model

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README.md +2653 -3
config.json +34 -0
conversion_config.json +11 -0
convert.py +51 -0
onnx/model.onnx +3 -0
onnx/model_int8.onnx +3 -0
onnx/model_uint8.onnx +3 -0
special_tokens_map.json +37 -0
test_local.py +49 -0
test_teradata.py +106 -0
tokenizer.json +0 -0
tokenizer_config.json +58 -0
vocab.txt +0 -0

README.md CHANGED Viewed

@@ -1,3 +1,2653 @@
----
-license: mit
----

+---
+language:
+- en
+license: mit
+model-index:
+- name: bge-base-en-v1.5
+  results:
+  - dataset:
+      config: en
+      name: MTEB AmazonCounterfactualClassification (en)
+      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
+      split: test
+      type: mteb/amazon_counterfactual
+    metrics:
+    - type: accuracy
+      value: 76.14925373134328
+    - type: ap
+      value: 39.32336517995478
+    - type: f1
+      value: 70.16902252611425
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB AmazonPolarityClassification
+      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
+      split: test
+      type: mteb/amazon_polarity
+    metrics:
+    - type: accuracy
+      value: 93.386825
+    - type: ap
+      value: 90.21276917991995
+    - type: f1
+      value: 93.37741030006174
+    task:
+      type: Classification
+  - dataset:
+      config: en
+      name: MTEB AmazonReviewsClassification (en)
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+      split: test
+      type: mteb/amazon_reviews_multi
+    metrics:
+    - type: accuracy
+      value: 48.846000000000004
+    - type: f1
+      value: 48.14646269778261
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB ArguAna
+      revision: None
+      split: test
+      type: arguana
+    metrics:
+    - type: map_at_1
+      value: 40.754000000000005
+    - type: map_at_10
+      value: 55.761
+    - type: map_at_100
+      value: 56.330999999999996
+    - type: map_at_1000
+      value: 56.333999999999996
+    - type: map_at_3
+      value: 51.92
+    - type: map_at_5
+      value: 54.010999999999996
+    - type: mrr_at_1
+      value: 41.181
+    - type: mrr_at_10
+      value: 55.967999999999996
+    - type: mrr_at_100
+      value: 56.538
+    - type: mrr_at_1000
+      value: 56.542
+    - type: mrr_at_3
+      value: 51.980000000000004
+    - type: mrr_at_5
+      value: 54.208999999999996
+    - type: ndcg_at_1
+      value: 40.754000000000005
+    - type: ndcg_at_10
+      value: 63.605000000000004
+    - type: ndcg_at_100
+      value: 66.05199999999999
+    - type: ndcg_at_1000
+      value: 66.12
+    - type: ndcg_at_3
+      value: 55.708
+    - type: ndcg_at_5
+      value: 59.452000000000005
+    - type: precision_at_1
+      value: 40.754000000000005
+    - type: precision_at_10
+      value: 8.841000000000001
+    - type: precision_at_100
+      value: 0.991
+    - type: precision_at_1000
+      value: 0.1
+    - type: precision_at_3
+      value: 22.238
+    - type: precision_at_5
+      value: 15.149000000000001
+    - type: recall_at_1
+      value: 40.754000000000005
+    - type: recall_at_10
+      value: 88.407
+    - type: recall_at_100
+      value: 99.14699999999999
+    - type: recall_at_1000
+      value: 99.644
+    - type: recall_at_3
+      value: 66.714
+    - type: recall_at_5
+      value: 75.747
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB ArxivClusteringP2P
+      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
+      split: test
+      type: mteb/arxiv-clustering-p2p
+    metrics:
+    - type: v_measure
+      value: 48.74884539679369
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB ArxivClusteringS2S
+      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
+      split: test
+      type: mteb/arxiv-clustering-s2s
+    metrics:
+    - type: v_measure
+      value: 42.8075893810716
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB AskUbuntuDupQuestions
+      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
+      split: test
+      type: mteb/askubuntudupquestions-reranking
+    metrics:
+    - type: map
+      value: 62.128470519187736
+    - type: mrr
+      value: 74.28065778481289
+    task:
+      type: Reranking
+  - dataset:
+      config: default
+      name: MTEB BIOSSES
+      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
+      split: test
+      type: mteb/biosses-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 89.24629081484655
+    - type: cos_sim_spearman
+      value: 86.93752309911496
+    - type: euclidean_pearson
+      value: 87.58589628573816
+    - type: euclidean_spearman
+      value: 88.05622328825284
+    - type: manhattan_pearson
+      value: 87.5594959805773
+    - type: manhattan_spearman
+      value: 88.19658793233961
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB Banking77Classification
+      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
+      split: test
+      type: mteb/banking77
+    metrics:
+    - type: accuracy
+      value: 86.9512987012987
+    - type: f1
+      value: 86.92515357973708
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB BiorxivClusteringP2P
+      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
+      split: test
+      type: mteb/biorxiv-clustering-p2p
+    metrics:
+    - type: v_measure
+      value: 39.10263762928872
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB BiorxivClusteringS2S
+      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
+      split: test
+      type: mteb/biorxiv-clustering-s2s
+    metrics:
+    - type: v_measure
+      value: 36.69711517426737
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB CQADupstackAndroidRetrieval
+      revision: None
+      split: test
+      type: BeIR/cqadupstack
+    metrics:
+    - type: map_at_1
+      value: 32.327
+    - type: map_at_10
+      value: 44.099
+    - type: map_at_100
+      value: 45.525
+    - type: map_at_1000
+      value: 45.641999999999996
+    - type: map_at_3
+      value: 40.47
+    - type: map_at_5
+      value: 42.36
+    - type: mrr_at_1
+      value: 39.199
+    - type: mrr_at_10
+      value: 49.651
+    - type: mrr_at_100
+      value: 50.29
+    - type: mrr_at_1000
+      value: 50.329
+    - type: mrr_at_3
+      value: 46.924
+    - type: mrr_at_5
+      value: 48.548
+    - type: ndcg_at_1
+      value: 39.199
+    - type: ndcg_at_10
+      value: 50.773
+    - type: ndcg_at_100
+      value: 55.67999999999999
+    - type: ndcg_at_1000
+      value: 57.495
+    - type: ndcg_at_3
+      value: 45.513999999999996
+    - type: ndcg_at_5
+      value: 47.703
+    - type: precision_at_1
+      value: 39.199
+    - type: precision_at_10
+      value: 9.914000000000001
+    - type: precision_at_100
+      value: 1.5310000000000001
+    - type: precision_at_1000
+      value: 0.198
+    - type: precision_at_3
+      value: 21.984
+    - type: precision_at_5
+      value: 15.737000000000002
+    - type: recall_at_1
+      value: 32.327
+    - type: recall_at_10
+      value: 63.743
+    - type: recall_at_100
+      value: 84.538
+    - type: recall_at_1000
+      value: 96.089
+    - type: recall_at_3
+      value: 48.065000000000005
+    - type: recall_at_5
+      value: 54.519
+    - type: map_at_1
+      value: 32.671
+    - type: map_at_10
+      value: 42.954
+    - type: map_at_100
+      value: 44.151
+    - type: map_at_1000
+      value: 44.287
+    - type: map_at_3
+      value: 39.912
+    - type: map_at_5
+      value: 41.798
+    - type: mrr_at_1
+      value: 41.465
+    - type: mrr_at_10
+      value: 49.351
+    - type: mrr_at_100
+      value: 49.980000000000004
+    - type: mrr_at_1000
+      value: 50.016000000000005
+    - type: mrr_at_3
+      value: 47.144000000000005
+    - type: mrr_at_5
+      value: 48.592999999999996
+    - type: ndcg_at_1
+      value: 41.465
+    - type: ndcg_at_10
+      value: 48.565999999999995
+    - type: ndcg_at_100
+      value: 52.76499999999999
+    - type: ndcg_at_1000
+      value: 54.749
+    - type: ndcg_at_3
+      value: 44.57
+    - type: ndcg_at_5
+      value: 46.759
+    - type: precision_at_1
+      value: 41.465
+    - type: precision_at_10
+      value: 9.107999999999999
+    - type: precision_at_100
+      value: 1.433
+    - type: precision_at_1000
+      value: 0.191
+    - type: precision_at_3
+      value: 21.423000000000002
+    - type: precision_at_5
+      value: 15.414
+    - type: recall_at_1
+      value: 32.671
+    - type: recall_at_10
+      value: 57.738
+    - type: recall_at_100
+      value: 75.86500000000001
+    - type: recall_at_1000
+      value: 88.36
+    - type: recall_at_3
+      value: 45.626
+    - type: recall_at_5
+      value: 51.812000000000005
+    - type: map_at_1
+      value: 41.185
+    - type: map_at_10
+      value: 53.929
+    - type: map_at_100
+      value: 54.92
+    - type: map_at_1000
+      value: 54.967999999999996
+    - type: map_at_3
+      value: 50.70400000000001
+    - type: map_at_5
+      value: 52.673
+    - type: mrr_at_1
+      value: 47.398
+    - type: mrr_at_10
+      value: 57.303000000000004
+    - type: mrr_at_100
+      value: 57.959
+    - type: mrr_at_1000
+      value: 57.985
+    - type: mrr_at_3
+      value: 54.932
+    - type: mrr_at_5
+      value: 56.464999999999996
+    - type: ndcg_at_1
+      value: 47.398
+    - type: ndcg_at_10
+      value: 59.653
+    - type: ndcg_at_100
+      value: 63.627
+    - type: ndcg_at_1000
+      value: 64.596
+    - type: ndcg_at_3
+      value: 54.455
+    - type: ndcg_at_5
+      value: 57.245000000000005
+    - type: precision_at_1
+      value: 47.398
+    - type: precision_at_10
+      value: 9.524000000000001
+    - type: precision_at_100
+      value: 1.243
+    - type: precision_at_1000
+      value: 0.13699999999999998
+    - type: precision_at_3
+      value: 24.389
+    - type: precision_at_5
+      value: 16.752
+    - type: recall_at_1
+      value: 41.185
+    - type: recall_at_10
+      value: 73.193
+    - type: recall_at_100
+      value: 90.357
+    - type: recall_at_1000
+      value: 97.253
+    - type: recall_at_3
+      value: 59.199999999999996
+    - type: recall_at_5
+      value: 66.118
+    - type: map_at_1
+      value: 27.27
+    - type: map_at_10
+      value: 36.223
+    - type: map_at_100
+      value: 37.218
+    - type: map_at_1000
+      value: 37.293
+    - type: map_at_3
+      value: 33.503
+    - type: map_at_5
+      value: 35.097
+    - type: mrr_at_1
+      value: 29.492
+    - type: mrr_at_10
+      value: 38.352000000000004
+    - type: mrr_at_100
+      value: 39.188
+    - type: mrr_at_1000
+      value: 39.247
+    - type: mrr_at_3
+      value: 35.876000000000005
+    - type: mrr_at_5
+      value: 37.401
+    - type: ndcg_at_1
+      value: 29.492
+    - type: ndcg_at_10
+      value: 41.239
+    - type: ndcg_at_100
+      value: 46.066
+    - type: ndcg_at_1000
+      value: 47.992000000000004
+    - type: ndcg_at_3
+      value: 36.11
+    - type: ndcg_at_5
+      value: 38.772
+    - type: precision_at_1
+      value: 29.492
+    - type: precision_at_10
+      value: 6.260000000000001
+    - type: precision_at_100
+      value: 0.914
+    - type: precision_at_1000
+      value: 0.11100000000000002
+    - type: precision_at_3
+      value: 15.104000000000001
+    - type: precision_at_5
+      value: 10.644
+    - type: recall_at_1
+      value: 27.27
+    - type: recall_at_10
+      value: 54.589
+    - type: recall_at_100
+      value: 76.70700000000001
+    - type: recall_at_1000
+      value: 91.158
+    - type: recall_at_3
+      value: 40.974
+    - type: recall_at_5
+      value: 47.327000000000005
+    - type: map_at_1
+      value: 17.848
+    - type: map_at_10
+      value: 26.207
+    - type: map_at_100
+      value: 27.478
+    - type: map_at_1000
+      value: 27.602
+    - type: map_at_3
+      value: 23.405
+    - type: map_at_5
+      value: 24.98
+    - type: mrr_at_1
+      value: 21.891
+    - type: mrr_at_10
+      value: 31.041999999999998
+    - type: mrr_at_100
+      value: 32.092
+    - type: mrr_at_1000
+      value: 32.151999999999994
+    - type: mrr_at_3
+      value: 28.358
+    - type: mrr_at_5
+      value: 29.969
+    - type: ndcg_at_1
+      value: 21.891
+    - type: ndcg_at_10
+      value: 31.585
+    - type: ndcg_at_100
+      value: 37.531
+    - type: ndcg_at_1000
+      value: 40.256
+    - type: ndcg_at_3
+      value: 26.508
+    - type: ndcg_at_5
+      value: 28.894
+    - type: precision_at_1
+      value: 21.891
+    - type: precision_at_10
+      value: 5.795999999999999
+    - type: precision_at_100
+      value: 0.9990000000000001
+    - type: precision_at_1000
+      value: 0.13799999999999998
+    - type: precision_at_3
+      value: 12.769
+    - type: precision_at_5
+      value: 9.279
+    - type: recall_at_1
+      value: 17.848
+    - type: recall_at_10
+      value: 43.452
+    - type: recall_at_100
+      value: 69.216
+    - type: recall_at_1000
+      value: 88.102
+    - type: recall_at_3
+      value: 29.18
+    - type: recall_at_5
+      value: 35.347
+    - type: map_at_1
+      value: 30.94
+    - type: map_at_10
+      value: 41.248000000000005
+    - type: map_at_100
+      value: 42.495
+    - type: map_at_1000
+      value: 42.602000000000004
+    - type: map_at_3
+      value: 37.939
+    - type: map_at_5
+      value: 39.924
+    - type: mrr_at_1
+      value: 37.824999999999996
+    - type: mrr_at_10
+      value: 47.041
+    - type: mrr_at_100
+      value: 47.83
+    - type: mrr_at_1000
+      value: 47.878
+    - type: mrr_at_3
+      value: 44.466
+    - type: mrr_at_5
+      value: 46.111999999999995
+    - type: ndcg_at_1
+      value: 37.824999999999996
+    - type: ndcg_at_10
+      value: 47.223
+    - type: ndcg_at_100
+      value: 52.394
+    - type: ndcg_at_1000
+      value: 54.432
+    - type: ndcg_at_3
+      value: 42.032000000000004
+    - type: ndcg_at_5
+      value: 44.772
+    - type: precision_at_1
+      value: 37.824999999999996
+    - type: precision_at_10
+      value: 8.393
+    - type: precision_at_100
+      value: 1.2890000000000001
+    - type: precision_at_1000
+      value: 0.164
+    - type: precision_at_3
+      value: 19.698
+    - type: precision_at_5
+      value: 14.013
+    - type: recall_at_1
+      value: 30.94
+    - type: recall_at_10
+      value: 59.316
+    - type: recall_at_100
+      value: 80.783
+    - type: recall_at_1000
+      value: 94.15400000000001
+    - type: recall_at_3
+      value: 44.712
+    - type: recall_at_5
+      value: 51.932
+    - type: map_at_1
+      value: 27.104
+    - type: map_at_10
+      value: 36.675999999999995
+    - type: map_at_100
+      value: 38.076
+    - type: map_at_1000
+      value: 38.189
+    - type: map_at_3
+      value: 33.733999999999995
+    - type: map_at_5
+      value: 35.287
+    - type: mrr_at_1
+      value: 33.904
+    - type: mrr_at_10
+      value: 42.55
+    - type: mrr_at_100
+      value: 43.434
+    - type: mrr_at_1000
+      value: 43.494
+    - type: mrr_at_3
+      value: 40.126
+    - type: mrr_at_5
+      value: 41.473
+    - type: ndcg_at_1
+      value: 33.904
+    - type: ndcg_at_10
+      value: 42.414
+    - type: ndcg_at_100
+      value: 48.203
+    - type: ndcg_at_1000
+      value: 50.437
+    - type: ndcg_at_3
+      value: 37.633
+    - type: ndcg_at_5
+      value: 39.67
+    - type: precision_at_1
+      value: 33.904
+    - type: precision_at_10
+      value: 7.82
+    - type: precision_at_100
+      value: 1.2409999999999999
+    - type: precision_at_1000
+      value: 0.159
+    - type: precision_at_3
+      value: 17.884
+    - type: precision_at_5
+      value: 12.648000000000001
+    - type: recall_at_1
+      value: 27.104
+    - type: recall_at_10
+      value: 53.563
+    - type: recall_at_100
+      value: 78.557
+    - type: recall_at_1000
+      value: 93.533
+    - type: recall_at_3
+      value: 39.92
+    - type: recall_at_5
+      value: 45.457
+    - type: map_at_1
+      value: 27.707749999999997
+    - type: map_at_10
+      value: 36.961
+    - type: map_at_100
+      value: 38.158833333333334
+    - type: map_at_1000
+      value: 38.270333333333326
+    - type: map_at_3
+      value: 34.07183333333334
+    - type: map_at_5
+      value: 35.69533333333334
+    - type: mrr_at_1
+      value: 32.81875
+    - type: mrr_at_10
+      value: 41.293
+    - type: mrr_at_100
+      value: 42.116499999999995
+    - type: mrr_at_1000
+      value: 42.170249999999996
+    - type: mrr_at_3
+      value: 38.83983333333333
+    - type: mrr_at_5
+      value: 40.29775
+    - type: ndcg_at_1
+      value: 32.81875
+    - type: ndcg_at_10
+      value: 42.355
+    - type: ndcg_at_100
+      value: 47.41374999999999
+    - type: ndcg_at_1000
+      value: 49.5805
+    - type: ndcg_at_3
+      value: 37.52825
+    - type: ndcg_at_5
+      value: 39.83266666666667
+    - type: precision_at_1
+      value: 32.81875
+    - type: precision_at_10
+      value: 7.382416666666666
+    - type: precision_at_100
+      value: 1.1640833333333334
+    - type: precision_at_1000
+      value: 0.15383333333333335
+    - type: precision_at_3
+      value: 17.134166666666665
+    - type: precision_at_5
+      value: 12.174833333333336
+    - type: recall_at_1
+      value: 27.707749999999997
+    - type: recall_at_10
+      value: 53.945
+    - type: recall_at_100
+      value: 76.191
+    - type: recall_at_1000
+      value: 91.101
+    - type: recall_at_3
+      value: 40.39083333333334
+    - type: recall_at_5
+      value: 46.40083333333333
+    - type: map_at_1
+      value: 26.482
+    - type: map_at_10
+      value: 33.201
+    - type: map_at_100
+      value: 34.107
+    - type: map_at_1000
+      value: 34.197
+    - type: map_at_3
+      value: 31.174000000000003
+    - type: map_at_5
+      value: 32.279
+    - type: mrr_at_1
+      value: 29.908
+    - type: mrr_at_10
+      value: 36.235
+    - type: mrr_at_100
+      value: 37.04
+    - type: mrr_at_1000
+      value: 37.105
+    - type: mrr_at_3
+      value: 34.355999999999995
+    - type: mrr_at_5
+      value: 35.382999999999996
+    - type: ndcg_at_1
+      value: 29.908
+    - type: ndcg_at_10
+      value: 37.325
+    - type: ndcg_at_100
+      value: 41.795
+    - type: ndcg_at_1000
+      value: 44.105
+    - type: ndcg_at_3
+      value: 33.555
+    - type: ndcg_at_5
+      value: 35.266999999999996
+    - type: precision_at_1
+      value: 29.908
+    - type: precision_at_10
+      value: 5.721
+    - type: precision_at_100
+      value: 0.8630000000000001
+    - type: precision_at_1000
+      value: 0.11299999999999999
+    - type: precision_at_3
+      value: 14.008000000000001
+    - type: precision_at_5
+      value: 9.754999999999999
+    - type: recall_at_1
+      value: 26.482
+    - type: recall_at_10
+      value: 47.072
+    - type: recall_at_100
+      value: 67.27
+    - type: recall_at_1000
+      value: 84.371
+    - type: recall_at_3
+      value: 36.65
+    - type: recall_at_5
+      value: 40.774
+    - type: map_at_1
+      value: 18.815
+    - type: map_at_10
+      value: 26.369999999999997
+    - type: map_at_100
+      value: 27.458
+    - type: map_at_1000
+      value: 27.588
+    - type: map_at_3
+      value: 23.990000000000002
+    - type: map_at_5
+      value: 25.345000000000002
+    - type: mrr_at_1
+      value: 22.953000000000003
+    - type: mrr_at_10
+      value: 30.342999999999996
+    - type: mrr_at_100
+      value: 31.241000000000003
+    - type: mrr_at_1000
+      value: 31.319000000000003
+    - type: mrr_at_3
+      value: 28.16
+    - type: mrr_at_5
+      value: 29.406
+    - type: ndcg_at_1
+      value: 22.953000000000003
+    - type: ndcg_at_10
+      value: 31.151
+    - type: ndcg_at_100
+      value: 36.309000000000005
+    - type: ndcg_at_1000
+      value: 39.227000000000004
+    - type: ndcg_at_3
+      value: 26.921
+    - type: ndcg_at_5
+      value: 28.938000000000002
+    - type: precision_at_1
+      value: 22.953000000000003
+    - type: precision_at_10
+      value: 5.602
+    - type: precision_at_100
+      value: 0.9530000000000001
+    - type: precision_at_1000
+      value: 0.13899999999999998
+    - type: precision_at_3
+      value: 12.606
+    - type: precision_at_5
+      value: 9.119
+    - type: recall_at_1
+      value: 18.815
+    - type: recall_at_10
+      value: 41.574
+    - type: recall_at_100
+      value: 64.84400000000001
+    - type: recall_at_1000
+      value: 85.406
+    - type: recall_at_3
+      value: 29.694
+    - type: recall_at_5
+      value: 34.935
+    - type: map_at_1
+      value: 27.840999999999998
+    - type: map_at_10
+      value: 36.797999999999995
+    - type: map_at_100
+      value: 37.993
+    - type: map_at_1000
+      value: 38.086999999999996
+    - type: map_at_3
+      value: 34.050999999999995
+    - type: map_at_5
+      value: 35.379
+    - type: mrr_at_1
+      value: 32.649
+    - type: mrr_at_10
+      value: 41.025
+    - type: mrr_at_100
+      value: 41.878
+    - type: mrr_at_1000
+      value: 41.929
+    - type: mrr_at_3
+      value: 38.573
+    - type: mrr_at_5
+      value: 39.715
+    - type: ndcg_at_1
+      value: 32.649
+    - type: ndcg_at_10
+      value: 42.142
+    - type: ndcg_at_100
+      value: 47.558
+    - type: ndcg_at_1000
+      value: 49.643
+    - type: ndcg_at_3
+      value: 37.12
+    - type: ndcg_at_5
+      value: 38.983000000000004
+    - type: precision_at_1
+      value: 32.649
+    - type: precision_at_10
+      value: 7.08
+    - type: precision_at_100
+      value: 1.1039999999999999
+    - type: precision_at_1000
+      value: 0.13899999999999998
+    - type: precision_at_3
+      value: 16.698
+    - type: precision_at_5
+      value: 11.511000000000001
+    - type: recall_at_1
+      value: 27.840999999999998
+    - type: recall_at_10
+      value: 54.245
+    - type: recall_at_100
+      value: 77.947
+    - type: recall_at_1000
+      value: 92.36999999999999
+    - type: recall_at_3
+      value: 40.146
+    - type: recall_at_5
+      value: 44.951
+    - type: map_at_1
+      value: 26.529000000000003
+    - type: map_at_10
+      value: 35.010000000000005
+    - type: map_at_100
+      value: 36.647
+    - type: map_at_1000
+      value: 36.857
+    - type: map_at_3
+      value: 31.968000000000004
+    - type: map_at_5
+      value: 33.554
+    - type: mrr_at_1
+      value: 31.818
+    - type: mrr_at_10
+      value: 39.550999999999995
+    - type: mrr_at_100
+      value: 40.54
+    - type: mrr_at_1000
+      value: 40.596
+    - type: mrr_at_3
+      value: 36.726
+    - type: mrr_at_5
+      value: 38.416
+    - type: ndcg_at_1
+      value: 31.818
+    - type: ndcg_at_10
+      value: 40.675
+    - type: ndcg_at_100
+      value: 46.548
+    - type: ndcg_at_1000
+      value: 49.126
+    - type: ndcg_at_3
+      value: 35.829
+    - type: ndcg_at_5
+      value: 38.0
+    - type: precision_at_1
+      value: 31.818
+    - type: precision_at_10
+      value: 7.826
+    - type: precision_at_100
+      value: 1.538
+    - type: precision_at_1000
+      value: 0.24
+    - type: precision_at_3
+      value: 16.601
+    - type: precision_at_5
+      value: 12.095
+    - type: recall_at_1
+      value: 26.529000000000003
+    - type: recall_at_10
+      value: 51.03
+    - type: recall_at_100
+      value: 77.556
+    - type: recall_at_1000
+      value: 93.804
+    - type: recall_at_3
+      value: 36.986000000000004
+    - type: recall_at_5
+      value: 43.096000000000004
+    - type: map_at_1
+      value: 23.480999999999998
+    - type: map_at_10
+      value: 30.817
+    - type: map_at_100
+      value: 31.838
+    - type: map_at_1000
+      value: 31.932
+    - type: map_at_3
+      value: 28.011999999999997
+    - type: map_at_5
+      value: 29.668
+    - type: mrr_at_1
+      value: 25.323
+    - type: mrr_at_10
+      value: 33.072
+    - type: mrr_at_100
+      value: 33.926
+    - type: mrr_at_1000
+      value: 33.993
+    - type: mrr_at_3
+      value: 30.436999999999998
+    - type: mrr_at_5
+      value: 32.092
+    - type: ndcg_at_1
+      value: 25.323
+    - type: ndcg_at_10
+      value: 35.514
+    - type: ndcg_at_100
+      value: 40.489000000000004
+    - type: ndcg_at_1000
+      value: 42.908
+    - type: ndcg_at_3
+      value: 30.092000000000002
+    - type: ndcg_at_5
+      value: 32.989000000000004
+    - type: precision_at_1
+      value: 25.323
+    - type: precision_at_10
+      value: 5.545
+    - type: precision_at_100
+      value: 0.861
+    - type: precision_at_1000
+      value: 0.117
+    - type: precision_at_3
+      value: 12.446
+    - type: precision_at_5
+      value: 9.131
+    - type: recall_at_1
+      value: 23.480999999999998
+    - type: recall_at_10
+      value: 47.825
+    - type: recall_at_100
+      value: 70.652
+    - type: recall_at_1000
+      value: 88.612
+    - type: recall_at_3
+      value: 33.537
+    - type: recall_at_5
+      value: 40.542
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB ClimateFEVER
+      revision: None
+      split: test
+      type: climate-fever
+    metrics:
+    - type: map_at_1
+      value: 13.333999999999998
+    - type: map_at_10
+      value: 22.524
+    - type: map_at_100
+      value: 24.506
+    - type: map_at_1000
+      value: 24.715
+    - type: map_at_3
+      value: 19.022
+    - type: map_at_5
+      value: 20.693
+    - type: mrr_at_1
+      value: 29.186
+    - type: mrr_at_10
+      value: 41.22
+    - type: mrr_at_100
+      value: 42.16
+    - type: mrr_at_1000
+      value: 42.192
+    - type: mrr_at_3
+      value: 38.013000000000005
+    - type: mrr_at_5
+      value: 39.704
+    - type: ndcg_at_1
+      value: 29.186
+    - type: ndcg_at_10
+      value: 31.167
+    - type: ndcg_at_100
+      value: 38.879000000000005
+    - type: ndcg_at_1000
+      value: 42.376000000000005
+    - type: ndcg_at_3
+      value: 25.817
+    - type: ndcg_at_5
+      value: 27.377000000000002
+    - type: precision_at_1
+      value: 29.186
+    - type: precision_at_10
+      value: 9.693999999999999
+    - type: precision_at_100
+      value: 1.8030000000000002
+    - type: precision_at_1000
+      value: 0.246
+    - type: precision_at_3
+      value: 19.11
+    - type: precision_at_5
+      value: 14.344999999999999
+    - type: recall_at_1
+      value: 13.333999999999998
+    - type: recall_at_10
+      value: 37.092000000000006
+    - type: recall_at_100
+      value: 63.651
+    - type: recall_at_1000
+      value: 83.05
+    - type: recall_at_3
+      value: 23.74
+    - type: recall_at_5
+      value: 28.655
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB DBPedia
+      revision: None
+      split: test
+      type: dbpedia-entity
+    metrics:
+    - type: map_at_1
+      value: 9.151
+    - type: map_at_10
+      value: 19.653000000000002
+    - type: map_at_100
+      value: 28.053
+    - type: map_at_1000
+      value: 29.709000000000003
+    - type: map_at_3
+      value: 14.191
+    - type: map_at_5
+      value: 16.456
+    - type: mrr_at_1
+      value: 66.25
+    - type: mrr_at_10
+      value: 74.4
+    - type: mrr_at_100
+      value: 74.715
+    - type: mrr_at_1000
+      value: 74.726
+    - type: mrr_at_3
+      value: 72.417
+    - type: mrr_at_5
+      value: 73.667
+    - type: ndcg_at_1
+      value: 54.25
+    - type: ndcg_at_10
+      value: 40.77
+    - type: ndcg_at_100
+      value: 46.359
+    - type: ndcg_at_1000
+      value: 54.193000000000005
+    - type: ndcg_at_3
+      value: 44.832
+    - type: ndcg_at_5
+      value: 42.63
+    - type: precision_at_1
+      value: 66.25
+    - type: precision_at_10
+      value: 32.175
+    - type: precision_at_100
+      value: 10.668
+    - type: precision_at_1000
+      value: 2.067
+    - type: precision_at_3
+      value: 47.667
+    - type: precision_at_5
+      value: 41.3
+    - type: recall_at_1
+      value: 9.151
+    - type: recall_at_10
+      value: 25.003999999999998
+    - type: recall_at_100
+      value: 52.976
+    - type: recall_at_1000
+      value: 78.315
+    - type: recall_at_3
+      value: 15.487
+    - type: recall_at_5
+      value: 18.999
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB EmotionClassification
+      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
+      split: test
+      type: mteb/emotion
+    metrics:
+    - type: accuracy
+      value: 51.89999999999999
+    - type: f1
+      value: 46.47777925067403
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB FEVER
+      revision: None
+      split: test
+      type: fever
+    metrics:
+    - type: map_at_1
+      value: 73.706
+    - type: map_at_10
+      value: 82.423
+    - type: map_at_100
+      value: 82.67999999999999
+    - type: map_at_1000
+      value: 82.694
+    - type: map_at_3
+      value: 81.328
+    - type: map_at_5
+      value: 82.001
+    - type: mrr_at_1
+      value: 79.613
+    - type: mrr_at_10
+      value: 87.07000000000001
+    - type: mrr_at_100
+      value: 87.169
+    - type: mrr_at_1000
+      value: 87.17
+    - type: mrr_at_3
+      value: 86.404
+    - type: mrr_at_5
+      value: 86.856
+    - type: ndcg_at_1
+      value: 79.613
+    - type: ndcg_at_10
+      value: 86.289
+    - type: ndcg_at_100
+      value: 87.201
+    - type: ndcg_at_1000
+      value: 87.428
+    - type: ndcg_at_3
+      value: 84.625
+    - type: ndcg_at_5
+      value: 85.53699999999999
+    - type: precision_at_1
+      value: 79.613
+    - type: precision_at_10
+      value: 10.399
+    - type: precision_at_100
+      value: 1.1079999999999999
+    - type: precision_at_1000
+      value: 0.11499999999999999
+    - type: precision_at_3
+      value: 32.473
+    - type: precision_at_5
+      value: 20.132
+    - type: recall_at_1
+      value: 73.706
+    - type: recall_at_10
+      value: 93.559
+    - type: recall_at_100
+      value: 97.188
+    - type: recall_at_1000
+      value: 98.555
+    - type: recall_at_3
+      value: 88.98700000000001
+    - type: recall_at_5
+      value: 91.373
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB FiQA2018
+      revision: None
+      split: test
+      type: fiqa
+    metrics:
+    - type: map_at_1
+      value: 19.841
+    - type: map_at_10
+      value: 32.643
+    - type: map_at_100
+      value: 34.575
+    - type: map_at_1000
+      value: 34.736
+    - type: map_at_3
+      value: 28.317999999999998
+    - type: map_at_5
+      value: 30.964000000000002
+    - type: mrr_at_1
+      value: 39.660000000000004
+    - type: mrr_at_10
+      value: 48.620000000000005
+    - type: mrr_at_100
+      value: 49.384
+    - type: mrr_at_1000
+      value: 49.415
+    - type: mrr_at_3
+      value: 45.988
+    - type: mrr_at_5
+      value: 47.361
+    - type: ndcg_at_1
+      value: 39.660000000000004
+    - type: ndcg_at_10
+      value: 40.646
+    - type: ndcg_at_100
+      value: 47.657
+    - type: ndcg_at_1000
+      value: 50.428
+    - type: ndcg_at_3
+      value: 36.689
+    - type: ndcg_at_5
+      value: 38.211
+    - type: precision_at_1
+      value: 39.660000000000004
+    - type: precision_at_10
+      value: 11.235000000000001
+    - type: precision_at_100
+      value: 1.8530000000000002
+    - type: precision_at_1000
+      value: 0.23600000000000002
+    - type: precision_at_3
+      value: 24.587999999999997
+    - type: precision_at_5
+      value: 18.395
+    - type: recall_at_1
+      value: 19.841
+    - type: recall_at_10
+      value: 48.135
+    - type: recall_at_100
+      value: 74.224
+    - type: recall_at_1000
+      value: 90.826
+    - type: recall_at_3
+      value: 33.536
+    - type: recall_at_5
+      value: 40.311
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB HotpotQA
+      revision: None
+      split: test
+      type: hotpotqa
+    metrics:
+    - type: map_at_1
+      value: 40.358
+    - type: map_at_10
+      value: 64.497
+    - type: map_at_100
+      value: 65.362
+    - type: map_at_1000
+      value: 65.41900000000001
+    - type: map_at_3
+      value: 61.06700000000001
+    - type: map_at_5
+      value: 63.317
+    - type: mrr_at_1
+      value: 80.716
+    - type: mrr_at_10
+      value: 86.10799999999999
+    - type: mrr_at_100
+      value: 86.265
+    - type: mrr_at_1000
+      value: 86.27
+    - type: mrr_at_3
+      value: 85.271
+    - type: mrr_at_5
+      value: 85.82499999999999
+    - type: ndcg_at_1
+      value: 80.716
+    - type: ndcg_at_10
+      value: 72.597
+    - type: ndcg_at_100
+      value: 75.549
+    - type: ndcg_at_1000
+      value: 76.61
+    - type: ndcg_at_3
+      value: 67.874
+    - type: ndcg_at_5
+      value: 70.655
+    - type: precision_at_1
+      value: 80.716
+    - type: precision_at_10
+      value: 15.148
+    - type: precision_at_100
+      value: 1.745
+    - type: precision_at_1000
+      value: 0.188
+    - type: precision_at_3
+      value: 43.597
+    - type: precision_at_5
+      value: 28.351
+    - type: recall_at_1
+      value: 40.358
+    - type: recall_at_10
+      value: 75.739
+    - type: recall_at_100
+      value: 87.259
+    - type: recall_at_1000
+      value: 94.234
+    - type: recall_at_3
+      value: 65.39500000000001
+    - type: recall_at_5
+      value: 70.878
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB ImdbClassification
+      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
+      split: test
+      type: mteb/imdb
+    metrics:
+    - type: accuracy
+      value: 90.80799999999998
+    - type: ap
+      value: 86.81350378180757
+    - type: f1
+      value: 90.79901248314215
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB MSMARCO
+      revision: None
+      split: dev
+      type: msmarco
+    metrics:
+    - type: map_at_1
+      value: 22.096
+    - type: map_at_10
+      value: 34.384
+    - type: map_at_100
+      value: 35.541
+    - type: map_at_1000
+      value: 35.589999999999996
+    - type: map_at_3
+      value: 30.496000000000002
+    - type: map_at_5
+      value: 32.718
+    - type: mrr_at_1
+      value: 22.750999999999998
+    - type: mrr_at_10
+      value: 35.024
+    - type: mrr_at_100
+      value: 36.125
+    - type: mrr_at_1000
+      value: 36.168
+    - type: mrr_at_3
+      value: 31.225
+    - type: mrr_at_5
+      value: 33.416000000000004
+    - type: ndcg_at_1
+      value: 22.750999999999998
+    - type: ndcg_at_10
+      value: 41.351
+    - type: ndcg_at_100
+      value: 46.92
+    - type: ndcg_at_1000
+      value: 48.111
+    - type: ndcg_at_3
+      value: 33.439
+    - type: ndcg_at_5
+      value: 37.407000000000004
+    - type: precision_at_1
+      value: 22.750999999999998
+    - type: precision_at_10
+      value: 6.564
+    - type: precision_at_100
+      value: 0.935
+    - type: precision_at_1000
+      value: 0.104
+    - type: precision_at_3
+      value: 14.288
+    - type: precision_at_5
+      value: 10.581999999999999
+    - type: recall_at_1
+      value: 22.096
+    - type: recall_at_10
+      value: 62.771
+    - type: recall_at_100
+      value: 88.529
+    - type: recall_at_1000
+      value: 97.55
+    - type: recall_at_3
+      value: 41.245
+    - type: recall_at_5
+      value: 50.788
+    task:
+      type: Retrieval
+  - dataset:
+      config: en
+      name: MTEB MTOPDomainClassification (en)
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+      split: test
+      type: mteb/mtop_domain
+    metrics:
+    - type: accuracy
+      value: 94.16780665754673
+    - type: f1
+      value: 93.96331194859894
+    task:
+      type: Classification
+  - dataset:
+      config: en
+      name: MTEB MTOPIntentClassification (en)
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+      split: test
+      type: mteb/mtop_intent
+    metrics:
+    - type: accuracy
+      value: 76.90606475148198
+    - type: f1
+      value: 58.58344986604187
+    task:
+      type: Classification
+  - dataset:
+      config: en
+      name: MTEB MassiveIntentClassification (en)
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+      split: test
+      type: mteb/amazon_massive_intent
+    metrics:
+    - type: accuracy
+      value: 76.14660390047075
+    - type: f1
+      value: 74.31533923533614
+    task:
+      type: Classification
+  - dataset:
+      config: en
+      name: MTEB MassiveScenarioClassification (en)
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+      split: test
+      type: mteb/amazon_massive_scenario
+    metrics:
+    - type: accuracy
+      value: 80.16139878950908
+    - type: f1
+      value: 80.18532656824924
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB MedrxivClusteringP2P
+      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
+      split: test
+      type: mteb/medrxiv-clustering-p2p
+    metrics:
+    - type: v_measure
+      value: 32.949880906135085
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB MedrxivClusteringS2S
+      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
+      split: test
+      type: mteb/medrxiv-clustering-s2s
+    metrics:
+    - type: v_measure
+      value: 31.56300351524862
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB MindSmallReranking
+      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
+      split: test
+      type: mteb/mind_small
+    metrics:
+    - type: map
+      value: 31.196521894371315
+    - type: mrr
+      value: 32.22644231694389
+    task:
+      type: Reranking
+  - dataset:
+      config: default
+      name: MTEB NFCorpus
+      revision: None
+      split: test
+      type: nfcorpus
+    metrics:
+    - type: map_at_1
+      value: 6.783
+    - type: map_at_10
+      value: 14.549000000000001
+    - type: map_at_100
+      value: 18.433
+    - type: map_at_1000
+      value: 19.949
+    - type: map_at_3
+      value: 10.936
+    - type: map_at_5
+      value: 12.514
+    - type: mrr_at_1
+      value: 47.368
+    - type: mrr_at_10
+      value: 56.42
+    - type: mrr_at_100
+      value: 56.908
+    - type: mrr_at_1000
+      value: 56.95
+    - type: mrr_at_3
+      value: 54.283
+    - type: mrr_at_5
+      value: 55.568
+    - type: ndcg_at_1
+      value: 45.666000000000004
+    - type: ndcg_at_10
+      value: 37.389
+    - type: ndcg_at_100
+      value: 34.253
+    - type: ndcg_at_1000
+      value: 43.059999999999995
+    - type: ndcg_at_3
+      value: 42.725
+    - type: ndcg_at_5
+      value: 40.193
+    - type: precision_at_1
+      value: 47.368
+    - type: precision_at_10
+      value: 27.988000000000003
+    - type: precision_at_100
+      value: 8.672
+    - type: precision_at_1000
+      value: 2.164
+    - type: precision_at_3
+      value: 40.248
+    - type: precision_at_5
+      value: 34.737
+    - type: recall_at_1
+      value: 6.783
+    - type: recall_at_10
+      value: 17.838
+    - type: recall_at_100
+      value: 33.672000000000004
+    - type: recall_at_1000
+      value: 66.166
+    - type: recall_at_3
+      value: 11.849
+    - type: recall_at_5
+      value: 14.205000000000002
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB NQ
+      revision: None
+      split: test
+      type: nq
+    metrics:
+    - type: map_at_1
+      value: 31.698999999999998
+    - type: map_at_10
+      value: 46.556
+    - type: map_at_100
+      value: 47.652
+    - type: map_at_1000
+      value: 47.68
+    - type: map_at_3
+      value: 42.492000000000004
+    - type: map_at_5
+      value: 44.763999999999996
+    - type: mrr_at_1
+      value: 35.747
+    - type: mrr_at_10
+      value: 49.242999999999995
+    - type: mrr_at_100
+      value: 50.052
+    - type: mrr_at_1000
+      value: 50.068
+    - type: mrr_at_3
+      value: 45.867000000000004
+    - type: mrr_at_5
+      value: 47.778999999999996
+    - type: ndcg_at_1
+      value: 35.717999999999996
+    - type: ndcg_at_10
+      value: 54.14600000000001
+    - type: ndcg_at_100
+      value: 58.672999999999995
+    - type: ndcg_at_1000
+      value: 59.279
+    - type: ndcg_at_3
+      value: 46.407
+    - type: ndcg_at_5
+      value: 50.181
+    - type: precision_at_1
+      value: 35.717999999999996
+    - type: precision_at_10
+      value: 8.844000000000001
+    - type: precision_at_100
+      value: 1.139
+    - type: precision_at_1000
+      value: 0.12
+    - type: precision_at_3
+      value: 20.993000000000002
+    - type: precision_at_5
+      value: 14.791000000000002
+    - type: recall_at_1
+      value: 31.698999999999998
+    - type: recall_at_10
+      value: 74.693
+    - type: recall_at_100
+      value: 94.15299999999999
+    - type: recall_at_1000
+      value: 98.585
+    - type: recall_at_3
+      value: 54.388999999999996
+    - type: recall_at_5
+      value: 63.08200000000001
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB QuoraRetrieval
+      revision: None
+      split: test
+      type: quora
+    metrics:
+    - type: map_at_1
+      value: 71.283
+    - type: map_at_10
+      value: 85.24000000000001
+    - type: map_at_100
+      value: 85.882
+    - type: map_at_1000
+      value: 85.897
+    - type: map_at_3
+      value: 82.326
+    - type: map_at_5
+      value: 84.177
+    - type: mrr_at_1
+      value: 82.21000000000001
+    - type: mrr_at_10
+      value: 88.228
+    - type: mrr_at_100
+      value: 88.32
+    - type: mrr_at_1000
+      value: 88.32
+    - type: mrr_at_3
+      value: 87.323
+    - type: mrr_at_5
+      value: 87.94800000000001
+    - type: ndcg_at_1
+      value: 82.17999999999999
+    - type: ndcg_at_10
+      value: 88.9
+    - type: ndcg_at_100
+      value: 90.079
+    - type: ndcg_at_1000
+      value: 90.158
+    - type: ndcg_at_3
+      value: 86.18299999999999
+    - type: ndcg_at_5
+      value: 87.71799999999999
+    - type: precision_at_1
+      value: 82.17999999999999
+    - type: precision_at_10
+      value: 13.464
+    - type: precision_at_100
+      value: 1.533
+    - type: precision_at_1000
+      value: 0.157
+    - type: precision_at_3
+      value: 37.693
+    - type: precision_at_5
+      value: 24.792
+    - type: recall_at_1
+      value: 71.283
+    - type: recall_at_10
+      value: 95.742
+    - type: recall_at_100
+      value: 99.67200000000001
+    - type: recall_at_1000
+      value: 99.981
+    - type: recall_at_3
+      value: 87.888
+    - type: recall_at_5
+      value: 92.24
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB RedditClustering
+      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
+      split: test
+      type: mteb/reddit-clustering
+    metrics:
+    - type: v_measure
+      value: 56.24267063669042
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB RedditClusteringP2P
+      revision: 282350215ef01743dc01b456c7f5241fa8937f16
+      split: test
+      type: mteb/reddit-clustering-p2p
+    metrics:
+    - type: v_measure
+      value: 62.88056988932578
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB SCIDOCS
+      revision: None
+      split: test
+      type: scidocs
+    metrics:
+    - type: map_at_1
+      value: 4.903
+    - type: map_at_10
+      value: 13.202
+    - type: map_at_100
+      value: 15.5
+    - type: map_at_1000
+      value: 15.870999999999999
+    - type: map_at_3
+      value: 9.407
+    - type: map_at_5
+      value: 11.238
+    - type: mrr_at_1
+      value: 24.2
+    - type: mrr_at_10
+      value: 35.867
+    - type: mrr_at_100
+      value: 37.001
+    - type: mrr_at_1000
+      value: 37.043
+    - type: mrr_at_3
+      value: 32.5
+    - type: mrr_at_5
+      value: 34.35
+    - type: ndcg_at_1
+      value: 24.2
+    - type: ndcg_at_10
+      value: 21.731
+    - type: ndcg_at_100
+      value: 30.7
+    - type: ndcg_at_1000
+      value: 36.618
+    - type: ndcg_at_3
+      value: 20.72
+    - type: ndcg_at_5
+      value: 17.954
+    - type: precision_at_1
+      value: 24.2
+    - type: precision_at_10
+      value: 11.33
+    - type: precision_at_100
+      value: 2.4410000000000003
+    - type: precision_at_1000
+      value: 0.386
+    - type: precision_at_3
+      value: 19.667
+    - type: precision_at_5
+      value: 15.86
+    - type: recall_at_1
+      value: 4.903
+    - type: recall_at_10
+      value: 22.962
+    - type: recall_at_100
+      value: 49.563
+    - type: recall_at_1000
+      value: 78.238
+    - type: recall_at_3
+      value: 11.953
+    - type: recall_at_5
+      value: 16.067999999999998
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB SICK-R
+      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
+      split: test
+      type: mteb/sickr-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 84.12694254604078
+    - type: cos_sim_spearman
+      value: 80.30141815181918
+    - type: euclidean_pearson
+      value: 81.34015449877128
+    - type: euclidean_spearman
+      value: 80.13984197010849
+    - type: manhattan_pearson
+      value: 81.31767068124086
+    - type: manhattan_spearman
+      value: 80.11720513114103
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STS12
+      revision: a0d554a64d88156834ff5ae9920b964011b16384
+      split: test
+      type: mteb/sts12-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 86.13112984010417
+    - type: cos_sim_spearman
+      value: 78.03063573402875
+    - type: euclidean_pearson
+      value: 83.51928418844804
+    - type: euclidean_spearman
+      value: 78.4045235411144
+    - type: manhattan_pearson
+      value: 83.49981637388689
+    - type: manhattan_spearman
+      value: 78.4042575139372
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STS13
+      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
+      split: test
+      type: mteb/sts13-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 82.50327987379504
+    - type: cos_sim_spearman
+      value: 84.18556767756205
+    - type: euclidean_pearson
+      value: 82.69684424327679
+    - type: euclidean_spearman
+      value: 83.5368106038335
+    - type: manhattan_pearson
+      value: 82.57967581007374
+    - type: manhattan_spearman
+      value: 83.43009053133697
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STS14
+      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
+      split: test
+      type: mteb/sts14-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 82.50756863007814
+    - type: cos_sim_spearman
+      value: 82.27204331279108
+    - type: euclidean_pearson
+      value: 81.39535251429741
+    - type: euclidean_spearman
+      value: 81.84386626336239
+    - type: manhattan_pearson
+      value: 81.34281737280695
+    - type: manhattan_spearman
+      value: 81.81149375673166
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STS15
+      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
+      split: test
+      type: mteb/sts15-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 86.8727714856726
+    - type: cos_sim_spearman
+      value: 87.95738287792312
+    - type: euclidean_pearson
+      value: 86.62920602795887
+    - type: euclidean_spearman
+      value: 87.05207355381243
+    - type: manhattan_pearson
+      value: 86.53587918472225
+    - type: manhattan_spearman
+      value: 86.95382961029586
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STS16
+      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
+      split: test
+      type: mteb/sts16-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 83.52240359769479
+    - type: cos_sim_spearman
+      value: 85.47685776238286
+    - type: euclidean_pearson
+      value: 84.25815333483058
+    - type: euclidean_spearman
+      value: 85.27415639683198
+    - type: manhattan_pearson
+      value: 84.29127757025637
+    - type: manhattan_spearman
+      value: 85.30226224917351
+    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: 86.42501708915708
+    - type: cos_sim_spearman
+      value: 86.42276182795041
+    - type: euclidean_pearson
+      value: 86.5408207354761
+    - type: euclidean_spearman
+      value: 85.46096321750838
+    - type: manhattan_pearson
+      value: 86.54177303026881
+    - type: manhattan_spearman
+      value: 85.50313151916117
+    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: 64.86521089250766
+    - type: cos_sim_spearman
+      value: 65.94868540323003
+    - type: euclidean_pearson
+      value: 67.16569626533084
+    - type: euclidean_spearman
+      value: 66.37667004134917
+    - type: manhattan_pearson
+      value: 67.1482365102333
+    - type: manhattan_spearman
+      value: 66.53240122580029
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STSBenchmark
+      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
+      split: test
+      type: mteb/stsbenchmark-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 84.64746265365318
+    - type: cos_sim_spearman
+      value: 86.41888825906786
+    - type: euclidean_pearson
+      value: 85.27453642725811
+    - type: euclidean_spearman
+      value: 85.94095796602544
+    - type: manhattan_pearson
+      value: 85.28643660505334
+    - type: manhattan_spearman
+      value: 85.95028003260744
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB SciDocsRR
+      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
+      split: test
+      type: mteb/scidocs-reranking
+    metrics:
+    - type: map
+      value: 87.48903153618527
+    - type: mrr
+      value: 96.41081503826601
+    task:
+      type: Reranking
+  - dataset:
+      config: default
+      name: MTEB SciFact
+      revision: None
+      split: test
+      type: scifact
+    metrics:
+    - type: map_at_1
+      value: 58.594
+    - type: map_at_10
+      value: 69.296
+    - type: map_at_100
+      value: 69.782
+    - type: map_at_1000
+      value: 69.795
+    - type: map_at_3
+      value: 66.23
+    - type: map_at_5
+      value: 68.293
+    - type: mrr_at_1
+      value: 61.667
+    - type: mrr_at_10
+      value: 70.339
+    - type: mrr_at_100
+      value: 70.708
+    - type: mrr_at_1000
+      value: 70.722
+    - type: mrr_at_3
+      value: 68.0
+    - type: mrr_at_5
+      value: 69.56700000000001
+    - type: ndcg_at_1
+      value: 61.667
+    - type: ndcg_at_10
+      value: 74.039
+    - type: ndcg_at_100
+      value: 76.103
+    - type: ndcg_at_1000
+      value: 76.47800000000001
+    - type: ndcg_at_3
+      value: 68.967
+    - type: ndcg_at_5
+      value: 71.96900000000001
+    - type: precision_at_1
+      value: 61.667
+    - type: precision_at_10
+      value: 9.866999999999999
+    - type: precision_at_100
+      value: 1.097
+    - type: precision_at_1000
+      value: 0.11299999999999999
+    - type: precision_at_3
+      value: 27.111
+    - type: precision_at_5
+      value: 18.2
+    - type: recall_at_1
+      value: 58.594
+    - type: recall_at_10
+      value: 87.422
+    - type: recall_at_100
+      value: 96.667
+    - type: recall_at_1000
+      value: 99.667
+    - type: recall_at_3
+      value: 74.217
+    - type: recall_at_5
+      value: 81.539
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB SprintDuplicateQuestions
+      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
+      split: test
+      type: mteb/sprintduplicatequestions-pairclassification
+    metrics:
+    - type: cos_sim_accuracy
+      value: 99.85049504950496
+    - type: cos_sim_ap
+      value: 96.33111544137081
+    - type: cos_sim_f1
+      value: 92.35443037974684
+    - type: cos_sim_precision
+      value: 93.53846153846153
+    - type: cos_sim_recall
+      value: 91.2
+    - type: dot_accuracy
+      value: 99.82376237623762
+    - type: dot_ap
+      value: 95.38082527310888
+    - type: dot_f1
+      value: 90.90909090909092
+    - type: dot_precision
+      value: 92.90187891440502
+    - type: dot_recall
+      value: 89.0
+    - type: euclidean_accuracy
+      value: 99.84851485148515
+    - type: euclidean_ap
+      value: 96.32316003996347
+    - type: euclidean_f1
+      value: 92.2071392659628
+    - type: euclidean_precision
+      value: 92.71991911021233
+    - type: euclidean_recall
+      value: 91.7
+    - type: manhattan_accuracy
+      value: 99.84851485148515
+    - type: manhattan_ap
+      value: 96.3655668249217
+    - type: manhattan_f1
+      value: 92.18356026222895
+    - type: manhattan_precision
+      value: 92.98067141403867
+    - type: manhattan_recall
+      value: 91.4
+    - type: max_accuracy
+      value: 99.85049504950496
+    - type: max_ap
+      value: 96.3655668249217
+    - type: max_f1
+      value: 92.35443037974684
+    task:
+      type: PairClassification
+  - dataset:
+      config: default
+      name: MTEB StackExchangeClustering
+      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
+      split: test
+      type: mteb/stackexchange-clustering
+    metrics:
+    - type: v_measure
+      value: 65.94861371629051
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB StackExchangeClusteringP2P
+      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
+      split: test
+      type: mteb/stackexchange-clustering-p2p
+    metrics:
+    - type: v_measure
+      value: 35.009430451385
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB StackOverflowDupQuestions
+      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
+      split: test
+      type: mteb/stackoverflowdupquestions-reranking
+    metrics:
+    - type: map
+      value: 54.61164066427969
+    - type: mrr
+      value: 55.49710603938544
+    task:
+      type: Reranking
+  - dataset:
+      config: default
+      name: MTEB SummEval
+      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
+      split: test
+      type: mteb/summeval
+    metrics:
+    - type: cos_sim_pearson
+      value: 30.622620124907662
+    - type: cos_sim_spearman
+      value: 31.0678351356163
+    - type: dot_pearson
+      value: 30.863727693306814
+    - type: dot_spearman
+      value: 31.230306567021255
+    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: 2.011
+    - type: map_at_100
+      value: 10.974
+    - type: map_at_1000
+      value: 25.819
+    - type: map_at_3
+      value: 0.6649999999999999
+    - type: map_at_5
+      value: 1.076
+    - type: mrr_at_1
+      value: 86.0
+    - type: mrr_at_10
+      value: 91.8
+    - type: mrr_at_100
+      value: 91.8
+    - type: mrr_at_1000
+      value: 91.8
+    - type: mrr_at_3
+      value: 91.0
+    - type: mrr_at_5
+      value: 91.8
+    - type: ndcg_at_1
+      value: 82.0
+    - type: ndcg_at_10
+      value: 78.07300000000001
+    - type: ndcg_at_100
+      value: 58.231
+    - type: ndcg_at_1000
+      value: 51.153000000000006
+    - type: ndcg_at_3
+      value: 81.123
+    - type: ndcg_at_5
+      value: 81.059
+    - type: precision_at_1
+      value: 86.0
+    - type: precision_at_10
+      value: 83.0
+    - type: precision_at_100
+      value: 59.38
+    - type: precision_at_1000
+      value: 22.55
+    - type: precision_at_3
+      value: 87.333
+    - type: precision_at_5
+      value: 86.8
+    - type: recall_at_1
+      value: 0.22
+    - type: recall_at_10
+      value: 2.2079999999999997
+    - type: recall_at_100
+      value: 14.069
+    - type: recall_at_1000
+      value: 47.678
+    - type: recall_at_3
+      value: 0.7040000000000001
+    - type: recall_at_5
+      value: 1.161
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB Touche2020
+      revision: None
+      split: test
+      type: webis-touche2020
+    metrics:
+    - type: map_at_1
+      value: 2.809
+    - type: map_at_10
+      value: 10.394
+    - type: map_at_100
+      value: 16.598
+    - type: map_at_1000
+      value: 18.142
+    - type: map_at_3
+      value: 5.572
+    - type: map_at_5
+      value: 7.1370000000000005
+    - type: mrr_at_1
+      value: 32.653
+    - type: mrr_at_10
+      value: 46.564
+    - type: mrr_at_100
+      value: 47.469
+    - type: mrr_at_1000
+      value: 47.469
+    - type: mrr_at_3
+      value: 42.177
+    - type: mrr_at_5
+      value: 44.524
+    - type: ndcg_at_1
+      value: 30.612000000000002
+    - type: ndcg_at_10
+      value: 25.701
+    - type: ndcg_at_100
+      value: 37.532
+    - type: ndcg_at_1000
+      value: 48.757
+    - type: ndcg_at_3
+      value: 28.199999999999996
+    - type: ndcg_at_5
+      value: 25.987
+    - type: precision_at_1
+      value: 32.653
+    - type: precision_at_10
+      value: 23.469
+    - type: precision_at_100
+      value: 7.9799999999999995
+    - type: precision_at_1000
+      value: 1.5350000000000001
+    - type: precision_at_3
+      value: 29.932
+    - type: precision_at_5
+      value: 26.122
+    - type: recall_at_1
+      value: 2.809
+    - type: recall_at_10
+      value: 16.887
+    - type: recall_at_100
+      value: 48.67
+    - type: recall_at_1000
+      value: 82.89699999999999
+    - type: recall_at_3
+      value: 6.521000000000001
+    - type: recall_at_5
+      value: 9.609
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB ToxicConversationsClassification
+      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
+      split: test
+      type: mteb/toxic_conversations_50k
+    metrics:
+    - type: accuracy
+      value: 71.57860000000001
+    - type: ap
+      value: 13.82629211536393
+    - type: f1
+      value: 54.59860966183956
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB TweetSentimentExtractionClassification
+      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
+      split: test
+      type: mteb/tweet_sentiment_extraction
+    metrics:
+    - type: accuracy
+      value: 59.38030560271647
+    - type: f1
+      value: 59.69685552567865
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB TwentyNewsgroupsClustering
+      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
+      split: test
+      type: mteb/twentynewsgroups-clustering
+    metrics:
+    - type: v_measure
+      value: 51.4736717043405
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB TwitterSemEval2015
+      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
+      split: test
+      type: mteb/twittersemeval2015-pairclassification
+    metrics:
+    - type: cos_sim_accuracy
+      value: 86.92853311080646
+    - type: cos_sim_ap
+      value: 77.67872502591382
+    - type: cos_sim_f1
+      value: 70.33941236068895
+    - type: cos_sim_precision
+      value: 67.63273258645884
+    - type: cos_sim_recall
+      value: 73.27176781002639
+    - type: dot_accuracy
+      value: 85.79603027954938
+    - type: dot_ap
+      value: 73.73786190233379
+    - type: dot_f1
+      value: 67.3437901774235
+    - type: dot_precision
+      value: 65.67201604814443
+    - type: dot_recall
+      value: 69.10290237467018
+    - type: euclidean_accuracy
+      value: 86.94045419324074
+    - type: euclidean_ap
+      value: 77.6687791535167
+    - type: euclidean_f1
+      value: 70.47209214023542
+    - type: euclidean_precision
+      value: 67.7207492094381
+    - type: euclidean_recall
+      value: 73.45646437994723
+    - type: manhattan_accuracy
+      value: 86.87488823985218
+    - type: manhattan_ap
+      value: 77.63373392430728
+    - type: manhattan_f1
+      value: 70.40920716112532
+    - type: manhattan_precision
+      value: 68.31265508684864
+    - type: manhattan_recall
+      value: 72.63852242744063
+    - type: max_accuracy
+      value: 86.94045419324074
+    - type: max_ap
+      value: 77.67872502591382
+    - type: max_f1
+      value: 70.47209214023542
+    task:
+      type: PairClassification
+  - dataset:
+      config: default
+      name: MTEB TwitterURLCorpus
+      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
+      split: test
+      type: mteb/twitterurlcorpus-pairclassification
+    metrics:
+    - type: cos_sim_accuracy
+      value: 88.67155664221679
+    - type: cos_sim_ap
+      value: 85.64591703003417
+    - type: cos_sim_f1
+      value: 77.59531005352656
+    - type: cos_sim_precision
+      value: 73.60967184801382
+    - type: cos_sim_recall
+      value: 82.03726516784724
+    - type: dot_accuracy
+      value: 88.41541506578181
+    - type: dot_ap
+      value: 84.6482788957769
+    - type: dot_f1
+      value: 77.04748541466657
+    - type: dot_precision
+      value: 74.02440754931176
+    - type: dot_recall
+      value: 80.3279950723745
+    - type: euclidean_accuracy
+      value: 88.63080684596576
+    - type: euclidean_ap
+      value: 85.44570045321562
+    - type: euclidean_f1
+      value: 77.28769403336106
+    - type: euclidean_precision
+      value: 72.90600040958427
+    - type: euclidean_recall
+      value: 82.22975053895904
+    - type: manhattan_accuracy
+      value: 88.59393798269105
+    - type: manhattan_ap
+      value: 85.40271361038187
+    - type: manhattan_f1
+      value: 77.17606419344392
+    - type: manhattan_precision
+      value: 72.4447747078295
+    - type: manhattan_recall
+      value: 82.5685247921158
+    - type: max_accuracy
+      value: 88.67155664221679
+    - type: max_ap
+      value: 85.64591703003417
+    - type: max_f1
+      value: 77.59531005352656
+    task:
+      type: PairClassification
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- transformers
+- mteb
+- onnx
+- teradata
+---
+# A Teradata Vantage compatible Embeddings Model
+# BAAI/bge-base-en-v1.5
+## Overview of this Model
+An Embedding Model which maps text (sentence/ paragraphs) into a vector.  The [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-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.
+- 109.48M params (Sizes in ONNX format - "fp32": 415.72MB, "int8": 104.75MB, "uint8": 104.75MB)
+- 512 maximum input tokens
+- 768 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-base-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.
+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-base-en-v1.5"
+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 [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-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-base-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 ADDED Viewed

	@@ -0,0 +1,34 @@

+{
+  "_attn_implementation_autoset": true,
+  "_name_or_path": "BAAI/bge-base-en-v1.5",
+  "architectures": [
+    "BertModel"
+  ],
+  "attention_probs_dropout_prob": 0.1,
+  "classifier_dropout": null,
+  "export_model_type": "transformer",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 768,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 3072,
+  "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 ADDED Viewed

	@@ -0,0 +1,11 @@

+{
+    "model_id": "BAAI/bge-base-en-v1.5",
+    "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 ADDED Viewed

	@@ -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 ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:81839c8d8c266e14a40b2f1fe9dec105a8c5c242c1fe69933c6211dc762d8dd8
+size 435913661

onnx/model_int8.onnx ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:59e021271eec474fa6eb675fa0ad4634a47446fda83a0792945b880e1130a387
+size 109837980

onnx/model_uint8.onnx ADDED Viewed

	@@ -0,0 +1,3 @@

+version https://git-lfs.github.com/spec/v1
+oid sha256:100874a3b224a8d15c5069e8ebd621b1bcef47626f1439ed7f8f85fad90f50a8
+size 109838017

special_tokens_map.json ADDED Viewed

	@@ -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 ADDED Viewed

	@@ -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 ADDED Viewed

	@@ -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.json ADDED Viewed

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tokenizer_config.json ADDED Viewed

	@@ -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]"
+}

vocab.txt ADDED Viewed

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