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-large-en-v1.5
+  results:
+  - dataset:
+      config: en
+      name: MTEB AmazonCounterfactualClassification (en)
+      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
+      split: test
+      type: mteb/amazon_counterfactual
+    metrics:
+    - type: accuracy
+      value: 75.8507462686567
+    - type: ap
+      value: 38.566457320228245
+    - type: f1
+      value: 69.69386648043475
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB AmazonPolarityClassification
+      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
+      split: test
+      type: mteb/amazon_polarity
+    metrics:
+    - type: accuracy
+      value: 92.416675
+    - type: ap
+      value: 89.1928861155922
+    - type: f1
+      value: 92.39477019574215
+    task:
+      type: Classification
+  - dataset:
+      config: en
+      name: MTEB AmazonReviewsClassification (en)
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+      split: test
+      type: mteb/amazon_reviews_multi
+    metrics:
+    - type: accuracy
+      value: 48.175999999999995
+    - type: f1
+      value: 47.80712792870253
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB ArguAna
+      revision: None
+      split: test
+      type: arguana
+    metrics:
+    - type: map_at_1
+      value: 40.184999999999995
+    - type: map_at_10
+      value: 55.654
+    - type: map_at_100
+      value: 56.25
+    - type: map_at_1000
+      value: 56.255
+    - type: map_at_3
+      value: 51.742999999999995
+    - type: map_at_5
+      value: 54.129000000000005
+    - type: mrr_at_1
+      value: 40.967
+    - type: mrr_at_10
+      value: 55.96
+    - type: mrr_at_100
+      value: 56.54900000000001
+    - type: mrr_at_1000
+      value: 56.554
+    - type: mrr_at_3
+      value: 51.980000000000004
+    - type: mrr_at_5
+      value: 54.44
+    - type: ndcg_at_1
+      value: 40.184999999999995
+    - type: ndcg_at_10
+      value: 63.542
+    - type: ndcg_at_100
+      value: 65.96499999999999
+    - type: ndcg_at_1000
+      value: 66.08699999999999
+    - type: ndcg_at_3
+      value: 55.582
+    - type: ndcg_at_5
+      value: 59.855000000000004
+    - type: precision_at_1
+      value: 40.184999999999995
+    - type: precision_at_10
+      value: 8.841000000000001
+    - type: precision_at_100
+      value: 0.987
+    - type: precision_at_1000
+      value: 0.1
+    - type: precision_at_3
+      value: 22.238
+    - type: precision_at_5
+      value: 15.405
+    - type: recall_at_1
+      value: 40.184999999999995
+    - type: recall_at_10
+      value: 88.407
+    - type: recall_at_100
+      value: 98.72
+    - type: recall_at_1000
+      value: 99.644
+    - type: recall_at_3
+      value: 66.714
+    - type: recall_at_5
+      value: 77.027
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB ArxivClusteringP2P
+      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
+      split: test
+      type: mteb/arxiv-clustering-p2p
+    metrics:
+    - type: v_measure
+      value: 48.567077926750066
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB ArxivClusteringS2S
+      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
+      split: test
+      type: mteb/arxiv-clustering-s2s
+    metrics:
+    - type: v_measure
+      value: 43.19453389182364
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB AskUbuntuDupQuestions
+      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
+      split: test
+      type: mteb/askubuntudupquestions-reranking
+    metrics:
+    - type: map
+      value: 64.46555939623092
+    - type: mrr
+      value: 77.82361605768807
+    task:
+      type: Reranking
+  - dataset:
+      config: default
+      name: MTEB BIOSSES
+      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
+      split: test
+      type: mteb/biosses-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 84.9554128814735
+    - type: cos_sim_spearman
+      value: 84.65373612172036
+    - type: euclidean_pearson
+      value: 83.2905059954138
+    - type: euclidean_spearman
+      value: 84.52240782811128
+    - type: manhattan_pearson
+      value: 82.99533802997436
+    - type: manhattan_spearman
+      value: 84.20673798475734
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB Banking77Classification
+      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
+      split: test
+      type: mteb/banking77
+    metrics:
+    - type: accuracy
+      value: 87.78896103896103
+    - type: f1
+      value: 87.77189310964883
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB BiorxivClusteringP2P
+      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
+      split: test
+      type: mteb/biorxiv-clustering-p2p
+    metrics:
+    - type: v_measure
+      value: 39.714538337650495
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB BiorxivClusteringS2S
+      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
+      split: test
+      type: mteb/biorxiv-clustering-s2s
+    metrics:
+    - type: v_measure
+      value: 36.90108349284447
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB CQADupstackAndroidRetrieval
+      revision: None
+      split: test
+      type: BeIR/cqadupstack
+    metrics:
+    - type: map_at_1
+      value: 32.795
+    - type: map_at_10
+      value: 43.669000000000004
+    - type: map_at_100
+      value: 45.151
+    - type: map_at_1000
+      value: 45.278
+    - type: map_at_3
+      value: 40.006
+    - type: map_at_5
+      value: 42.059999999999995
+    - type: mrr_at_1
+      value: 39.771
+    - type: mrr_at_10
+      value: 49.826
+    - type: mrr_at_100
+      value: 50.504000000000005
+    - type: mrr_at_1000
+      value: 50.549
+    - type: mrr_at_3
+      value: 47.115
+    - type: mrr_at_5
+      value: 48.832
+    - type: ndcg_at_1
+      value: 39.771
+    - type: ndcg_at_10
+      value: 50.217999999999996
+    - type: ndcg_at_100
+      value: 55.454
+    - type: ndcg_at_1000
+      value: 57.37
+    - type: ndcg_at_3
+      value: 44.885000000000005
+    - type: ndcg_at_5
+      value: 47.419
+    - type: precision_at_1
+      value: 39.771
+    - type: precision_at_10
+      value: 9.642000000000001
+    - type: precision_at_100
+      value: 1.538
+    - type: precision_at_1000
+      value: 0.198
+    - type: precision_at_3
+      value: 21.268
+    - type: precision_at_5
+      value: 15.536
+    - type: recall_at_1
+      value: 32.795
+    - type: recall_at_10
+      value: 62.580999999999996
+    - type: recall_at_100
+      value: 84.438
+    - type: recall_at_1000
+      value: 96.492
+    - type: recall_at_3
+      value: 47.071000000000005
+    - type: recall_at_5
+      value: 54.079
+    - type: map_at_1
+      value: 32.671
+    - type: map_at_10
+      value: 43.334
+    - type: map_at_100
+      value: 44.566
+    - type: map_at_1000
+      value: 44.702999999999996
+    - type: map_at_3
+      value: 40.343
+    - type: map_at_5
+      value: 41.983
+    - type: mrr_at_1
+      value: 40.764
+    - type: mrr_at_10
+      value: 49.382
+    - type: mrr_at_100
+      value: 49.988
+    - type: mrr_at_1000
+      value: 50.03300000000001
+    - type: mrr_at_3
+      value: 47.293
+    - type: mrr_at_5
+      value: 48.51
+    - type: ndcg_at_1
+      value: 40.764
+    - type: ndcg_at_10
+      value: 49.039
+    - type: ndcg_at_100
+      value: 53.259
+    - type: ndcg_at_1000
+      value: 55.253
+    - type: ndcg_at_3
+      value: 45.091
+    - type: ndcg_at_5
+      value: 46.839999999999996
+    - type: precision_at_1
+      value: 40.764
+    - type: precision_at_10
+      value: 9.191
+    - type: precision_at_100
+      value: 1.476
+    - type: precision_at_1000
+      value: 0.19499999999999998
+    - type: precision_at_3
+      value: 21.72
+    - type: precision_at_5
+      value: 15.299
+    - type: recall_at_1
+      value: 32.671
+    - type: recall_at_10
+      value: 58.816
+    - type: recall_at_100
+      value: 76.654
+    - type: recall_at_1000
+      value: 89.05999999999999
+    - type: recall_at_3
+      value: 46.743
+    - type: recall_at_5
+      value: 51.783
+    - type: map_at_1
+      value: 40.328
+    - type: map_at_10
+      value: 53.32599999999999
+    - type: map_at_100
+      value: 54.37499999999999
+    - type: map_at_1000
+      value: 54.429
+    - type: map_at_3
+      value: 49.902
+    - type: map_at_5
+      value: 52.002
+    - type: mrr_at_1
+      value: 46.332
+    - type: mrr_at_10
+      value: 56.858
+    - type: mrr_at_100
+      value: 57.522
+    - type: mrr_at_1000
+      value: 57.54899999999999
+    - type: mrr_at_3
+      value: 54.472
+    - type: mrr_at_5
+      value: 55.996
+    - type: ndcg_at_1
+      value: 46.332
+    - type: ndcg_at_10
+      value: 59.313
+    - type: ndcg_at_100
+      value: 63.266999999999996
+    - type: ndcg_at_1000
+      value: 64.36
+    - type: ndcg_at_3
+      value: 53.815000000000005
+    - type: ndcg_at_5
+      value: 56.814
+    - type: precision_at_1
+      value: 46.332
+    - type: precision_at_10
+      value: 9.53
+    - type: precision_at_100
+      value: 1.238
+    - type: precision_at_1000
+      value: 0.13699999999999998
+    - type: precision_at_3
+      value: 24.054000000000002
+    - type: precision_at_5
+      value: 16.589000000000002
+    - type: recall_at_1
+      value: 40.328
+    - type: recall_at_10
+      value: 73.421
+    - type: recall_at_100
+      value: 90.059
+    - type: recall_at_1000
+      value: 97.81
+    - type: recall_at_3
+      value: 59.009
+    - type: recall_at_5
+      value: 66.352
+    - type: map_at_1
+      value: 27.424
+    - type: map_at_10
+      value: 36.332
+    - type: map_at_100
+      value: 37.347
+    - type: map_at_1000
+      value: 37.422
+    - type: map_at_3
+      value: 33.743
+    - type: map_at_5
+      value: 35.176
+    - type: mrr_at_1
+      value: 29.153000000000002
+    - type: mrr_at_10
+      value: 38.233
+    - type: mrr_at_100
+      value: 39.109
+    - type: mrr_at_1000
+      value: 39.164
+    - type: mrr_at_3
+      value: 35.876000000000005
+    - type: mrr_at_5
+      value: 37.169000000000004
+    - type: ndcg_at_1
+      value: 29.153000000000002
+    - type: ndcg_at_10
+      value: 41.439
+    - type: ndcg_at_100
+      value: 46.42
+    - type: ndcg_at_1000
+      value: 48.242000000000004
+    - type: ndcg_at_3
+      value: 36.362
+    - type: ndcg_at_5
+      value: 38.743
+    - type: precision_at_1
+      value: 29.153000000000002
+    - type: precision_at_10
+      value: 6.315999999999999
+    - type: precision_at_100
+      value: 0.927
+    - type: precision_at_1000
+      value: 0.11199999999999999
+    - type: precision_at_3
+      value: 15.443000000000001
+    - type: precision_at_5
+      value: 10.644
+    - type: recall_at_1
+      value: 27.424
+    - type: recall_at_10
+      value: 55.364000000000004
+    - type: recall_at_100
+      value: 78.211
+    - type: recall_at_1000
+      value: 91.74600000000001
+    - type: recall_at_3
+      value: 41.379
+    - type: recall_at_5
+      value: 47.14
+    - type: map_at_1
+      value: 19.601
+    - type: map_at_10
+      value: 27.826
+    - type: map_at_100
+      value: 29.017
+    - type: map_at_1000
+      value: 29.137
+    - type: map_at_3
+      value: 25.125999999999998
+    - type: map_at_5
+      value: 26.765
+    - type: mrr_at_1
+      value: 24.005000000000003
+    - type: mrr_at_10
+      value: 32.716
+    - type: mrr_at_100
+      value: 33.631
+    - type: mrr_at_1000
+      value: 33.694
+    - type: mrr_at_3
+      value: 29.934
+    - type: mrr_at_5
+      value: 31.630999999999997
+    - type: ndcg_at_1
+      value: 24.005000000000003
+    - type: ndcg_at_10
+      value: 33.158
+    - type: ndcg_at_100
+      value: 38.739000000000004
+    - type: ndcg_at_1000
+      value: 41.495
+    - type: ndcg_at_3
+      value: 28.185
+    - type: ndcg_at_5
+      value: 30.796
+    - type: precision_at_1
+      value: 24.005000000000003
+    - type: precision_at_10
+      value: 5.908
+    - type: precision_at_100
+      value: 1.005
+    - type: precision_at_1000
+      value: 0.13899999999999998
+    - type: precision_at_3
+      value: 13.391
+    - type: precision_at_5
+      value: 9.876
+    - type: recall_at_1
+      value: 19.601
+    - type: recall_at_10
+      value: 44.746
+    - type: recall_at_100
+      value: 68.82300000000001
+    - type: recall_at_1000
+      value: 88.215
+    - type: recall_at_3
+      value: 31.239
+    - type: recall_at_5
+      value: 37.695
+    - type: map_at_1
+      value: 30.130000000000003
+    - type: map_at_10
+      value: 40.96
+    - type: map_at_100
+      value: 42.282
+    - type: map_at_1000
+      value: 42.392
+    - type: map_at_3
+      value: 37.889
+    - type: map_at_5
+      value: 39.661
+    - type: mrr_at_1
+      value: 36.958999999999996
+    - type: mrr_at_10
+      value: 46.835
+    - type: mrr_at_100
+      value: 47.644
+    - type: mrr_at_1000
+      value: 47.688
+    - type: mrr_at_3
+      value: 44.562000000000005
+    - type: mrr_at_5
+      value: 45.938
+    - type: ndcg_at_1
+      value: 36.958999999999996
+    - type: ndcg_at_10
+      value: 47.06
+    - type: ndcg_at_100
+      value: 52.345
+    - type: ndcg_at_1000
+      value: 54.35
+    - type: ndcg_at_3
+      value: 42.301
+    - type: ndcg_at_5
+      value: 44.635999999999996
+    - type: precision_at_1
+      value: 36.958999999999996
+    - type: precision_at_10
+      value: 8.479000000000001
+    - type: precision_at_100
+      value: 1.284
+    - type: precision_at_1000
+      value: 0.163
+    - type: precision_at_3
+      value: 20.244
+    - type: precision_at_5
+      value: 14.224999999999998
+    - type: recall_at_1
+      value: 30.130000000000003
+    - type: recall_at_10
+      value: 59.27
+    - type: recall_at_100
+      value: 81.195
+    - type: recall_at_1000
+      value: 94.21199999999999
+    - type: recall_at_3
+      value: 45.885
+    - type: recall_at_5
+      value: 52.016
+    - type: map_at_1
+      value: 26.169999999999998
+    - type: map_at_10
+      value: 36.451
+    - type: map_at_100
+      value: 37.791000000000004
+    - type: map_at_1000
+      value: 37.897
+    - type: map_at_3
+      value: 33.109
+    - type: map_at_5
+      value: 34.937000000000005
+    - type: mrr_at_1
+      value: 32.877
+    - type: mrr_at_10
+      value: 42.368
+    - type: mrr_at_100
+      value: 43.201
+    - type: mrr_at_1000
+      value: 43.259
+    - type: mrr_at_3
+      value: 39.763999999999996
+    - type: mrr_at_5
+      value: 41.260000000000005
+    - type: ndcg_at_1
+      value: 32.877
+    - type: ndcg_at_10
+      value: 42.659000000000006
+    - type: ndcg_at_100
+      value: 48.161
+    - type: ndcg_at_1000
+      value: 50.345
+    - type: ndcg_at_3
+      value: 37.302
+    - type: ndcg_at_5
+      value: 39.722
+    - type: precision_at_1
+      value: 32.877
+    - type: precision_at_10
+      value: 7.9
+    - type: precision_at_100
+      value: 1.236
+    - type: precision_at_1000
+      value: 0.158
+    - type: precision_at_3
+      value: 17.846
+    - type: precision_at_5
+      value: 12.9
+    - type: recall_at_1
+      value: 26.169999999999998
+    - type: recall_at_10
+      value: 55.35
+    - type: recall_at_100
+      value: 78.755
+    - type: recall_at_1000
+      value: 93.518
+    - type: recall_at_3
+      value: 40.176
+    - type: recall_at_5
+      value: 46.589000000000006
+    - type: map_at_1
+      value: 27.15516666666667
+    - type: map_at_10
+      value: 36.65741666666667
+    - type: map_at_100
+      value: 37.84991666666666
+    - type: map_at_1000
+      value: 37.96316666666667
+    - type: map_at_3
+      value: 33.74974999999999
+    - type: map_at_5
+      value: 35.3765
+    - type: mrr_at_1
+      value: 32.08233333333334
+    - type: mrr_at_10
+      value: 41.033833333333334
+    - type: mrr_at_100
+      value: 41.84524999999999
+    - type: mrr_at_1000
+      value: 41.89983333333333
+    - type: mrr_at_3
+      value: 38.62008333333333
+    - type: mrr_at_5
+      value: 40.03441666666666
+    - type: ndcg_at_1
+      value: 32.08233333333334
+    - type: ndcg_at_10
+      value: 42.229
+    - type: ndcg_at_100
+      value: 47.26716666666667
+    - type: ndcg_at_1000
+      value: 49.43466666666667
+    - type: ndcg_at_3
+      value: 37.36408333333333
+    - type: ndcg_at_5
+      value: 39.6715
+    - type: precision_at_1
+      value: 32.08233333333334
+    - type: precision_at_10
+      value: 7.382583333333334
+    - type: precision_at_100
+      value: 1.16625
+    - type: precision_at_1000
+      value: 0.15408333333333332
+    - type: precision_at_3
+      value: 17.218
+    - type: precision_at_5
+      value: 12.21875
+    - type: recall_at_1
+      value: 27.15516666666667
+    - type: recall_at_10
+      value: 54.36683333333333
+    - type: recall_at_100
+      value: 76.37183333333333
+    - type: recall_at_1000
+      value: 91.26183333333333
+    - type: recall_at_3
+      value: 40.769916666666674
+    - type: recall_at_5
+      value: 46.702333333333335
+    - type: map_at_1
+      value: 25.749
+    - type: map_at_10
+      value: 33.001999999999995
+    - type: map_at_100
+      value: 33.891
+    - type: map_at_1000
+      value: 33.993
+    - type: map_at_3
+      value: 30.703999999999997
+    - type: map_at_5
+      value: 31.959
+    - type: mrr_at_1
+      value: 28.834
+    - type: mrr_at_10
+      value: 35.955
+    - type: mrr_at_100
+      value: 36.709
+    - type: mrr_at_1000
+      value: 36.779
+    - type: mrr_at_3
+      value: 33.947
+    - type: mrr_at_5
+      value: 35.089
+    - type: ndcg_at_1
+      value: 28.834
+    - type: ndcg_at_10
+      value: 37.329
+    - type: ndcg_at_100
+      value: 41.79
+    - type: ndcg_at_1000
+      value: 44.169000000000004
+    - type: ndcg_at_3
+      value: 33.184999999999995
+    - type: ndcg_at_5
+      value: 35.107
+    - type: precision_at_1
+      value: 28.834
+    - type: precision_at_10
+      value: 5.7669999999999995
+    - type: precision_at_100
+      value: 0.876
+    - type: precision_at_1000
+      value: 0.11399999999999999
+    - type: precision_at_3
+      value: 14.213000000000001
+    - type: precision_at_5
+      value: 9.754999999999999
+    - type: recall_at_1
+      value: 25.749
+    - type: recall_at_10
+      value: 47.791
+    - type: recall_at_100
+      value: 68.255
+    - type: recall_at_1000
+      value: 85.749
+    - type: recall_at_3
+      value: 36.199
+    - type: recall_at_5
+      value: 41.071999999999996
+    - type: map_at_1
+      value: 17.777
+    - type: map_at_10
+      value: 25.201
+    - type: map_at_100
+      value: 26.423999999999996
+    - type: map_at_1000
+      value: 26.544
+    - type: map_at_3
+      value: 22.869
+    - type: map_at_5
+      value: 24.023
+    - type: mrr_at_1
+      value: 21.473
+    - type: mrr_at_10
+      value: 29.12
+    - type: mrr_at_100
+      value: 30.144
+    - type: mrr_at_1000
+      value: 30.215999999999998
+    - type: mrr_at_3
+      value: 26.933
+    - type: mrr_at_5
+      value: 28.051
+    - type: ndcg_at_1
+      value: 21.473
+    - type: ndcg_at_10
+      value: 30.003
+    - type: ndcg_at_100
+      value: 35.766
+    - type: ndcg_at_1000
+      value: 38.501000000000005
+    - type: ndcg_at_3
+      value: 25.773000000000003
+    - type: ndcg_at_5
+      value: 27.462999999999997
+    - type: precision_at_1
+      value: 21.473
+    - type: precision_at_10
+      value: 5.482
+    - type: precision_at_100
+      value: 0.975
+    - type: precision_at_1000
+      value: 0.13799999999999998
+    - type: precision_at_3
+      value: 12.205
+    - type: precision_at_5
+      value: 8.692
+    - type: recall_at_1
+      value: 17.777
+    - type: recall_at_10
+      value: 40.582
+    - type: recall_at_100
+      value: 66.305
+    - type: recall_at_1000
+      value: 85.636
+    - type: recall_at_3
+      value: 28.687
+    - type: recall_at_5
+      value: 33.089
+    - type: map_at_1
+      value: 26.677
+    - type: map_at_10
+      value: 36.309000000000005
+    - type: map_at_100
+      value: 37.403999999999996
+    - type: map_at_1000
+      value: 37.496
+    - type: map_at_3
+      value: 33.382
+    - type: map_at_5
+      value: 34.98
+    - type: mrr_at_1
+      value: 31.343
+    - type: mrr_at_10
+      value: 40.549
+    - type: mrr_at_100
+      value: 41.342
+    - type: mrr_at_1000
+      value: 41.397
+    - type: mrr_at_3
+      value: 38.029
+    - type: mrr_at_5
+      value: 39.451
+    - type: ndcg_at_1
+      value: 31.343
+    - type: ndcg_at_10
+      value: 42.1
+    - type: ndcg_at_100
+      value: 47.089999999999996
+    - type: ndcg_at_1000
+      value: 49.222
+    - type: ndcg_at_3
+      value: 36.836999999999996
+    - type: ndcg_at_5
+      value: 39.21
+    - type: precision_at_1
+      value: 31.343
+    - type: precision_at_10
+      value: 7.164
+    - type: precision_at_100
+      value: 1.0959999999999999
+    - type: precision_at_1000
+      value: 0.13899999999999998
+    - type: precision_at_3
+      value: 16.915
+    - type: precision_at_5
+      value: 11.940000000000001
+    - type: recall_at_1
+      value: 26.677
+    - type: recall_at_10
+      value: 55.54599999999999
+    - type: recall_at_100
+      value: 77.094
+    - type: recall_at_1000
+      value: 92.01
+    - type: recall_at_3
+      value: 41.191
+    - type: recall_at_5
+      value: 47.006
+    - type: map_at_1
+      value: 24.501
+    - type: map_at_10
+      value: 33.102
+    - type: map_at_100
+      value: 34.676
+    - type: map_at_1000
+      value: 34.888000000000005
+    - type: map_at_3
+      value: 29.944
+    - type: map_at_5
+      value: 31.613999999999997
+    - type: mrr_at_1
+      value: 29.447000000000003
+    - type: mrr_at_10
+      value: 37.996
+    - type: mrr_at_100
+      value: 38.946
+    - type: mrr_at_1000
+      value: 38.995000000000005
+    - type: mrr_at_3
+      value: 35.079
+    - type: mrr_at_5
+      value: 36.69
+    - type: ndcg_at_1
+      value: 29.447000000000003
+    - type: ndcg_at_10
+      value: 39.232
+    - type: ndcg_at_100
+      value: 45.247
+    - type: ndcg_at_1000
+      value: 47.613
+    - type: ndcg_at_3
+      value: 33.922999999999995
+    - type: ndcg_at_5
+      value: 36.284
+    - type: precision_at_1
+      value: 29.447000000000003
+    - type: precision_at_10
+      value: 7.648000000000001
+    - type: precision_at_100
+      value: 1.516
+    - type: precision_at_1000
+      value: 0.23900000000000002
+    - type: precision_at_3
+      value: 16.008
+    - type: precision_at_5
+      value: 11.779
+    - type: recall_at_1
+      value: 24.501
+    - type: recall_at_10
+      value: 51.18899999999999
+    - type: recall_at_100
+      value: 78.437
+    - type: recall_at_1000
+      value: 92.842
+    - type: recall_at_3
+      value: 35.808
+    - type: recall_at_5
+      value: 42.197
+    - type: map_at_1
+      value: 22.039
+    - type: map_at_10
+      value: 30.377
+    - type: map_at_100
+      value: 31.275
+    - type: map_at_1000
+      value: 31.379
+    - type: map_at_3
+      value: 27.98
+    - type: map_at_5
+      value: 29.358
+    - type: mrr_at_1
+      value: 24.03
+    - type: mrr_at_10
+      value: 32.568000000000005
+    - type: mrr_at_100
+      value: 33.403
+    - type: mrr_at_1000
+      value: 33.475
+    - type: mrr_at_3
+      value: 30.436999999999998
+    - type: mrr_at_5
+      value: 31.796000000000003
+    - type: ndcg_at_1
+      value: 24.03
+    - type: ndcg_at_10
+      value: 35.198
+    - type: ndcg_at_100
+      value: 39.668
+    - type: ndcg_at_1000
+      value: 42.296
+    - type: ndcg_at_3
+      value: 30.709999999999997
+    - type: ndcg_at_5
+      value: 33.024
+    - type: precision_at_1
+      value: 24.03
+    - type: precision_at_10
+      value: 5.564
+    - type: precision_at_100
+      value: 0.828
+    - type: precision_at_1000
+      value: 0.117
+    - type: precision_at_3
+      value: 13.309000000000001
+    - type: precision_at_5
+      value: 9.39
+    - type: recall_at_1
+      value: 22.039
+    - type: recall_at_10
+      value: 47.746
+    - type: recall_at_100
+      value: 68.23599999999999
+    - type: recall_at_1000
+      value: 87.852
+    - type: recall_at_3
+      value: 35.852000000000004
+    - type: recall_at_5
+      value: 41.410000000000004
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB ClimateFEVER
+      revision: None
+      split: test
+      type: climate-fever
+    metrics:
+    - type: map_at_1
+      value: 15.692999999999998
+    - type: map_at_10
+      value: 26.903
+    - type: map_at_100
+      value: 28.987000000000002
+    - type: map_at_1000
+      value: 29.176999999999996
+    - type: map_at_3
+      value: 22.137
+    - type: map_at_5
+      value: 24.758
+    - type: mrr_at_1
+      value: 35.57
+    - type: mrr_at_10
+      value: 47.821999999999996
+    - type: mrr_at_100
+      value: 48.608000000000004
+    - type: mrr_at_1000
+      value: 48.638999999999996
+    - type: mrr_at_3
+      value: 44.452000000000005
+    - type: mrr_at_5
+      value: 46.546
+    - type: ndcg_at_1
+      value: 35.57
+    - type: ndcg_at_10
+      value: 36.567
+    - type: ndcg_at_100
+      value: 44.085
+    - type: ndcg_at_1000
+      value: 47.24
+    - type: ndcg_at_3
+      value: 29.964000000000002
+    - type: ndcg_at_5
+      value: 32.511
+    - type: precision_at_1
+      value: 35.57
+    - type: precision_at_10
+      value: 11.485
+    - type: precision_at_100
+      value: 1.9619999999999997
+    - type: precision_at_1000
+      value: 0.256
+    - type: precision_at_3
+      value: 22.237000000000002
+    - type: precision_at_5
+      value: 17.471999999999998
+    - type: recall_at_1
+      value: 15.692999999999998
+    - type: recall_at_10
+      value: 43.056
+    - type: recall_at_100
+      value: 68.628
+    - type: recall_at_1000
+      value: 86.075
+    - type: recall_at_3
+      value: 26.918999999999997
+    - type: recall_at_5
+      value: 34.14
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB DBPedia
+      revision: None
+      split: test
+      type: dbpedia-entity
+    metrics:
+    - type: map_at_1
+      value: 9.53
+    - type: map_at_10
+      value: 20.951
+    - type: map_at_100
+      value: 30.136000000000003
+    - type: map_at_1000
+      value: 31.801000000000002
+    - type: map_at_3
+      value: 15.021
+    - type: map_at_5
+      value: 17.471999999999998
+    - type: mrr_at_1
+      value: 71.0
+    - type: mrr_at_10
+      value: 79.176
+    - type: mrr_at_100
+      value: 79.418
+    - type: mrr_at_1000
+      value: 79.426
+    - type: mrr_at_3
+      value: 78.125
+    - type: mrr_at_5
+      value: 78.61200000000001
+    - type: ndcg_at_1
+      value: 58.5
+    - type: ndcg_at_10
+      value: 44.106
+    - type: ndcg_at_100
+      value: 49.268
+    - type: ndcg_at_1000
+      value: 56.711999999999996
+    - type: ndcg_at_3
+      value: 48.934
+    - type: ndcg_at_5
+      value: 45.826
+    - type: precision_at_1
+      value: 71.0
+    - type: precision_at_10
+      value: 35.0
+    - type: precision_at_100
+      value: 11.360000000000001
+    - type: precision_at_1000
+      value: 2.046
+    - type: precision_at_3
+      value: 52.833
+    - type: precision_at_5
+      value: 44.15
+    - type: recall_at_1
+      value: 9.53
+    - type: recall_at_10
+      value: 26.811
+    - type: recall_at_100
+      value: 55.916999999999994
+    - type: recall_at_1000
+      value: 79.973
+    - type: recall_at_3
+      value: 16.413
+    - type: recall_at_5
+      value: 19.980999999999998
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB EmotionClassification
+      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
+      split: test
+      type: mteb/emotion
+    metrics:
+    - type: accuracy
+      value: 51.519999999999996
+    - type: f1
+      value: 46.36601294761231
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB FEVER
+      revision: None
+      split: test
+      type: fever
+    metrics:
+    - type: map_at_1
+      value: 74.413
+    - type: map_at_10
+      value: 83.414
+    - type: map_at_100
+      value: 83.621
+    - type: map_at_1000
+      value: 83.635
+    - type: map_at_3
+      value: 82.337
+    - type: map_at_5
+      value: 83.039
+    - type: mrr_at_1
+      value: 80.19800000000001
+    - type: mrr_at_10
+      value: 87.715
+    - type: mrr_at_100
+      value: 87.778
+    - type: mrr_at_1000
+      value: 87.779
+    - type: mrr_at_3
+      value: 87.106
+    - type: mrr_at_5
+      value: 87.555
+    - type: ndcg_at_1
+      value: 80.19800000000001
+    - type: ndcg_at_10
+      value: 87.182
+    - type: ndcg_at_100
+      value: 87.90299999999999
+    - type: ndcg_at_1000
+      value: 88.143
+    - type: ndcg_at_3
+      value: 85.60600000000001
+    - type: ndcg_at_5
+      value: 86.541
+    - type: precision_at_1
+      value: 80.19800000000001
+    - type: precision_at_10
+      value: 10.531
+    - type: precision_at_100
+      value: 1.113
+    - type: precision_at_1000
+      value: 0.11499999999999999
+    - type: precision_at_3
+      value: 32.933
+    - type: precision_at_5
+      value: 20.429
+    - type: recall_at_1
+      value: 74.413
+    - type: recall_at_10
+      value: 94.363
+    - type: recall_at_100
+      value: 97.165
+    - type: recall_at_1000
+      value: 98.668
+    - type: recall_at_3
+      value: 90.108
+    - type: recall_at_5
+      value: 92.52
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB FiQA2018
+      revision: None
+      split: test
+      type: fiqa
+    metrics:
+    - type: map_at_1
+      value: 22.701
+    - type: map_at_10
+      value: 37.122
+    - type: map_at_100
+      value: 39.178000000000004
+    - type: map_at_1000
+      value: 39.326
+    - type: map_at_3
+      value: 32.971000000000004
+    - type: map_at_5
+      value: 35.332
+    - type: mrr_at_1
+      value: 44.753
+    - type: mrr_at_10
+      value: 53.452
+    - type: mrr_at_100
+      value: 54.198
+    - type: mrr_at_1000
+      value: 54.225
+    - type: mrr_at_3
+      value: 50.952
+    - type: mrr_at_5
+      value: 52.464
+    - type: ndcg_at_1
+      value: 44.753
+    - type: ndcg_at_10
+      value: 45.021
+    - type: ndcg_at_100
+      value: 52.028
+    - type: ndcg_at_1000
+      value: 54.596000000000004
+    - type: ndcg_at_3
+      value: 41.622
+    - type: ndcg_at_5
+      value: 42.736000000000004
+    - type: precision_at_1
+      value: 44.753
+    - type: precision_at_10
+      value: 12.284
+    - type: precision_at_100
+      value: 1.955
+    - type: precision_at_1000
+      value: 0.243
+    - type: precision_at_3
+      value: 27.828999999999997
+    - type: precision_at_5
+      value: 20.061999999999998
+    - type: recall_at_1
+      value: 22.701
+    - type: recall_at_10
+      value: 51.432
+    - type: recall_at_100
+      value: 77.009
+    - type: recall_at_1000
+      value: 92.511
+    - type: recall_at_3
+      value: 37.919000000000004
+    - type: recall_at_5
+      value: 44.131
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB HotpotQA
+      revision: None
+      split: test
+      type: hotpotqa
+    metrics:
+    - type: map_at_1
+      value: 40.189
+    - type: map_at_10
+      value: 66.24600000000001
+    - type: map_at_100
+      value: 67.098
+    - type: map_at_1000
+      value: 67.149
+    - type: map_at_3
+      value: 62.684
+    - type: map_at_5
+      value: 64.974
+    - type: mrr_at_1
+      value: 80.378
+    - type: mrr_at_10
+      value: 86.127
+    - type: mrr_at_100
+      value: 86.29299999999999
+    - type: mrr_at_1000
+      value: 86.297
+    - type: mrr_at_3
+      value: 85.31400000000001
+    - type: mrr_at_5
+      value: 85.858
+    - type: ndcg_at_1
+      value: 80.378
+    - type: ndcg_at_10
+      value: 74.101
+    - type: ndcg_at_100
+      value: 76.993
+    - type: ndcg_at_1000
+      value: 77.948
+    - type: ndcg_at_3
+      value: 69.232
+    - type: ndcg_at_5
+      value: 72.04599999999999
+    - type: precision_at_1
+      value: 80.378
+    - type: precision_at_10
+      value: 15.595999999999998
+    - type: precision_at_100
+      value: 1.7840000000000003
+    - type: precision_at_1000
+      value: 0.191
+    - type: precision_at_3
+      value: 44.884
+    - type: precision_at_5
+      value: 29.145
+    - type: recall_at_1
+      value: 40.189
+    - type: recall_at_10
+      value: 77.981
+    - type: recall_at_100
+      value: 89.21
+    - type: recall_at_1000
+      value: 95.48299999999999
+    - type: recall_at_3
+      value: 67.326
+    - type: recall_at_5
+      value: 72.863
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB ImdbClassification
+      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
+      split: test
+      type: mteb/imdb
+    metrics:
+    - type: accuracy
+      value: 92.84599999999999
+    - type: ap
+      value: 89.4710787567357
+    - type: f1
+      value: 92.83752676932258
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB MSMARCO
+      revision: None
+      split: dev
+      type: msmarco
+    metrics:
+    - type: map_at_1
+      value: 23.132
+    - type: map_at_10
+      value: 35.543
+    - type: map_at_100
+      value: 36.702
+    - type: map_at_1000
+      value: 36.748999999999995
+    - type: map_at_3
+      value: 31.737
+    - type: map_at_5
+      value: 33.927
+    - type: mrr_at_1
+      value: 23.782
+    - type: mrr_at_10
+      value: 36.204
+    - type: mrr_at_100
+      value: 37.29
+    - type: mrr_at_1000
+      value: 37.330999999999996
+    - type: mrr_at_3
+      value: 32.458999999999996
+    - type: mrr_at_5
+      value: 34.631
+    - type: ndcg_at_1
+      value: 23.782
+    - type: ndcg_at_10
+      value: 42.492999999999995
+    - type: ndcg_at_100
+      value: 47.985
+    - type: ndcg_at_1000
+      value: 49.141
+    - type: ndcg_at_3
+      value: 34.748000000000005
+    - type: ndcg_at_5
+      value: 38.651
+    - type: precision_at_1
+      value: 23.782
+    - type: precision_at_10
+      value: 6.665
+    - type: precision_at_100
+      value: 0.941
+    - type: precision_at_1000
+      value: 0.104
+    - type: precision_at_3
+      value: 14.776
+    - type: precision_at_5
+      value: 10.84
+    - type: recall_at_1
+      value: 23.132
+    - type: recall_at_10
+      value: 63.794
+    - type: recall_at_100
+      value: 89.027
+    - type: recall_at_1000
+      value: 97.807
+    - type: recall_at_3
+      value: 42.765
+    - type: recall_at_5
+      value: 52.11
+    task:
+      type: Retrieval
+  - dataset:
+      config: en
+      name: MTEB MTOPDomainClassification (en)
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+      split: test
+      type: mteb/mtop_domain
+    metrics:
+    - type: accuracy
+      value: 94.59188326493388
+    - type: f1
+      value: 94.3842594786827
+    task:
+      type: Classification
+  - dataset:
+      config: en
+      name: MTEB MTOPIntentClassification (en)
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+      split: test
+      type: mteb/mtop_intent
+    metrics:
+    - type: accuracy
+      value: 79.49384404924761
+    - type: f1
+      value: 59.7580539534629
+    task:
+      type: Classification
+  - dataset:
+      config: en
+      name: MTEB MassiveIntentClassification (en)
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+      split: test
+      type: mteb/amazon_massive_intent
+    metrics:
+    - type: accuracy
+      value: 77.56220578345663
+    - type: f1
+      value: 75.27228165561478
+    task:
+      type: Classification
+  - dataset:
+      config: en
+      name: MTEB MassiveScenarioClassification (en)
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+      split: test
+      type: mteb/amazon_massive_scenario
+    metrics:
+    - type: accuracy
+      value: 80.53463349024884
+    - type: f1
+      value: 80.4893958236536
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB MedrxivClusteringP2P
+      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
+      split: test
+      type: mteb/medrxiv-clustering-p2p
+    metrics:
+    - type: v_measure
+      value: 32.56100273484962
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB MedrxivClusteringS2S
+      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
+      split: test
+      type: mteb/medrxiv-clustering-s2s
+    metrics:
+    - type: v_measure
+      value: 31.470380028839607
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB MindSmallReranking
+      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
+      split: test
+      type: mteb/mind_small
+    metrics:
+    - type: map
+      value: 32.06102792457849
+    - type: mrr
+      value: 33.30709199672238
+    task:
+      type: Reranking
+  - dataset:
+      config: default
+      name: MTEB NFCorpus
+      revision: None
+      split: test
+      type: nfcorpus
+    metrics:
+    - type: map_at_1
+      value: 6.776999999999999
+    - type: map_at_10
+      value: 14.924000000000001
+    - type: map_at_100
+      value: 18.955
+    - type: map_at_1000
+      value: 20.538999999999998
+    - type: map_at_3
+      value: 10.982
+    - type: map_at_5
+      value: 12.679000000000002
+    - type: mrr_at_1
+      value: 47.988
+    - type: mrr_at_10
+      value: 57.232000000000006
+    - type: mrr_at_100
+      value: 57.818999999999996
+    - type: mrr_at_1000
+      value: 57.847
+    - type: mrr_at_3
+      value: 54.901999999999994
+    - type: mrr_at_5
+      value: 56.481
+    - type: ndcg_at_1
+      value: 46.594
+    - type: ndcg_at_10
+      value: 38.129000000000005
+    - type: ndcg_at_100
+      value: 35.54
+    - type: ndcg_at_1000
+      value: 44.172
+    - type: ndcg_at_3
+      value: 43.025999999999996
+    - type: ndcg_at_5
+      value: 41.052
+    - type: precision_at_1
+      value: 47.988
+    - type: precision_at_10
+      value: 28.111000000000004
+    - type: precision_at_100
+      value: 8.929
+    - type: precision_at_1000
+      value: 2.185
+    - type: precision_at_3
+      value: 40.144000000000005
+    - type: precision_at_5
+      value: 35.232
+    - type: recall_at_1
+      value: 6.776999999999999
+    - type: recall_at_10
+      value: 19.289
+    - type: recall_at_100
+      value: 36.359
+    - type: recall_at_1000
+      value: 67.54
+    - type: recall_at_3
+      value: 11.869
+    - type: recall_at_5
+      value: 14.999
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB NQ
+      revision: None
+      split: test
+      type: nq
+    metrics:
+    - type: map_at_1
+      value: 31.108000000000004
+    - type: map_at_10
+      value: 47.126000000000005
+    - type: map_at_100
+      value: 48.171
+    - type: map_at_1000
+      value: 48.199
+    - type: map_at_3
+      value: 42.734
+    - type: map_at_5
+      value: 45.362
+    - type: mrr_at_1
+      value: 34.936
+    - type: mrr_at_10
+      value: 49.571
+    - type: mrr_at_100
+      value: 50.345
+    - type: mrr_at_1000
+      value: 50.363
+    - type: mrr_at_3
+      value: 45.959
+    - type: mrr_at_5
+      value: 48.165
+    - type: ndcg_at_1
+      value: 34.936
+    - type: ndcg_at_10
+      value: 55.028999999999996
+    - type: ndcg_at_100
+      value: 59.244
+    - type: ndcg_at_1000
+      value: 59.861
+    - type: ndcg_at_3
+      value: 46.872
+    - type: ndcg_at_5
+      value: 51.217999999999996
+    - type: precision_at_1
+      value: 34.936
+    - type: precision_at_10
+      value: 9.099
+    - type: precision_at_100
+      value: 1.145
+    - type: precision_at_1000
+      value: 0.12
+    - type: precision_at_3
+      value: 21.456
+    - type: precision_at_5
+      value: 15.411
+    - type: recall_at_1
+      value: 31.108000000000004
+    - type: recall_at_10
+      value: 76.53999999999999
+    - type: recall_at_100
+      value: 94.39
+    - type: recall_at_1000
+      value: 98.947
+    - type: recall_at_3
+      value: 55.572
+    - type: recall_at_5
+      value: 65.525
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB QuoraRetrieval
+      revision: None
+      split: test
+      type: quora
+    metrics:
+    - type: map_at_1
+      value: 71.56400000000001
+    - type: map_at_10
+      value: 85.482
+    - type: map_at_100
+      value: 86.114
+    - type: map_at_1000
+      value: 86.13
+    - type: map_at_3
+      value: 82.607
+    - type: map_at_5
+      value: 84.405
+    - type: mrr_at_1
+      value: 82.42
+    - type: mrr_at_10
+      value: 88.304
+    - type: mrr_at_100
+      value: 88.399
+    - type: mrr_at_1000
+      value: 88.399
+    - type: mrr_at_3
+      value: 87.37
+    - type: mrr_at_5
+      value: 88.024
+    - type: ndcg_at_1
+      value: 82.45
+    - type: ndcg_at_10
+      value: 89.06500000000001
+    - type: ndcg_at_100
+      value: 90.232
+    - type: ndcg_at_1000
+      value: 90.305
+    - type: ndcg_at_3
+      value: 86.375
+    - type: ndcg_at_5
+      value: 87.85300000000001
+    - type: precision_at_1
+      value: 82.45
+    - type: precision_at_10
+      value: 13.486999999999998
+    - type: precision_at_100
+      value: 1.534
+    - type: precision_at_1000
+      value: 0.157
+    - type: precision_at_3
+      value: 37.813
+    - type: precision_at_5
+      value: 24.773999999999997
+    - type: recall_at_1
+      value: 71.56400000000001
+    - type: recall_at_10
+      value: 95.812
+    - type: recall_at_100
+      value: 99.7
+    - type: recall_at_1000
+      value: 99.979
+    - type: recall_at_3
+      value: 87.966
+    - type: recall_at_5
+      value: 92.268
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB RedditClustering
+      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
+      split: test
+      type: mteb/reddit-clustering
+    metrics:
+    - type: v_measure
+      value: 57.241876648614145
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB RedditClusteringP2P
+      revision: 282350215ef01743dc01b456c7f5241fa8937f16
+      split: test
+      type: mteb/reddit-clustering-p2p
+    metrics:
+    - type: v_measure
+      value: 64.66212576446223
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB SCIDOCS
+      revision: None
+      split: test
+      type: scidocs
+    metrics:
+    - type: map_at_1
+      value: 5.308
+    - type: map_at_10
+      value: 13.803
+    - type: map_at_100
+      value: 16.176
+    - type: map_at_1000
+      value: 16.561
+    - type: map_at_3
+      value: 9.761000000000001
+    - type: map_at_5
+      value: 11.802
+    - type: mrr_at_1
+      value: 26.200000000000003
+    - type: mrr_at_10
+      value: 37.621
+    - type: mrr_at_100
+      value: 38.767
+    - type: mrr_at_1000
+      value: 38.815
+    - type: mrr_at_3
+      value: 34.117
+    - type: mrr_at_5
+      value: 36.107
+    - type: ndcg_at_1
+      value: 26.200000000000003
+    - type: ndcg_at_10
+      value: 22.64
+    - type: ndcg_at_100
+      value: 31.567
+    - type: ndcg_at_1000
+      value: 37.623
+    - type: ndcg_at_3
+      value: 21.435000000000002
+    - type: ndcg_at_5
+      value: 18.87
+    - type: precision_at_1
+      value: 26.200000000000003
+    - type: precision_at_10
+      value: 11.74
+    - type: precision_at_100
+      value: 2.465
+    - type: precision_at_1000
+      value: 0.391
+    - type: precision_at_3
+      value: 20.033
+    - type: precision_at_5
+      value: 16.64
+    - type: recall_at_1
+      value: 5.308
+    - type: recall_at_10
+      value: 23.794999999999998
+    - type: recall_at_100
+      value: 50.015
+    - type: recall_at_1000
+      value: 79.283
+    - type: recall_at_3
+      value: 12.178
+    - type: recall_at_5
+      value: 16.882
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB SICK-R
+      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
+      split: test
+      type: mteb/sickr-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 84.93231134675553
+    - type: cos_sim_spearman
+      value: 81.68319292603205
+    - type: euclidean_pearson
+      value: 81.8396814380367
+    - type: euclidean_spearman
+      value: 81.24641903349945
+    - type: manhattan_pearson
+      value: 81.84698799204274
+    - type: manhattan_spearman
+      value: 81.24269997904105
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STS12
+      revision: a0d554a64d88156834ff5ae9920b964011b16384
+      split: test
+      type: mteb/sts12-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 86.73241671587446
+    - type: cos_sim_spearman
+      value: 79.05091082971826
+    - type: euclidean_pearson
+      value: 83.91146869578044
+    - type: euclidean_spearman
+      value: 79.87978465370936
+    - type: manhattan_pearson
+      value: 83.90888338917678
+    - type: manhattan_spearman
+      value: 79.87482848584241
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STS13
+      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
+      split: test
+      type: mteb/sts13-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 85.14970731146177
+    - type: cos_sim_spearman
+      value: 86.37363490084627
+    - type: euclidean_pearson
+      value: 83.02154218530433
+    - type: euclidean_spearman
+      value: 83.80258761957367
+    - type: manhattan_pearson
+      value: 83.01664495119347
+    - type: manhattan_spearman
+      value: 83.77567458007952
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STS14
+      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
+      split: test
+      type: mteb/sts14-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 83.40474139886784
+    - type: cos_sim_spearman
+      value: 82.77768789165984
+    - type: euclidean_pearson
+      value: 80.7065877443695
+    - type: euclidean_spearman
+      value: 81.375940662505
+    - type: manhattan_pearson
+      value: 80.6507552270278
+    - type: manhattan_spearman
+      value: 81.32782179098741
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STS15
+      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
+      split: test
+      type: mteb/sts15-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 87.08585968722274
+    - type: cos_sim_spearman
+      value: 88.03110031451399
+    - type: euclidean_pearson
+      value: 85.74012019602384
+    - type: euclidean_spearman
+      value: 86.13592849438209
+    - type: manhattan_pearson
+      value: 85.74404842369206
+    - type: manhattan_spearman
+      value: 86.14492318960154
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STS16
+      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
+      split: test
+      type: mteb/sts16-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 84.95069052788875
+    - type: cos_sim_spearman
+      value: 86.4867991595147
+    - type: euclidean_pearson
+      value: 84.31013325754635
+    - type: euclidean_spearman
+      value: 85.01529258006482
+    - type: manhattan_pearson
+      value: 84.26995570085374
+    - type: manhattan_spearman
+      value: 84.96982104986162
+    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: 87.54617647971897
+    - type: cos_sim_spearman
+      value: 87.49834181751034
+    - type: euclidean_pearson
+      value: 86.01015322577122
+    - type: euclidean_spearman
+      value: 84.63362652063199
+    - type: manhattan_pearson
+      value: 86.13807574475706
+    - type: manhattan_spearman
+      value: 84.7772370721132
+    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: 67.20047755786615
+    - type: cos_sim_spearman
+      value: 67.05324077987636
+    - type: euclidean_pearson
+      value: 66.91930642976601
+    - type: euclidean_spearman
+      value: 65.21491856099105
+    - type: manhattan_pearson
+      value: 66.78756851976624
+    - type: manhattan_spearman
+      value: 65.12356257740728
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STSBenchmark
+      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
+      split: test
+      type: mteb/stsbenchmark-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 86.19852871539686
+    - type: cos_sim_spearman
+      value: 87.5161895296395
+    - type: euclidean_pearson
+      value: 84.59848645207485
+    - type: euclidean_spearman
+      value: 85.26427328757919
+    - type: manhattan_pearson
+      value: 84.59747366996524
+    - type: manhattan_spearman
+      value: 85.24045855146915
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB SciDocsRR
+      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
+      split: test
+      type: mteb/scidocs-reranking
+    metrics:
+    - type: map
+      value: 87.63320317811032
+    - type: mrr
+      value: 96.26242947321379
+    task:
+      type: Reranking
+  - dataset:
+      config: default
+      name: MTEB SciFact
+      revision: None
+      split: test
+      type: scifact
+    metrics:
+    - type: map_at_1
+      value: 60.928000000000004
+    - type: map_at_10
+      value: 70.112
+    - type: map_at_100
+      value: 70.59299999999999
+    - type: map_at_1000
+      value: 70.623
+    - type: map_at_3
+      value: 66.846
+    - type: map_at_5
+      value: 68.447
+    - type: mrr_at_1
+      value: 64.0
+    - type: mrr_at_10
+      value: 71.212
+    - type: mrr_at_100
+      value: 71.616
+    - type: mrr_at_1000
+      value: 71.64500000000001
+    - type: mrr_at_3
+      value: 68.77799999999999
+    - type: mrr_at_5
+      value: 70.094
+    - type: ndcg_at_1
+      value: 64.0
+    - type: ndcg_at_10
+      value: 74.607
+    - type: ndcg_at_100
+      value: 76.416
+    - type: ndcg_at_1000
+      value: 77.102
+    - type: ndcg_at_3
+      value: 69.126
+    - type: ndcg_at_5
+      value: 71.41300000000001
+    - type: precision_at_1
+      value: 64.0
+    - type: precision_at_10
+      value: 9.933
+    - type: precision_at_100
+      value: 1.077
+    - type: precision_at_1000
+      value: 0.11299999999999999
+    - type: precision_at_3
+      value: 26.556
+    - type: precision_at_5
+      value: 17.467
+    - type: recall_at_1
+      value: 60.928000000000004
+    - type: recall_at_10
+      value: 87.322
+    - type: recall_at_100
+      value: 94.833
+    - type: recall_at_1000
+      value: 100.0
+    - type: recall_at_3
+      value: 72.628
+    - type: recall_at_5
+      value: 78.428
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB SprintDuplicateQuestions
+      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
+      split: test
+      type: mteb/sprintduplicatequestions-pairclassification
+    metrics:
+    - type: cos_sim_accuracy
+      value: 99.86237623762376
+    - type: cos_sim_ap
+      value: 96.72586477206649
+    - type: cos_sim_f1
+      value: 93.01858362631845
+    - type: cos_sim_precision
+      value: 93.4409687184662
+    - type: cos_sim_recall
+      value: 92.60000000000001
+    - type: dot_accuracy
+      value: 99.78019801980199
+    - type: dot_ap
+      value: 93.72748205246228
+    - type: dot_f1
+      value: 89.04109589041096
+    - type: dot_precision
+      value: 87.16475095785441
+    - type: dot_recall
+      value: 91.0
+    - type: euclidean_accuracy
+      value: 99.85445544554456
+    - type: euclidean_ap
+      value: 96.6661459876145
+    - type: euclidean_f1
+      value: 92.58337481333997
+    - type: euclidean_precision
+      value: 92.17046580773042
+    - type: euclidean_recall
+      value: 93.0
+    - type: manhattan_accuracy
+      value: 99.85445544554456
+    - type: manhattan_ap
+      value: 96.6883549244056
+    - type: manhattan_f1
+      value: 92.57598405580468
+    - type: manhattan_precision
+      value: 92.25422045680239
+    - type: manhattan_recall
+      value: 92.9
+    - type: max_accuracy
+      value: 99.86237623762376
+    - type: max_ap
+      value: 96.72586477206649
+    - type: max_f1
+      value: 93.01858362631845
+    task:
+      type: PairClassification
+  - dataset:
+      config: default
+      name: MTEB StackExchangeClustering
+      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
+      split: test
+      type: mteb/stackexchange-clustering
+    metrics:
+    - type: v_measure
+      value: 66.39930057069995
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB StackExchangeClusteringP2P
+      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
+      split: test
+      type: mteb/stackexchange-clustering-p2p
+    metrics:
+    - type: v_measure
+      value: 34.96398659903402
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB StackOverflowDupQuestions
+      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
+      split: test
+      type: mteb/stackoverflowdupquestions-reranking
+    metrics:
+    - type: map
+      value: 55.946944700355395
+    - type: mrr
+      value: 56.97151398438164
+    task:
+      type: Reranking
+  - dataset:
+      config: default
+      name: MTEB SummEval
+      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
+      split: test
+      type: mteb/summeval
+    metrics:
+    - type: cos_sim_pearson
+      value: 31.541657650692905
+    - type: cos_sim_spearman
+      value: 31.605804192286303
+    - type: dot_pearson
+      value: 28.26905996736398
+    - type: dot_spearman
+      value: 27.864801765851187
+    task:
+      type: Summarization
+  - dataset:
+      config: default
+      name: MTEB TRECCOVID
+      revision: None
+      split: test
+      type: trec-covid
+    metrics:
+    - type: map_at_1
+      value: 0.22599999999999998
+    - type: map_at_10
+      value: 1.8870000000000002
+    - type: map_at_100
+      value: 9.78
+    - type: map_at_1000
+      value: 22.514
+    - type: map_at_3
+      value: 0.6669999999999999
+    - type: map_at_5
+      value: 1.077
+    - type: mrr_at_1
+      value: 82.0
+    - type: mrr_at_10
+      value: 89.86699999999999
+    - type: mrr_at_100
+      value: 89.86699999999999
+    - type: mrr_at_1000
+      value: 89.86699999999999
+    - type: mrr_at_3
+      value: 89.667
+    - type: mrr_at_5
+      value: 89.667
+    - type: ndcg_at_1
+      value: 79.0
+    - type: ndcg_at_10
+      value: 74.818
+    - type: ndcg_at_100
+      value: 53.715999999999994
+    - type: ndcg_at_1000
+      value: 47.082
+    - type: ndcg_at_3
+      value: 82.134
+    - type: ndcg_at_5
+      value: 79.81899999999999
+    - type: precision_at_1
+      value: 82.0
+    - type: precision_at_10
+      value: 78.0
+    - type: precision_at_100
+      value: 54.48
+    - type: precision_at_1000
+      value: 20.518
+    - type: precision_at_3
+      value: 87.333
+    - type: precision_at_5
+      value: 85.2
+    - type: recall_at_1
+      value: 0.22599999999999998
+    - type: recall_at_10
+      value: 2.072
+    - type: recall_at_100
+      value: 13.013
+    - type: recall_at_1000
+      value: 43.462
+    - type: recall_at_3
+      value: 0.695
+    - type: recall_at_5
+      value: 1.139
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB Touche2020
+      revision: None
+      split: test
+      type: webis-touche2020
+    metrics:
+    - type: map_at_1
+      value: 2.328
+    - type: map_at_10
+      value: 9.795
+    - type: map_at_100
+      value: 15.801000000000002
+    - type: map_at_1000
+      value: 17.23
+    - type: map_at_3
+      value: 4.734
+    - type: map_at_5
+      value: 6.644
+    - type: mrr_at_1
+      value: 30.612000000000002
+    - type: mrr_at_10
+      value: 46.902
+    - type: mrr_at_100
+      value: 47.495
+    - type: mrr_at_1000
+      value: 47.495
+    - type: mrr_at_3
+      value: 41.156
+    - type: mrr_at_5
+      value: 44.218
+    - type: ndcg_at_1
+      value: 28.571
+    - type: ndcg_at_10
+      value: 24.806
+    - type: ndcg_at_100
+      value: 36.419000000000004
+    - type: ndcg_at_1000
+      value: 47.272999999999996
+    - type: ndcg_at_3
+      value: 25.666
+    - type: ndcg_at_5
+      value: 25.448999999999998
+    - type: precision_at_1
+      value: 30.612000000000002
+    - type: precision_at_10
+      value: 23.061
+    - type: precision_at_100
+      value: 7.714
+    - type: precision_at_1000
+      value: 1.484
+    - type: precision_at_3
+      value: 26.531
+    - type: precision_at_5
+      value: 26.122
+    - type: recall_at_1
+      value: 2.328
+    - type: recall_at_10
+      value: 16.524
+    - type: recall_at_100
+      value: 47.179
+    - type: recall_at_1000
+      value: 81.22200000000001
+    - type: recall_at_3
+      value: 5.745
+    - type: recall_at_5
+      value: 9.339
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB ToxicConversationsClassification
+      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
+      split: test
+      type: mteb/toxic_conversations_50k
+    metrics:
+    - type: accuracy
+      value: 70.9142
+    - type: ap
+      value: 14.335574772555415
+    - type: f1
+      value: 54.62839595194111
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB TweetSentimentExtractionClassification
+      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
+      split: test
+      type: mteb/tweet_sentiment_extraction
+    metrics:
+    - type: accuracy
+      value: 59.94340690435768
+    - type: f1
+      value: 60.286487936731916
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB TwentyNewsgroupsClustering
+      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
+      split: test
+      type: mteb/twentynewsgroups-clustering
+    metrics:
+    - type: v_measure
+      value: 51.26597708987974
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB TwitterSemEval2015
+      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
+      split: test
+      type: mteb/twittersemeval2015-pairclassification
+    metrics:
+    - type: cos_sim_accuracy
+      value: 87.48882398521786
+    - type: cos_sim_ap
+      value: 79.04326607602204
+    - type: cos_sim_f1
+      value: 71.64566826860633
+    - type: cos_sim_precision
+      value: 70.55512918905092
+    - type: cos_sim_recall
+      value: 72.77044854881267
+    - type: dot_accuracy
+      value: 84.19264469213805
+    - type: dot_ap
+      value: 67.96360043562528
+    - type: dot_f1
+      value: 64.06418393006827
+    - type: dot_precision
+      value: 58.64941898706424
+    - type: dot_recall
+      value: 70.58047493403694
+    - type: euclidean_accuracy
+      value: 87.45902127913214
+    - type: euclidean_ap
+      value: 78.9742237648272
+    - type: euclidean_f1
+      value: 71.5553235908142
+    - type: euclidean_precision
+      value: 70.77955601445535
+    - type: euclidean_recall
+      value: 72.34828496042216
+    - type: manhattan_accuracy
+      value: 87.41729749061214
+    - type: manhattan_ap
+      value: 78.90073137580596
+    - type: manhattan_f1
+      value: 71.3942611553533
+    - type: manhattan_precision
+      value: 68.52705653967483
+    - type: manhattan_recall
+      value: 74.51187335092348
+    - type: max_accuracy
+      value: 87.48882398521786
+    - type: max_ap
+      value: 79.04326607602204
+    - type: max_f1
+      value: 71.64566826860633
+    task:
+      type: PairClassification
+  - dataset:
+      config: default
+      name: MTEB TwitterURLCorpus
+      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
+      split: test
+      type: mteb/twitterurlcorpus-pairclassification
+    metrics:
+    - type: cos_sim_accuracy
+      value: 88.68125897465751
+    - type: cos_sim_ap
+      value: 85.6003454431979
+    - type: cos_sim_f1
+      value: 77.6957163958641
+    - type: cos_sim_precision
+      value: 73.0110366307807
+    - type: cos_sim_recall
+      value: 83.02279026793964
+    - type: dot_accuracy
+      value: 87.7672992587418
+    - type: dot_ap
+      value: 82.4971301112899
+    - type: dot_f1
+      value: 75.90528233151184
+    - type: dot_precision
+      value: 72.0370626469368
+    - type: dot_recall
+      value: 80.21250384970742
+    - type: euclidean_accuracy
+      value: 88.4503434625684
+    - type: euclidean_ap
+      value: 84.91949884748384
+    - type: euclidean_f1
+      value: 76.92365018444684
+    - type: euclidean_precision
+      value: 74.53245721712759
+    - type: euclidean_recall
+      value: 79.47336002463813
+    - type: manhattan_accuracy
+      value: 88.47556952691427
+    - type: manhattan_ap
+      value: 84.8963689101517
+    - type: manhattan_f1
+      value: 76.85901249256395
+    - type: manhattan_precision
+      value: 74.31693989071039
+    - type: manhattan_recall
+      value: 79.58115183246073
+    - type: max_accuracy
+      value: 88.68125897465751
+    - type: max_ap
+      value: 85.6003454431979
+    - type: max_f1
+      value: 77.6957163958641
+    task:
+      type: PairClassification
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- transformers
+- mteb
+- onnx
+- teradata
+---
+# A Teradata Vantage compatible Embeddings Model
+# BAAI/bge-large-en-v1.5
+## Overview of this Model
+An Embedding Model which maps text (sentence/ paragraphs) into a vector.  The [BAAI/bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-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.
+- 335.14M params (Sizes in ONNX format - "fp32": 1275.11MB, "int8": 320.63MB, "uint8": 320.63MB)
+- 512 maximum input tokens
+- 1024 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-large-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-large-en-v1.5"
+number_dimensions_output = 1024
+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-large-en-v1.5](https://huggingface.co/BAAI/bge-large-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-large-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-large-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": 1024,
+  "id2label": {
+    "0": "LABEL_0"
+  },
+  "initializer_range": 0.02,
+  "intermediate_size": 4096,
+  "label2id": {
+    "LABEL_0": 0
+  },
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 512,
+  "model_type": "bert",
+  "num_attention_heads": 16,
+  "num_hidden_layers": 24,
+  "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-large-en-v1.5",
+    "number_of_generated_embeddings": 1024,
+    "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 sha256:21b0701524af665e4d0b56087af48ad6e9974d6f2eb7da9a90e9c4729eee13aa
+size 1337046449

onnx/model_int8.onnx ADDED Viewed

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

+version https://git-lfs.github.com/spec/v1
+oid sha256:b3ed71cba4129cf775616f18528c48868f89d2aa54413a848d8d1ab9fa037e4d
+size 336200444

onnx/model_uint8.onnx ADDED Viewed

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

+version https://git-lfs.github.com/spec/v1
+oid sha256:1138b1bd289f2299d4a727a1e1343ce601ecd6bb137d71d30bc4c07883e5d181
+size 336200509

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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