedia
+      revision: None
+      split: test
+      type: dbpedia-entity
+    metrics:
+    - type: map_at_1
+      value: 8.462
+    - type: map_at_10
+      value: 18.637
+    - type: map_at_100
+      value: 26.131999999999998
+    - type: map_at_1000
+      value: 27.607
+    - type: map_at_3
+      value: 13.333
+    - type: map_at_5
+      value: 15.654000000000002
+    - type: mrr_at_1
+      value: 66.25
+    - type: mrr_at_10
+      value: 74.32600000000001
+    - type: mrr_at_100
+      value: 74.60900000000001
+    - type: mrr_at_1000
+      value: 74.62
+    - type: mrr_at_3
+      value: 72.667
+    - type: mrr_at_5
+      value: 73.817
+    - type: ndcg_at_1
+      value: 53.87499999999999
+    - type: ndcg_at_10
+      value: 40.028999999999996
+    - type: ndcg_at_100
+      value: 44.199
+    - type: ndcg_at_1000
+      value: 51.629999999999995
+    - type: ndcg_at_3
+      value: 44.113
+    - type: ndcg_at_5
+      value: 41.731
+    - type: precision_at_1
+      value: 66.25
+    - type: precision_at_10
+      value: 31.900000000000002
+    - type: precision_at_100
+      value: 10.043000000000001
+    - type: precision_at_1000
+      value: 1.926
+    - type: precision_at_3
+      value: 47.417
+    - type: precision_at_5
+      value: 40.65
+    - type: recall_at_1
+      value: 8.462
+    - type: recall_at_10
+      value: 24.293
+    - type: recall_at_100
+      value: 50.146
+    - type: recall_at_1000
+      value: 74.034
+    - type: recall_at_3
+      value: 14.967
+    - type: recall_at_5
+      value: 18.682000000000002
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB EmotionClassification
+      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
+      split: test
+      type: mteb/emotion
+    metrics:
+    - type: accuracy
+      value: 47.84499999999999
+    - type: f1
+      value: 42.48106691979349
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB FEVER
+      revision: None
+      split: test
+      type: fever
+    metrics:
+    - type: map_at_1
+      value: 74.034
+    - type: map_at_10
+      value: 82.76
+    - type: map_at_100
+      value: 82.968
+    - type: map_at_1000
+      value: 82.98299999999999
+    - type: map_at_3
+      value: 81.768
+    - type: map_at_5
+      value: 82.418
+    - type: mrr_at_1
+      value: 80.048
+    - type: mrr_at_10
+      value: 87.64999999999999
+    - type: mrr_at_100
+      value: 87.712
+    - type: mrr_at_1000
+      value: 87.713
+    - type: mrr_at_3
+      value: 87.01100000000001
+    - type: mrr_at_5
+      value: 87.466
+    - type: ndcg_at_1
+      value: 80.048
+    - type: ndcg_at_10
+      value: 86.643
+    - type: ndcg_at_100
+      value: 87.361
+    - type: ndcg_at_1000
+      value: 87.606
+    - type: ndcg_at_3
+      value: 85.137
+    - type: ndcg_at_5
+      value: 86.016
+    - type: precision_at_1
+      value: 80.048
+    - type: precision_at_10
+      value: 10.372
+    - type: precision_at_100
+      value: 1.093
+    - type: precision_at_1000
+      value: 0.11299999999999999
+    - type: precision_at_3
+      value: 32.638
+    - type: precision_at_5
+      value: 20.177
+    - type: recall_at_1
+      value: 74.034
+    - type: recall_at_10
+      value: 93.769
+    - type: recall_at_100
+      value: 96.569
+    - type: recall_at_1000
+      value: 98.039
+    - type: recall_at_3
+      value: 89.581
+    - type: recall_at_5
+      value: 91.906
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB FiQA2018
+      revision: None
+      split: test
+      type: fiqa
+    metrics:
+    - type: map_at_1
+      value: 20.5
+    - type: map_at_10
+      value: 32.857
+    - type: map_at_100
+      value: 34.589
+    - type: map_at_1000
+      value: 34.778
+    - type: map_at_3
+      value: 29.160999999999998
+    - type: map_at_5
+      value: 31.033
+    - type: mrr_at_1
+      value: 40.123
+    - type: mrr_at_10
+      value: 48.776
+    - type: mrr_at_100
+      value: 49.495
+    - type: mrr_at_1000
+      value: 49.539
+    - type: mrr_at_3
+      value: 46.605000000000004
+    - type: mrr_at_5
+      value: 47.654
+    - type: ndcg_at_1
+      value: 40.123
+    - type: ndcg_at_10
+      value: 40.343
+    - type: ndcg_at_100
+      value: 46.56
+    - type: ndcg_at_1000
+      value: 49.777
+    - type: ndcg_at_3
+      value: 37.322
+    - type: ndcg_at_5
+      value: 37.791000000000004
+    - type: precision_at_1
+      value: 40.123
+    - type: precision_at_10
+      value: 11.08
+    - type: precision_at_100
+      value: 1.752
+    - type: precision_at_1000
+      value: 0.232
+    - type: precision_at_3
+      value: 24.897
+    - type: precision_at_5
+      value: 17.809
+    - type: recall_at_1
+      value: 20.5
+    - type: recall_at_10
+      value: 46.388
+    - type: recall_at_100
+      value: 69.552
+    - type: recall_at_1000
+      value: 89.011
+    - type: recall_at_3
+      value: 33.617999999999995
+    - type: recall_at_5
+      value: 38.211
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB HotpotQA
+      revision: None
+      split: test
+      type: hotpotqa
+    metrics:
+    - type: map_at_1
+      value: 39.135999999999996
+    - type: map_at_10
+      value: 61.673
+    - type: map_at_100
+      value: 62.562
+    - type: map_at_1000
+      value: 62.62
+    - type: map_at_3
+      value: 58.467999999999996
+    - type: map_at_5
+      value: 60.463
+    - type: mrr_at_1
+      value: 78.271
+    - type: mrr_at_10
+      value: 84.119
+    - type: mrr_at_100
+      value: 84.29299999999999
+    - type: mrr_at_1000
+      value: 84.299
+    - type: mrr_at_3
+      value: 83.18900000000001
+    - type: mrr_at_5
+      value: 83.786
+    - type: ndcg_at_1
+      value: 78.271
+    - type: ndcg_at_10
+      value: 69.935
+    - type: ndcg_at_100
+      value: 73.01299999999999
+    - type: ndcg_at_1000
+      value: 74.126
+    - type: ndcg_at_3
+      value: 65.388
+    - type: ndcg_at_5
+      value: 67.906
+    - type: precision_at_1
+      value: 78.271
+    - type: precision_at_10
+      value: 14.562
+    - type: precision_at_100
+      value: 1.6969999999999998
+    - type: precision_at_1000
+      value: 0.184
+    - type: precision_at_3
+      value: 41.841
+    - type: precision_at_5
+      value: 27.087
+    - type: recall_at_1
+      value: 39.135999999999996
+    - type: recall_at_10
+      value: 72.809
+    - type: recall_at_100
+      value: 84.86200000000001
+    - type: recall_at_1000
+      value: 92.208
+    - type: recall_at_3
+      value: 62.76199999999999
+    - type: recall_at_5
+      value: 67.718
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB ImdbClassification
+      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
+      split: test
+      type: mteb/imdb
+    metrics:
+    - type: accuracy
+      value: 90.60600000000001
+    - type: ap
+      value: 86.6579587804335
+    - type: f1
+      value: 90.5938853929307
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB MSMARCO
+      revision: None
+      split: dev
+      type: msmarco
+    metrics:
+    - type: map_at_1
+      value: 21.852
+    - type: map_at_10
+      value: 33.982
+    - type: map_at_100
+      value: 35.116
+    - type: map_at_1000
+      value: 35.167
+    - type: map_at_3
+      value: 30.134
+    - type: map_at_5
+      value: 32.340999999999994
+    - type: mrr_at_1
+      value: 22.479
+    - type: mrr_at_10
+      value: 34.594
+    - type: mrr_at_100
+      value: 35.672
+    - type: mrr_at_1000
+      value: 35.716
+    - type: mrr_at_3
+      value: 30.84
+    - type: mrr_at_5
+      value: 32.998
+    - type: ndcg_at_1
+      value: 22.493
+    - type: ndcg_at_10
+      value: 40.833000000000006
+    - type: ndcg_at_100
+      value: 46.357
+    - type: ndcg_at_1000
+      value: 47.637
+    - type: ndcg_at_3
+      value: 32.995999999999995
+    - type: ndcg_at_5
+      value: 36.919000000000004
+    - type: precision_at_1
+      value: 22.493
+    - type: precision_at_10
+      value: 6.465999999999999
+    - type: precision_at_100
+      value: 0.9249999999999999
+    - type: precision_at_1000
+      value: 0.104
+    - type: precision_at_3
+      value: 14.030999999999999
+    - type: precision_at_5
+      value: 10.413
+    - type: recall_at_1
+      value: 21.852
+    - type: recall_at_10
+      value: 61.934999999999995
+    - type: recall_at_100
+      value: 87.611
+    - type: recall_at_1000
+      value: 97.441
+    - type: recall_at_3
+      value: 40.583999999999996
+    - type: recall_at_5
+      value: 49.992999999999995
+    task:
+      type: Retrieval
+  - dataset:
+      config: en
+      name: MTEB MTOPDomainClassification (en)
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+      split: test
+      type: mteb/mtop_domain
+    metrics:
+    - type: accuracy
+      value: 93.36069311445507
+    - type: f1
+      value: 93.16456330371453
+    task:
+      type: Classification
+  - dataset:
+      config: en
+      name: MTEB MTOPIntentClassification (en)
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+      split: test
+      type: mteb/mtop_intent
+    metrics:
+    - type: accuracy
+      value: 74.74692202462381
+    - type: f1
+      value: 58.17903579421599
+    task:
+      type: Classification
+  - dataset:
+      config: en
+      name: MTEB MassiveIntentClassification (en)
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+      split: test
+      type: mteb/amazon_massive_intent
+    metrics:
+    - type: accuracy
+      value: 74.80833893745796
+    - type: f1
+      value: 72.70786592684664
+    task:
+      type: Classification
+  - dataset:
+      config: en
+      name: MTEB MassiveScenarioClassification (en)
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+      split: test
+      type: mteb/amazon_massive_scenario
+    metrics:
+    - type: accuracy
+      value: 78.69872225958305
+    - type: f1
+      value: 78.61626934504731
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB MedrxivClusteringP2P
+      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
+      split: test
+      type: mteb/medrxiv-clustering-p2p
+    metrics:
+    - type: v_measure
+      value: 33.058658628717694
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB MedrxivClusteringS2S
+      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
+      split: test
+      type: mteb/medrxiv-clustering-s2s
+    metrics:
+    - type: v_measure
+      value: 30.85561739360599
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB MindSmallReranking
+      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
+      split: test
+      type: mteb/mind_small
+    metrics:
+    - type: map
+      value: 31.290259910144385
+    - type: mrr
+      value: 32.44223046102856
+    task:
+      type: Reranking
+  - dataset:
+      config: default
+      name: MTEB NFCorpus
+      revision: None
+      split: test
+      type: nfcorpus
+    metrics:
+    - type: map_at_1
+      value: 5.288
+    - type: map_at_10
+      value: 12.267999999999999
+    - type: map_at_100
+      value: 15.557000000000002
+    - type: map_at_1000
+      value: 16.98
+    - type: map_at_3
+      value: 8.866
+    - type: map_at_5
+      value: 10.418
+    - type: mrr_at_1
+      value: 43.653
+    - type: mrr_at_10
+      value: 52.681
+    - type: mrr_at_100
+      value: 53.315999999999995
+    - type: mrr_at_1000
+      value: 53.357
+    - type: mrr_at_3
+      value: 51.393
+    - type: mrr_at_5
+      value: 51.903999999999996
+    - type: ndcg_at_1
+      value: 42.415000000000006
+    - type: ndcg_at_10
+      value: 34.305
+    - type: ndcg_at_100
+      value: 30.825999999999997
+    - type: ndcg_at_1000
+      value: 39.393
+    - type: ndcg_at_3
+      value: 39.931
+    - type: ndcg_at_5
+      value: 37.519999999999996
+    - type: precision_at_1
+      value: 43.653
+    - type: precision_at_10
+      value: 25.728
+    - type: precision_at_100
+      value: 7.932
+    - type: precision_at_1000
+      value: 2.07
+    - type: precision_at_3
+      value: 38.184000000000005
+    - type: precision_at_5
+      value: 32.879000000000005
+    - type: recall_at_1
+      value: 5.288
+    - type: recall_at_10
+      value: 16.195
+    - type: recall_at_100
+      value: 31.135
+    - type: recall_at_1000
+      value: 61.531000000000006
+    - type: recall_at_3
+      value: 10.313
+    - type: recall_at_5
+      value: 12.754999999999999
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB NQ
+      revision: None
+      split: test
+      type: nq
+    metrics:
+    - type: map_at_1
+      value: 28.216
+    - type: map_at_10
+      value: 42.588
+    - type: map_at_100
+      value: 43.702999999999996
+    - type: map_at_1000
+      value: 43.739
+    - type: map_at_3
+      value: 38.177
+    - type: map_at_5
+      value: 40.754000000000005
+    - type: mrr_at_1
+      value: 31.866
+    - type: mrr_at_10
+      value: 45.189
+    - type: mrr_at_100
+      value: 46.056000000000004
+    - type: mrr_at_1000
+      value: 46.081
+    - type: mrr_at_3
+      value: 41.526999999999994
+    - type: mrr_at_5
+      value: 43.704
+    - type: ndcg_at_1
+      value: 31.837
+    - type: ndcg_at_10
+      value: 50.178
+    - type: ndcg_at_100
+      value: 54.98800000000001
+    - type: ndcg_at_1000
+      value: 55.812
+    - type: ndcg_at_3
+      value: 41.853
+    - type: ndcg_at_5
+      value: 46.153
+    - type: precision_at_1
+      value: 31.837
+    - type: precision_at_10
+      value: 8.43
+    - type: precision_at_100
+      value: 1.1119999999999999
+    - type: precision_at_1000
+      value: 0.11900000000000001
+    - type: precision_at_3
+      value: 19.023
+    - type: precision_at_5
+      value: 13.911000000000001
+    - type: recall_at_1
+      value: 28.216
+    - type: recall_at_10
+      value: 70.8
+    - type: recall_at_100
+      value: 91.857
+    - type: recall_at_1000
+      value: 97.941
+    - type: recall_at_3
+      value: 49.196
+    - type: recall_at_5
+      value: 59.072
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB QuoraRetrieval
+      revision: None
+      split: test
+      type: quora
+    metrics:
+    - type: map_at_1
+      value: 71.22800000000001
+    - type: map_at_10
+      value: 85.115
+    - type: map_at_100
+      value: 85.72
+    - type: map_at_1000
+      value: 85.737
+    - type: map_at_3
+      value: 82.149
+    - type: map_at_5
+      value: 84.029
+    - type: mrr_at_1
+      value: 81.96
+    - type: mrr_at_10
+      value: 88.00200000000001
+    - type: mrr_at_100
+      value: 88.088
+    - type: mrr_at_1000
+      value: 88.089
+    - type: mrr_at_3
+      value: 87.055
+    - type: mrr_at_5
+      value: 87.715
+    - type: ndcg_at_1
+      value: 82.01
+    - type: ndcg_at_10
+      value: 88.78
+    - type: ndcg_at_100
+      value: 89.91
+    - type: ndcg_at_1000
+      value: 90.013
+    - type: ndcg_at_3
+      value: 85.957
+    - type: ndcg_at_5
+      value: 87.56
+    - type: precision_at_1
+      value: 82.01
+    - type: precision_at_10
+      value: 13.462
+    - type: precision_at_100
+      value: 1.528
+    - type: precision_at_1000
+      value: 0.157
+    - type: precision_at_3
+      value: 37.553
+    - type: precision_at_5
+      value: 24.732000000000003
+    - type: recall_at_1
+      value: 71.22800000000001
+    - type: recall_at_10
+      value: 95.69
+    - type: recall_at_100
+      value: 99.531
+    - type: recall_at_1000
+      value: 99.98
+    - type: recall_at_3
+      value: 87.632
+    - type: recall_at_5
+      value: 92.117
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB RedditClustering
+      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
+      split: test
+      type: mteb/reddit-clustering
+    metrics:
+    - type: v_measure
+      value: 52.31768034366916
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB RedditClusteringP2P
+      revision: 282350215ef01743dc01b456c7f5241fa8937f16
+      split: test
+      type: mteb/reddit-clustering-p2p
+    metrics:
+    - type: v_measure
+      value: 60.640266772723606
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB SCIDOCS
+      revision: None
+      split: test
+      type: scidocs
+    metrics:
+    - type: map_at_1
+      value: 4.7780000000000005
+    - type: map_at_10
+      value: 12.299
+    - type: map_at_100
+      value: 14.363000000000001
+    - type: map_at_1000
+      value: 14.71
+    - type: map_at_3
+      value: 8.738999999999999
+    - type: map_at_5
+      value: 10.397
+    - type: mrr_at_1
+      value: 23.599999999999998
+    - type: mrr_at_10
+      value: 34.845
+    - type: mrr_at_100
+      value: 35.916
+    - type: mrr_at_1000
+      value: 35.973
+    - type: mrr_at_3
+      value: 31.7
+    - type: mrr_at_5
+      value: 33.535
+    - type: ndcg_at_1
+      value: 23.599999999999998
+    - type: ndcg_at_10
+      value: 20.522000000000002
+    - type: ndcg_at_100
+      value: 28.737000000000002
+    - type: ndcg_at_1000
+      value: 34.596
+    - type: ndcg_at_3
+      value: 19.542
+    - type: ndcg_at_5
+      value: 16.958000000000002
+    - type: precision_at_1
+      value: 23.599999999999998
+    - type: precision_at_10
+      value: 10.67
+    - type: precision_at_100
+      value: 2.259
+    - type: precision_at_1000
+      value: 0.367
+    - type: precision_at_3
+      value: 18.333
+    - type: precision_at_5
+      value: 14.879999999999999
+    - type: recall_at_1
+      value: 4.7780000000000005
+    - type: recall_at_10
+      value: 21.617
+    - type: recall_at_100
+      value: 45.905
+    - type: recall_at_1000
+      value: 74.42
+    - type: recall_at_3
+      value: 11.148
+    - type: recall_at_5
+      value: 15.082999999999998
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB SICK-R
+      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
+      split: test
+      type: mteb/sickr-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 83.22372750297885
+    - type: cos_sim_spearman
+      value: 79.40972617119405
+    - type: euclidean_pearson
+      value: 80.6101072020434
+    - type: euclidean_spearman
+      value: 79.53844217225202
+    - type: manhattan_pearson
+      value: 80.57265975286111
+    - type: manhattan_spearman
+      value: 79.46335611792958
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STS12
+      revision: a0d554a64d88156834ff5ae9920b964011b16384
+      split: test
+      type: mteb/sts12-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 85.43713315520749
+    - type: cos_sim_spearman
+      value: 77.44128693329532
+    - type: euclidean_pearson
+      value: 81.63869928101123
+    - type: euclidean_spearman
+      value: 77.29512977961515
+    - type: manhattan_pearson
+      value: 81.63704185566183
+    - type: manhattan_spearman
+      value: 77.29909412738657
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STS13
+      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
+      split: test
+      type: mteb/sts13-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 81.59451537860527
+    - type: cos_sim_spearman
+      value: 82.97994638856723
+    - type: euclidean_pearson
+      value: 82.89478688288412
+    - type: euclidean_spearman
+      value: 83.58740751053104
+    - type: manhattan_pearson
+      value: 82.69140840941608
+    - type: manhattan_spearman
+      value: 83.33665956040555
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STS14
+      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
+      split: test
+      type: mteb/sts14-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 82.00756527711764
+    - type: cos_sim_spearman
+      value: 81.83560996841379
+    - type: euclidean_pearson
+      value: 82.07684151976518
+    - type: euclidean_spearman
+      value: 82.00913052060511
+    - type: manhattan_pearson
+      value: 82.05690778488794
+    - type: manhattan_spearman
+      value: 82.02260252019525
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STS15
+      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
+      split: test
+      type: mteb/sts15-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 86.13710262895447
+    - type: cos_sim_spearman
+      value: 87.26412811156248
+    - type: euclidean_pearson
+      value: 86.94151453230228
+    - type: euclidean_spearman
+      value: 87.5363796699571
+    - type: manhattan_pearson
+      value: 86.86989424083748
+    - type: manhattan_spearman
+      value: 87.47315940781353
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STS16
+      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
+      split: test
+      type: mteb/sts16-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 83.0230597603627
+    - type: cos_sim_spearman
+      value: 84.93344499318864
+    - type: euclidean_pearson
+      value: 84.23754743431141
+    - type: euclidean_spearman
+      value: 85.09707376597099
+    - type: manhattan_pearson
+      value: 84.04325160987763
+    - type: manhattan_spearman
+      value: 84.89353071339909
+    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: 86.75620824563921
+    - type: cos_sim_spearman
+      value: 87.15065513706398
+    - type: euclidean_pearson
+      value: 88.26281533633521
+    - type: euclidean_spearman
+      value: 87.51963738643983
+    - type: manhattan_pearson
+      value: 88.25599267618065
+    - type: manhattan_spearman
+      value: 87.58048736047483
+    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: 64.74645319195137
+    - type: cos_sim_spearman
+      value: 65.29996325037214
+    - type: euclidean_pearson
+      value: 67.04297794086443
+    - type: euclidean_spearman
+      value: 65.43841726694343
+    - type: manhattan_pearson
+      value: 67.39459955690904
+    - type: manhattan_spearman
+      value: 65.92864704413651
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STSBenchmark
+      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
+      split: test
+      type: mteb/stsbenchmark-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 84.31291020270801
+    - type: cos_sim_spearman
+      value: 85.86473738688068
+    - type: euclidean_pearson
+      value: 85.65537275064152
+    - type: euclidean_spearman
+      value: 86.13087454209642
+    - type: manhattan_pearson
+      value: 85.43946955047609
+    - type: manhattan_spearman
+      value: 85.91568175344916
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB SciDocsRR
+      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
+      split: test
+      type: mteb/scidocs-reranking
+    metrics:
+    - type: map
+      value: 85.93798118350695
+    - type: mrr
+      value: 95.93536274908824
+    task:
+      type: Reranking
+  - dataset:
+      config: default
+      name: MTEB SciFact
+      revision: None
+      split: test
+      type: scifact
+    metrics:
+    - type: map_at_1
+      value: 57.594
+    - type: map_at_10
+      value: 66.81899999999999
+    - type: map_at_100
+      value: 67.368
+    - type: map_at_1000
+      value: 67.4
+    - type: map_at_3
+      value: 64.061
+    - type: map_at_5
+      value: 65.47
+    - type: mrr_at_1
+      value: 60.667
+    - type: mrr_at_10
+      value: 68.219
+    - type: mrr_at_100
+      value: 68.655
+    - type: mrr_at_1000
+      value: 68.684
+    - type: mrr_at_3
+      value: 66.22200000000001
+    - type: mrr_at_5
+      value: 67.289
+    - type: ndcg_at_1
+      value: 60.667
+    - type: ndcg_at_10
+      value: 71.275
+    - type: ndcg_at_100
+      value: 73.642
+    - type: ndcg_at_1000
+      value: 74.373
+    - type: ndcg_at_3
+      value: 66.521
+    - type: ndcg_at_5
+      value: 68.581
+    - type: precision_at_1
+      value: 60.667
+    - type: precision_at_10
+      value: 9.433
+    - type: precision_at_100
+      value: 1.0699999999999998
+    - type: precision_at_1000
+      value: 0.11299999999999999
+    - type: precision_at_3
+      value: 25.556
+    - type: precision_at_5
+      value: 16.8
+    - type: recall_at_1
+      value: 57.594
+    - type: recall_at_10
+      value: 83.622
+    - type: recall_at_100
+      value: 94.167
+    - type: recall_at_1000
+      value: 99.667
+    - type: recall_at_3
+      value: 70.64399999999999
+    - type: recall_at_5
+      value: 75.983
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB SprintDuplicateQuestions
+      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
+      split: test
+      type: mteb/sprintduplicatequestions-pairclassification
+    metrics:
+    - type: cos_sim_accuracy
+      value: 99.85841584158416
+    - type: cos_sim_ap
+      value: 96.66996142314342
+    - type: cos_sim_f1
+      value: 92.83208020050125
+    - type: cos_sim_precision
+      value: 93.06532663316584
+    - type: cos_sim_recall
+      value: 92.60000000000001
+    - type: dot_accuracy
+      value: 99.85841584158416
+    - type: dot_ap
+      value: 96.6775307676576
+    - type: dot_f1
+      value: 92.69289729177312
+    - type: dot_precision
+      value: 94.77533960292581
+    - type: dot_recall
+      value: 90.7
+    - type: euclidean_accuracy
+      value: 99.86138613861387
+    - type: euclidean_ap
+      value: 96.6338454403108
+    - type: euclidean_f1
+      value: 92.92214357937311
+    - type: euclidean_precision
+      value: 93.96728016359918
+    - type: euclidean_recall
+      value: 91.9
+    - type: manhattan_accuracy
+      value: 99.86237623762376
+    - type: manhattan_ap
+      value: 96.60370449645053
+    - type: manhattan_f1
+      value: 92.91177970423253
+    - type: manhattan_precision
+      value: 94.7970863683663
+    - type: manhattan_recall
+      value: 91.10000000000001
+    - type: max_accuracy
+      value: 99.86237623762376
+    - type: max_ap
+      value: 96.6775307676576
+    - type: max_f1
+      value: 92.92214357937311
+    task:
+      type: PairClassification
+  - dataset:
+      config: default
+      name: MTEB StackExchangeClustering
+      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
+      split: test
+      type: mteb/stackexchange-clustering
+    metrics:
+    - type: v_measure
+      value: 60.77977058695198
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB StackExchangeClusteringP2P
+      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
+      split: test
+      type: mteb/stackexchange-clustering-p2p
+    metrics:
+    - type: v_measure
+      value: 35.2725272535638
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB StackOverflowDupQuestions
+      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
+      split: test
+      type: mteb/stackoverflowdupquestions-reranking
+    metrics:
+    - type: map
+      value: 53.64052466362125
+    - type: mrr
+      value: 54.533067014684654
+    task:
+      type: Reranking
+  - dataset:
+      config: default
+      name: MTEB SummEval
+      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
+      split: test
+      type: mteb/summeval
+    metrics:
+    - type: cos_sim_pearson
+      value: 30.677624219206578
+    - type: cos_sim_spearman
+      value: 30.121368518123447
+    - type: dot_pearson
+      value: 30.69870088041608
+    - type: dot_spearman
+      value: 29.61284927093751
+    task:
+      type: Summarization
+  - dataset:
+      config: default
+      name: MTEB TRECCOVID
+      revision: None
+      split: test
+      type: trec-covid
+    metrics:
+    - type: map_at_1
+      value: 0.22
+    - type: map_at_10
+      value: 1.855
+    - type: map_at_100
+      value: 9.885
+    - type: map_at_1000
+      value: 23.416999999999998
+    - type: map_at_3
+      value: 0.637
+    - type: map_at_5
+      value: 1.024
+    - type: mrr_at_1
+      value: 88.0
+    - type: mrr_at_10
+      value: 93.067
+    - type: mrr_at_100
+      value: 93.067
+    - type: mrr_at_1000
+      value: 93.067
+    - type: mrr_at_3
+      value: 92.667
+    - type: mrr_at_5
+      value: 93.067
+    - type: ndcg_at_1
+      value: 82.0
+    - type: ndcg_at_10
+      value: 75.899
+    - type: ndcg_at_100
+      value: 55.115
+    - type: ndcg_at_1000
+      value: 48.368
+    - type: ndcg_at_3
+      value: 79.704
+    - type: ndcg_at_5
+      value: 78.39699999999999
+    - type: precision_at_1
+      value: 88.0
+    - type: precision_at_10
+      value: 79.60000000000001
+    - type: precision_at_100
+      value: 56.06
+    - type: precision_at_1000
+      value: 21.206
+    - type: precision_at_3
+      value: 84.667
+    - type: precision_at_5
+      value: 83.2
+    - type: recall_at_1
+      value: 0.22
+    - type: recall_at_10
+      value: 2.078
+    - type: recall_at_100
+      value: 13.297
+    - type: recall_at_1000
+      value: 44.979
+    - type: recall_at_3
+      value: 0.6689999999999999
+    - type: recall_at_5
+      value: 1.106
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB Touche2020
+      revision: None
+      split: test
+      type: webis-touche2020
+    metrics:
+    - type: map_at_1
+      value: 2.258
+    - type: map_at_10
+      value: 10.439
+    - type: map_at_100
+      value: 16.89
+    - type: map_at_1000
+      value: 18.407999999999998
+    - type: map_at_3
+      value: 5.668
+    - type: map_at_5
+      value: 7.718
+    - type: mrr_at_1
+      value: 32.653
+    - type: mrr_at_10
+      value: 51.159
+    - type: mrr_at_100
+      value: 51.714000000000006
+    - type: mrr_at_1000
+      value: 51.714000000000006
+    - type: mrr_at_3
+      value: 47.959
+    - type: mrr_at_5
+      value: 50.407999999999994
+    - type: ndcg_at_1
+      value: 29.592000000000002
+    - type: ndcg_at_10
+      value: 26.037
+    - type: ndcg_at_100
+      value: 37.924
+    - type: ndcg_at_1000
+      value: 49.126999999999995
+    - type: ndcg_at_3
+      value: 30.631999999999998
+    - type: ndcg_at_5
+      value: 28.571
+    - type: precision_at_1
+      value: 32.653
+    - type: precision_at_10
+      value: 22.857
+    - type: precision_at_100
+      value: 7.754999999999999
+    - type: precision_at_1000
+      value: 1.529
+    - type: precision_at_3
+      value: 34.014
+    - type: precision_at_5
+      value: 29.796
+    - type: recall_at_1
+      value: 2.258
+    - type: recall_at_10
+      value: 16.554
+    - type: recall_at_100
+      value: 48.439
+    - type: recall_at_1000
+      value: 82.80499999999999
+    - type: recall_at_3
+      value: 7.283
+    - type: recall_at_5
+      value: 10.732
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB ToxicConversationsClassification
+      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
+      split: test
+      type: mteb/toxic_conversations_50k
+    metrics:
+    - type: accuracy
+      value: 69.8858
+    - type: ap
+      value: 13.835684144362109
+    - type: f1
+      value: 53.803351693244586
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB TweetSentimentExtractionClassification
+      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
+      split: test
+      type: mteb/tweet_sentiment_extraction
+    metrics:
+    - type: accuracy
+      value: 60.50650820599886
+    - type: f1
+      value: 60.84357825979259
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB TwentyNewsgroupsClustering
+      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
+      split: test
+      type: mteb/twentynewsgroups-clustering
+    metrics:
+    - type: v_measure
+      value: 48.52131044852134
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB TwitterSemEval2015
+      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
+      split: test
+      type: mteb/twittersemeval2015-pairclassification
+    metrics:
+    - type: cos_sim_accuracy
+      value: 85.59337187816654
+    - type: cos_sim_ap
+      value: 73.23925826533437
+    - type: cos_sim_f1
+      value: 67.34693877551021
+    - type: cos_sim_precision
+      value: 62.40432237730752
+    - type: cos_sim_recall
+      value: 73.13984168865434
+    - type: dot_accuracy
+      value: 85.31322644096085
+    - type: dot_ap
+      value: 72.30723963807422
+    - type: dot_f1
+      value: 66.47051612112296
+    - type: dot_precision
+      value: 62.0792305930845
+    - type: dot_recall
+      value: 71.53034300791556
+    - type: euclidean_accuracy
+      value: 85.61125350181797
+    - type: euclidean_ap
+      value: 73.32843720487845
+    - type: euclidean_f1
+      value: 67.36549633745895
+    - type: euclidean_precision
+      value: 64.60755813953489
+    - type: euclidean_recall
+      value: 70.36939313984169
+    - type: manhattan_accuracy
+      value: 85.63509566668654
+    - type: manhattan_ap
+      value: 73.16658488311325
+    - type: manhattan_f1
+      value: 67.20597386434349
+    - type: manhattan_precision
+      value: 63.60424028268551
+    - type: manhattan_recall
+      value: 71.2401055408971
+    - type: max_accuracy
+      value: 85.63509566668654
+    - type: max_ap
+      value: 73.32843720487845
+    - type: max_f1
+      value: 67.36549633745895
+    task:
+      type: PairClassification
+  - dataset:
+      config: default
+      name: MTEB TwitterURLCorpus
+      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
+      split: test
+      type: mteb/twitterurlcorpus-pairclassification
+    metrics:
+    - type: cos_sim_accuracy
+      value: 88.33779640625606
+    - type: cos_sim_ap
+      value: 84.83868375898157
+    - type: cos_sim_f1
+      value: 77.16506154017773
+    - type: cos_sim_precision
+      value: 74.62064005753327
+    - type: cos_sim_recall
+      value: 79.88912842623961
+    - type: dot_accuracy
+      value: 88.02732176815307
+    - type: dot_ap
+      value: 83.95089283763002
+    - type: dot_f1
+      value: 76.29635101196631
+    - type: dot_precision
+      value: 73.31771720613288
+    - type: dot_recall
+      value: 79.52725592854944
+    - type: euclidean_accuracy
+      value: 88.44452206310397
+    - type: euclidean_ap
+      value: 84.98384576824827
+    - type: euclidean_f1
+      value: 77.29311047696697
+    - type: euclidean_precision
+      value: 74.51232583065381
+    - type: euclidean_recall
+      value: 80.28949799815214
+    - type: manhattan_accuracy
+      value: 88.47362906042613
+    - type: manhattan_ap
+      value: 84.91421462218432
+    - type: manhattan_f1
+      value: 77.05107637204792
+    - type: manhattan_precision
+      value: 74.74484256243214
+    - type: manhattan_recall
+      value: 79.50415768401602
+    - type: max_accuracy
+      value: 88.47362906042613
+    - type: max_ap
+      value: 84.98384576824827
+    - type: max_f1
+      value: 77.29311047696697
+    task:
+      type: PairClassification
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- transformers
+- mteb
+- onnx
+- teradata
+
+---
+# A Teradata Vantage compatible Embeddings Model
+
+# BAAI/bge-small-en-v1.5
+
+## Overview of this Model
+
+An Embedding Model which maps text (sentence/ paragraphs) into a vector.  The [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) 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.
+
+- 33.36M params (Sizes in ONNX format - "fp32": 127.03MB,  "int8": 32.4MB, "uint8": 32.4MB)
+- 512 maximum input tokens 
+- 384 dimensions of output vector
+- Licence: mit. The released models can be used for commercial purposes free of charge.
+- Reference to Original Model: https://huggingface.co/BAAI/bge-small-en-v1.5
+
+
+## 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.
+
+For detailed information, refer to the ONNXEmbeddings documentation: TODO
+
+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 = "bge-small-en-v1.5"
+number_dimensions_output = 384
+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 [BAAI/bge-small-en-v1.5](https://huggingface.co/BAAI/bge-small-en-v1.5) model from Hugging Face.
+
+To enhance performance and ensure compatibility with various execution environments, we'll use the [Optimum](https://github.com/huggingface/optimum) utility to convert the model into the ONNX (Open Neural Network Exchange) format. 
+
+After conversion to ONNX, we are fixing the opset in the ONNX file for compatibility with ONNX runtime used in Teradata Vantage
+
+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("BAAI/bge-small-en-v1.5")
+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 +1,33 @@
+{
+  "_attn_implementation_autoset": true,
+  "_name_or_path": "BAAI/bge-small-en-v1.5",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "export_model_type": "transformer",
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 384,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 1536,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "pad_token_id": 0,
+  "position_embedding_type": "absolute",
+  "torch_dtype": "float32",
+  "transformers_version": "4.47.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30522
+}

conversion_config.json

@@ -0,0 +1,12 @@
+{
+    "model_id": "BAAI/bge-small-en-v1.5",
+    "number_of_generated_embeddings": 384,
+    "precision_to_filename_map": {
+        "fp32": "onnx/model.onnx", 
+        "fp16": "onnx/model_fp16.onnx", 
+        "int8": "onnx/model_int8.onnx", 
+        "uint8": "onnx/model_uint8.onnx"
+    },
+    "opset": 16,
+    "IR": 8
+}

convert.py

@@ -0,0 +1,51 @@
+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
+
+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")
+
+    main_export(model_name_or_path=model_id, output="./",  opset=opset, trust_remote_code=True, task="feature-extraction", dtype="fp32")
+    
+    if "fp32" in precision_to_filename_map:
+        print("Exporting the fp32 onnx file...")
+        
+        shutil.copyfile('model.onnx', precision_to_filename_map["fp32"])
+        
+        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)
+        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)
+        print("Done\n\n")
+        
+    os.remove("model.onnx")

onnx/model.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7a91b627bb26d1d070cfd63c4efc5121b7f5198aad7371647b70b778d42d3249
+size 133195612

onnx/model_int8.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a159b205e5e0fcf7ad4fc4c3e4dec947a260cf5df33fc2d8fceb119b84d26e00
+size 33976425

onnx/model_uint8.onnx

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f73cd6bfeb60c638311baf84937eb1e70b188335499cc3184cdf1e539b4721a8
+size 33976453

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("./")
+    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]})[1][0]
+    
+        embeddings_2_onnx = onnx_session.run(None,     {"input_ids": [enc2.input_ids], 
+             "attention_mask": [enc2.attention_mask]})[1][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": 512,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}