model

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README.md +2656 -3
config.json +38 -0
conversion_config.json +12 -0
convert.py +198 -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 +1,2656 @@
----
-license: apache-2.0
----

+---
+datasets:
+- jinaai/negation-dataset
+inference: false
+language: en
+license: apache-2.0
+model-index:
+- name: jina-embedding-s-en-v2
+  results:
+  - dataset:
+      config: en
+      name: MTEB AmazonCounterfactualClassification (en)
+      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
+      split: test
+      type: mteb/amazon_counterfactual
+    metrics:
+    - type: accuracy
+      value: 71.35820895522387
+    - type: ap
+      value: 33.99931933598115
+    - type: f1
+      value: 65.3853685535555
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB AmazonPolarityClassification
+      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
+      split: test
+      type: mteb/amazon_polarity
+    metrics:
+    - type: accuracy
+      value: 82.90140000000001
+    - type: ap
+      value: 78.01434597815617
+    - type: f1
+      value: 82.83357802722676
+    task:
+      type: Classification
+  - dataset:
+      config: en
+      name: MTEB AmazonReviewsClassification (en)
+      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
+      split: test
+      type: mteb/amazon_reviews_multi
+    metrics:
+    - type: accuracy
+      value: 40.88999999999999
+    - type: f1
+      value: 39.209432767163456
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB ArguAna
+      revision: None
+      split: test
+      type: arguana
+    metrics:
+    - type: map_at_1
+      value: 23.257
+    - type: map_at_10
+      value: 37.946000000000005
+    - type: map_at_100
+      value: 39.17
+    - type: map_at_1000
+      value: 39.181
+    - type: map_at_3
+      value: 32.99
+    - type: map_at_5
+      value: 35.467999999999996
+    - type: mrr_at_1
+      value: 23.541999999999998
+    - type: mrr_at_10
+      value: 38.057
+    - type: mrr_at_100
+      value: 39.289
+    - type: mrr_at_1000
+      value: 39.299
+    - type: mrr_at_3
+      value: 33.096
+    - type: mrr_at_5
+      value: 35.628
+    - type: ndcg_at_1
+      value: 23.257
+    - type: ndcg_at_10
+      value: 46.729
+    - type: ndcg_at_100
+      value: 51.900999999999996
+    - type: ndcg_at_1000
+      value: 52.16
+    - type: ndcg_at_3
+      value: 36.323
+    - type: ndcg_at_5
+      value: 40.766999999999996
+    - type: precision_at_1
+      value: 23.257
+    - type: precision_at_10
+      value: 7.510999999999999
+    - type: precision_at_100
+      value: 0.976
+    - type: precision_at_1000
+      value: 0.1
+    - type: precision_at_3
+      value: 15.339
+    - type: precision_at_5
+      value: 11.350999999999999
+    - type: recall_at_1
+      value: 23.257
+    - type: recall_at_10
+      value: 75.107
+    - type: recall_at_100
+      value: 97.58200000000001
+    - type: recall_at_1000
+      value: 99.57300000000001
+    - type: recall_at_3
+      value: 46.017
+    - type: recall_at_5
+      value: 56.757000000000005
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB ArxivClusteringP2P
+      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
+      split: test
+      type: mteb/arxiv-clustering-p2p
+    metrics:
+    - type: v_measure
+      value: 44.02420878391967
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB ArxivClusteringS2S
+      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
+      split: test
+      type: mteb/arxiv-clustering-s2s
+    metrics:
+    - type: v_measure
+      value: 35.16136856000258
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB AskUbuntuDupQuestions
+      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
+      split: test
+      type: mteb/askubuntudupquestions-reranking
+    metrics:
+    - type: map
+      value: 59.61809790513646
+    - type: mrr
+      value: 73.07215406938397
+    task:
+      type: Reranking
+  - dataset:
+      config: default
+      name: MTEB BIOSSES
+      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
+      split: test
+      type: mteb/biosses-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 82.0167350090749
+    - type: cos_sim_spearman
+      value: 80.51569002630401
+    - type: euclidean_pearson
+      value: 81.46820525099726
+    - type: euclidean_spearman
+      value: 80.51569002630401
+    - type: manhattan_pearson
+      value: 81.35596555056757
+    - type: manhattan_spearman
+      value: 80.12592210903303
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB Banking77Classification
+      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
+      split: test
+      type: mteb/banking77
+    metrics:
+    - type: accuracy
+      value: 78.25
+    - type: f1
+      value: 77.34950913540605
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB BiorxivClusteringP2P
+      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
+      split: test
+      type: mteb/biorxiv-clustering-p2p
+    metrics:
+    - type: v_measure
+      value: 35.57238596005698
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB BiorxivClusteringS2S
+      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
+      split: test
+      type: mteb/biorxiv-clustering-s2s
+    metrics:
+    - type: v_measure
+      value: 29.066444306196683
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB CQADupstackAndroidRetrieval
+      revision: None
+      split: test
+      type: BeIR/cqadupstack
+    metrics:
+    - type: map_at_1
+      value: 31.891000000000002
+    - type: map_at_10
+      value: 42.772
+    - type: map_at_100
+      value: 44.108999999999995
+    - type: map_at_1000
+      value: 44.236
+    - type: map_at_3
+      value: 39.289
+    - type: map_at_5
+      value: 41.113
+    - type: mrr_at_1
+      value: 39.342
+    - type: mrr_at_10
+      value: 48.852000000000004
+    - type: mrr_at_100
+      value: 49.534
+    - type: mrr_at_1000
+      value: 49.582
+    - type: mrr_at_3
+      value: 46.089999999999996
+    - type: mrr_at_5
+      value: 47.685
+    - type: ndcg_at_1
+      value: 39.342
+    - type: ndcg_at_10
+      value: 48.988
+    - type: ndcg_at_100
+      value: 53.854
+    - type: ndcg_at_1000
+      value: 55.955
+    - type: ndcg_at_3
+      value: 43.877
+    - type: ndcg_at_5
+      value: 46.027
+    - type: precision_at_1
+      value: 39.342
+    - type: precision_at_10
+      value: 9.285
+    - type: precision_at_100
+      value: 1.488
+    - type: precision_at_1000
+      value: 0.194
+    - type: precision_at_3
+      value: 20.696
+    - type: precision_at_5
+      value: 14.878
+    - type: recall_at_1
+      value: 31.891000000000002
+    - type: recall_at_10
+      value: 60.608
+    - type: recall_at_100
+      value: 81.025
+    - type: recall_at_1000
+      value: 94.883
+    - type: recall_at_3
+      value: 45.694
+    - type: recall_at_5
+      value: 51.684
+    - type: map_at_1
+      value: 28.778
+    - type: map_at_10
+      value: 37.632
+    - type: map_at_100
+      value: 38.800000000000004
+    - type: map_at_1000
+      value: 38.934999999999995
+    - type: map_at_3
+      value: 35.293
+    - type: map_at_5
+      value: 36.547000000000004
+    - type: mrr_at_1
+      value: 35.35
+    - type: mrr_at_10
+      value: 42.936
+    - type: mrr_at_100
+      value: 43.69
+    - type: mrr_at_1000
+      value: 43.739
+    - type: mrr_at_3
+      value: 41.062
+    - type: mrr_at_5
+      value: 42.097
+    - type: ndcg_at_1
+      value: 35.35
+    - type: ndcg_at_10
+      value: 42.528
+    - type: ndcg_at_100
+      value: 46.983000000000004
+    - type: ndcg_at_1000
+      value: 49.187999999999995
+    - type: ndcg_at_3
+      value: 39.271
+    - type: ndcg_at_5
+      value: 40.654
+    - type: precision_at_1
+      value: 35.35
+    - type: precision_at_10
+      value: 7.828
+    - type: precision_at_100
+      value: 1.3010000000000002
+    - type: precision_at_1000
+      value: 0.17700000000000002
+    - type: precision_at_3
+      value: 18.96
+    - type: precision_at_5
+      value: 13.120999999999999
+    - type: recall_at_1
+      value: 28.778
+    - type: recall_at_10
+      value: 50.775000000000006
+    - type: recall_at_100
+      value: 69.66799999999999
+    - type: recall_at_1000
+      value: 83.638
+    - type: recall_at_3
+      value: 40.757
+    - type: recall_at_5
+      value: 44.86
+    - type: map_at_1
+      value: 37.584
+    - type: map_at_10
+      value: 49.69
+    - type: map_at_100
+      value: 50.639
+    - type: map_at_1000
+      value: 50.702999999999996
+    - type: map_at_3
+      value: 46.61
+    - type: map_at_5
+      value: 48.486000000000004
+    - type: mrr_at_1
+      value: 43.009
+    - type: mrr_at_10
+      value: 52.949999999999996
+    - type: mrr_at_100
+      value: 53.618
+    - type: mrr_at_1000
+      value: 53.65299999999999
+    - type: mrr_at_3
+      value: 50.605999999999995
+    - type: mrr_at_5
+      value: 52.095
+    - type: ndcg_at_1
+      value: 43.009
+    - type: ndcg_at_10
+      value: 55.278000000000006
+    - type: ndcg_at_100
+      value: 59.134
+    - type: ndcg_at_1000
+      value: 60.528999999999996
+    - type: ndcg_at_3
+      value: 50.184
+    - type: ndcg_at_5
+      value: 52.919000000000004
+    - type: precision_at_1
+      value: 43.009
+    - type: precision_at_10
+      value: 8.821
+    - type: precision_at_100
+      value: 1.161
+    - type: precision_at_1000
+      value: 0.133
+    - type: precision_at_3
+      value: 22.424
+    - type: precision_at_5
+      value: 15.436
+    - type: recall_at_1
+      value: 37.584
+    - type: recall_at_10
+      value: 68.514
+    - type: recall_at_100
+      value: 85.099
+    - type: recall_at_1000
+      value: 95.123
+    - type: recall_at_3
+      value: 55.007
+    - type: recall_at_5
+      value: 61.714999999999996
+    - type: map_at_1
+      value: 24.7
+    - type: map_at_10
+      value: 32.804
+    - type: map_at_100
+      value: 33.738
+    - type: map_at_1000
+      value: 33.825
+    - type: map_at_3
+      value: 30.639
+    - type: map_at_5
+      value: 31.781
+    - type: mrr_at_1
+      value: 26.328000000000003
+    - type: mrr_at_10
+      value: 34.679
+    - type: mrr_at_100
+      value: 35.510000000000005
+    - type: mrr_at_1000
+      value: 35.577999999999996
+    - type: mrr_at_3
+      value: 32.58
+    - type: mrr_at_5
+      value: 33.687
+    - type: ndcg_at_1
+      value: 26.328000000000003
+    - type: ndcg_at_10
+      value: 37.313
+    - type: ndcg_at_100
+      value: 42.004000000000005
+    - type: ndcg_at_1000
+      value: 44.232
+    - type: ndcg_at_3
+      value: 33.076
+    - type: ndcg_at_5
+      value: 34.966
+    - type: precision_at_1
+      value: 26.328000000000003
+    - type: precision_at_10
+      value: 5.627
+    - type: precision_at_100
+      value: 0.8410000000000001
+    - type: precision_at_1000
+      value: 0.106
+    - type: precision_at_3
+      value: 14.011000000000001
+    - type: precision_at_5
+      value: 9.582
+    - type: recall_at_1
+      value: 24.7
+    - type: recall_at_10
+      value: 49.324
+    - type: recall_at_100
+      value: 71.018
+    - type: recall_at_1000
+      value: 87.905
+    - type: recall_at_3
+      value: 37.7
+    - type: recall_at_5
+      value: 42.281
+    - type: map_at_1
+      value: 14.350999999999999
+    - type: map_at_10
+      value: 21.745
+    - type: map_at_100
+      value: 22.731
+    - type: map_at_1000
+      value: 22.852
+    - type: map_at_3
+      value: 19.245
+    - type: map_at_5
+      value: 20.788
+    - type: mrr_at_1
+      value: 18.159
+    - type: mrr_at_10
+      value: 25.833000000000002
+    - type: mrr_at_100
+      value: 26.728
+    - type: mrr_at_1000
+      value: 26.802
+    - type: mrr_at_3
+      value: 23.383000000000003
+    - type: mrr_at_5
+      value: 24.887999999999998
+    - type: ndcg_at_1
+      value: 18.159
+    - type: ndcg_at_10
+      value: 26.518000000000004
+    - type: ndcg_at_100
+      value: 31.473000000000003
+    - type: ndcg_at_1000
+      value: 34.576
+    - type: ndcg_at_3
+      value: 21.907
+    - type: ndcg_at_5
+      value: 24.39
+    - type: precision_at_1
+      value: 18.159
+    - type: precision_at_10
+      value: 4.938
+    - type: precision_at_100
+      value: 0.853
+    - type: precision_at_1000
+      value: 0.125
+    - type: precision_at_3
+      value: 10.655000000000001
+    - type: precision_at_5
+      value: 7.985
+    - type: recall_at_1
+      value: 14.350999999999999
+    - type: recall_at_10
+      value: 37.284
+    - type: recall_at_100
+      value: 59.11300000000001
+    - type: recall_at_1000
+      value: 81.634
+    - type: recall_at_3
+      value: 24.753
+    - type: recall_at_5
+      value: 30.979
+    - type: map_at_1
+      value: 26.978
+    - type: map_at_10
+      value: 36.276
+    - type: map_at_100
+      value: 37.547000000000004
+    - type: map_at_1000
+      value: 37.678
+    - type: map_at_3
+      value: 33.674
+    - type: map_at_5
+      value: 35.119
+    - type: mrr_at_1
+      value: 32.916000000000004
+    - type: mrr_at_10
+      value: 41.798
+    - type: mrr_at_100
+      value: 42.72
+    - type: mrr_at_1000
+      value: 42.778
+    - type: mrr_at_3
+      value: 39.493
+    - type: mrr_at_5
+      value: 40.927
+    - type: ndcg_at_1
+      value: 32.916000000000004
+    - type: ndcg_at_10
+      value: 41.81
+    - type: ndcg_at_100
+      value: 47.284
+    - type: ndcg_at_1000
+      value: 49.702
+    - type: ndcg_at_3
+      value: 37.486999999999995
+    - type: ndcg_at_5
+      value: 39.597
+    - type: precision_at_1
+      value: 32.916000000000004
+    - type: precision_at_10
+      value: 7.411
+    - type: precision_at_100
+      value: 1.189
+    - type: precision_at_1000
+      value: 0.158
+    - type: precision_at_3
+      value: 17.581
+    - type: precision_at_5
+      value: 12.397
+    - type: recall_at_1
+      value: 26.978
+    - type: recall_at_10
+      value: 52.869
+    - type: recall_at_100
+      value: 75.78399999999999
+    - type: recall_at_1000
+      value: 91.545
+    - type: recall_at_3
+      value: 40.717
+    - type: recall_at_5
+      value: 46.168
+    - type: map_at_1
+      value: 24.641
+    - type: map_at_10
+      value: 32.916000000000004
+    - type: map_at_100
+      value: 34.165
+    - type: map_at_1000
+      value: 34.286
+    - type: map_at_3
+      value: 30.335
+    - type: map_at_5
+      value: 31.569000000000003
+    - type: mrr_at_1
+      value: 30.593999999999998
+    - type: mrr_at_10
+      value: 38.448
+    - type: mrr_at_100
+      value: 39.299
+    - type: mrr_at_1000
+      value: 39.362
+    - type: mrr_at_3
+      value: 36.244
+    - type: mrr_at_5
+      value: 37.232
+    - type: ndcg_at_1
+      value: 30.593999999999998
+    - type: ndcg_at_10
+      value: 38.2
+    - type: ndcg_at_100
+      value: 43.742
+    - type: ndcg_at_1000
+      value: 46.217000000000006
+    - type: ndcg_at_3
+      value: 33.925
+    - type: ndcg_at_5
+      value: 35.394
+    - type: precision_at_1
+      value: 30.593999999999998
+    - type: precision_at_10
+      value: 6.895
+    - type: precision_at_100
+      value: 1.1320000000000001
+    - type: precision_at_1000
+      value: 0.153
+    - type: precision_at_3
+      value: 16.096
+    - type: precision_at_5
+      value: 11.05
+    - type: recall_at_1
+      value: 24.641
+    - type: recall_at_10
+      value: 48.588
+    - type: recall_at_100
+      value: 72.841
+    - type: recall_at_1000
+      value: 89.535
+    - type: recall_at_3
+      value: 36.087
+    - type: recall_at_5
+      value: 40.346
+    - type: map_at_1
+      value: 24.79425
+    - type: map_at_10
+      value: 33.12033333333333
+    - type: map_at_100
+      value: 34.221333333333334
+    - type: map_at_1000
+      value: 34.3435
+    - type: map_at_3
+      value: 30.636583333333338
+    - type: map_at_5
+      value: 31.974083333333326
+    - type: mrr_at_1
+      value: 29.242416666666664
+    - type: mrr_at_10
+      value: 37.11675
+    - type: mrr_at_100
+      value: 37.93783333333334
+    - type: mrr_at_1000
+      value: 38.003083333333336
+    - type: mrr_at_3
+      value: 34.904666666666664
+    - type: mrr_at_5
+      value: 36.12916666666667
+    - type: ndcg_at_1
+      value: 29.242416666666664
+    - type: ndcg_at_10
+      value: 38.03416666666667
+    - type: ndcg_at_100
+      value: 42.86674999999999
+    - type: ndcg_at_1000
+      value: 45.34550000000001
+    - type: ndcg_at_3
+      value: 33.76466666666666
+    - type: ndcg_at_5
+      value: 35.668666666666674
+    - type: precision_at_1
+      value: 29.242416666666664
+    - type: precision_at_10
+      value: 6.589833333333334
+    - type: precision_at_100
+      value: 1.0693333333333332
+    - type: precision_at_1000
+      value: 0.14641666666666667
+    - type: precision_at_3
+      value: 15.430749999999998
+    - type: precision_at_5
+      value: 10.833833333333333
+    - type: recall_at_1
+      value: 24.79425
+    - type: recall_at_10
+      value: 48.582916666666655
+    - type: recall_at_100
+      value: 69.88499999999999
+    - type: recall_at_1000
+      value: 87.211
+    - type: recall_at_3
+      value: 36.625499999999995
+    - type: recall_at_5
+      value: 41.553999999999995
+    - type: map_at_1
+      value: 22.767
+    - type: map_at_10
+      value: 28.450999999999997
+    - type: map_at_100
+      value: 29.332
+    - type: map_at_1000
+      value: 29.426000000000002
+    - type: map_at_3
+      value: 26.379
+    - type: map_at_5
+      value: 27.584999999999997
+    - type: mrr_at_1
+      value: 25.46
+    - type: mrr_at_10
+      value: 30.974
+    - type: mrr_at_100
+      value: 31.784000000000002
+    - type: mrr_at_1000
+      value: 31.857999999999997
+    - type: mrr_at_3
+      value: 28.962
+    - type: mrr_at_5
+      value: 30.066
+    - type: ndcg_at_1
+      value: 25.46
+    - type: ndcg_at_10
+      value: 32.041
+    - type: ndcg_at_100
+      value: 36.522
+    - type: ndcg_at_1000
+      value: 39.101
+    - type: ndcg_at_3
+      value: 28.152
+    - type: ndcg_at_5
+      value: 30.03
+    - type: precision_at_1
+      value: 25.46
+    - type: precision_at_10
+      value: 4.893
+    - type: precision_at_100
+      value: 0.77
+    - type: precision_at_1000
+      value: 0.107
+    - type: precision_at_3
+      value: 11.605
+    - type: precision_at_5
+      value: 8.19
+    - type: recall_at_1
+      value: 22.767
+    - type: recall_at_10
+      value: 40.71
+    - type: recall_at_100
+      value: 61.334999999999994
+    - type: recall_at_1000
+      value: 80.567
+    - type: recall_at_3
+      value: 30.198000000000004
+    - type: recall_at_5
+      value: 34.803
+    - type: map_at_1
+      value: 16.722
+    - type: map_at_10
+      value: 22.794
+    - type: map_at_100
+      value: 23.7
+    - type: map_at_1000
+      value: 23.822
+    - type: map_at_3
+      value: 20.781
+    - type: map_at_5
+      value: 22.024
+    - type: mrr_at_1
+      value: 20.061999999999998
+    - type: mrr_at_10
+      value: 26.346999999999998
+    - type: mrr_at_100
+      value: 27.153
+    - type: mrr_at_1000
+      value: 27.233
+    - type: mrr_at_3
+      value: 24.375
+    - type: mrr_at_5
+      value: 25.593
+    - type: ndcg_at_1
+      value: 20.061999999999998
+    - type: ndcg_at_10
+      value: 26.785999999999998
+    - type: ndcg_at_100
+      value: 31.319999999999997
+    - type: ndcg_at_1000
+      value: 34.346
+    - type: ndcg_at_3
+      value: 23.219
+    - type: ndcg_at_5
+      value: 25.107000000000003
+    - type: precision_at_1
+      value: 20.061999999999998
+    - type: precision_at_10
+      value: 4.78
+    - type: precision_at_100
+      value: 0.83
+    - type: precision_at_1000
+      value: 0.125
+    - type: precision_at_3
+      value: 10.874
+    - type: precision_at_5
+      value: 7.956
+    - type: recall_at_1
+      value: 16.722
+    - type: recall_at_10
+      value: 35.204
+    - type: recall_at_100
+      value: 55.797
+    - type: recall_at_1000
+      value: 77.689
+    - type: recall_at_3
+      value: 25.245
+    - type: recall_at_5
+      value: 30.115
+    - type: map_at_1
+      value: 24.842
+    - type: map_at_10
+      value: 32.917
+    - type: map_at_100
+      value: 33.961000000000006
+    - type: map_at_1000
+      value: 34.069
+    - type: map_at_3
+      value: 30.595
+    - type: map_at_5
+      value: 31.837
+    - type: mrr_at_1
+      value: 29.011
+    - type: mrr_at_10
+      value: 36.977
+    - type: mrr_at_100
+      value: 37.814
+    - type: mrr_at_1000
+      value: 37.885999999999996
+    - type: mrr_at_3
+      value: 34.966
+    - type: mrr_at_5
+      value: 36.043
+    - type: ndcg_at_1
+      value: 29.011
+    - type: ndcg_at_10
+      value: 37.735
+    - type: ndcg_at_100
+      value: 42.683
+    - type: ndcg_at_1000
+      value: 45.198
+    - type: ndcg_at_3
+      value: 33.650000000000006
+    - type: ndcg_at_5
+      value: 35.386
+    - type: precision_at_1
+      value: 29.011
+    - type: precision_at_10
+      value: 6.259
+    - type: precision_at_100
+      value: 0.984
+    - type: precision_at_1000
+      value: 0.13
+    - type: precision_at_3
+      value: 15.329999999999998
+    - type: precision_at_5
+      value: 10.541
+    - type: recall_at_1
+      value: 24.842
+    - type: recall_at_10
+      value: 48.304
+    - type: recall_at_100
+      value: 70.04899999999999
+    - type: recall_at_1000
+      value: 87.82600000000001
+    - type: recall_at_3
+      value: 36.922
+    - type: recall_at_5
+      value: 41.449999999999996
+    - type: map_at_1
+      value: 24.252000000000002
+    - type: map_at_10
+      value: 32.293
+    - type: map_at_100
+      value: 33.816
+    - type: map_at_1000
+      value: 34.053
+    - type: map_at_3
+      value: 29.781999999999996
+    - type: map_at_5
+      value: 31.008000000000003
+    - type: mrr_at_1
+      value: 29.051
+    - type: mrr_at_10
+      value: 36.722
+    - type: mrr_at_100
+      value: 37.663000000000004
+    - type: mrr_at_1000
+      value: 37.734
+    - type: mrr_at_3
+      value: 34.354
+    - type: mrr_at_5
+      value: 35.609
+    - type: ndcg_at_1
+      value: 29.051
+    - type: ndcg_at_10
+      value: 37.775999999999996
+    - type: ndcg_at_100
+      value: 43.221
+    - type: ndcg_at_1000
+      value: 46.116
+    - type: ndcg_at_3
+      value: 33.403
+    - type: ndcg_at_5
+      value: 35.118
+    - type: precision_at_1
+      value: 29.051
+    - type: precision_at_10
+      value: 7.332
+    - type: precision_at_100
+      value: 1.49
+    - type: precision_at_1000
+      value: 0.23600000000000002
+    - type: precision_at_3
+      value: 15.415000000000001
+    - type: precision_at_5
+      value: 11.107
+    - type: recall_at_1
+      value: 24.252000000000002
+    - type: recall_at_10
+      value: 47.861
+    - type: recall_at_100
+      value: 72.21600000000001
+    - type: recall_at_1000
+      value: 90.886
+    - type: recall_at_3
+      value: 35.533
+    - type: recall_at_5
+      value: 39.959
+    - type: map_at_1
+      value: 20.025000000000002
+    - type: map_at_10
+      value: 27.154
+    - type: map_at_100
+      value: 28.118
+    - type: map_at_1000
+      value: 28.237000000000002
+    - type: map_at_3
+      value: 25.017
+    - type: map_at_5
+      value: 25.832
+    - type: mrr_at_1
+      value: 21.627
+    - type: mrr_at_10
+      value: 28.884999999999998
+    - type: mrr_at_100
+      value: 29.741
+    - type: mrr_at_1000
+      value: 29.831999999999997
+    - type: mrr_at_3
+      value: 26.741
+    - type: mrr_at_5
+      value: 27.628000000000004
+    - type: ndcg_at_1
+      value: 21.627
+    - type: ndcg_at_10
+      value: 31.436999999999998
+    - type: ndcg_at_100
+      value: 36.181000000000004
+    - type: ndcg_at_1000
+      value: 38.986
+    - type: ndcg_at_3
+      value: 27.025
+    - type: ndcg_at_5
+      value: 28.436
+    - type: precision_at_1
+      value: 21.627
+    - type: precision_at_10
+      value: 5.009
+    - type: precision_at_100
+      value: 0.7929999999999999
+    - type: precision_at_1000
+      value: 0.11299999999999999
+    - type: precision_at_3
+      value: 11.522
+    - type: precision_at_5
+      value: 7.763000000000001
+    - type: recall_at_1
+      value: 20.025000000000002
+    - type: recall_at_10
+      value: 42.954
+    - type: recall_at_100
+      value: 64.67500000000001
+    - type: recall_at_1000
+      value: 85.301
+    - type: recall_at_3
+      value: 30.892999999999997
+    - type: recall_at_5
+      value: 34.288000000000004
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB ClimateFEVER
+      revision: None
+      split: test
+      type: climate-fever
+    metrics:
+    - type: map_at_1
+      value: 10.079
+    - type: map_at_10
+      value: 16.930999999999997
+    - type: map_at_100
+      value: 18.398999999999997
+    - type: map_at_1000
+      value: 18.561
+    - type: map_at_3
+      value: 14.294
+    - type: map_at_5
+      value: 15.579
+    - type: mrr_at_1
+      value: 22.606
+    - type: mrr_at_10
+      value: 32.513
+    - type: mrr_at_100
+      value: 33.463
+    - type: mrr_at_1000
+      value: 33.513999999999996
+    - type: mrr_at_3
+      value: 29.479
+    - type: mrr_at_5
+      value: 31.3
+    - type: ndcg_at_1
+      value: 22.606
+    - type: ndcg_at_10
+      value: 24.053
+    - type: ndcg_at_100
+      value: 30.258000000000003
+    - type: ndcg_at_1000
+      value: 33.516
+    - type: ndcg_at_3
+      value: 19.721
+    - type: ndcg_at_5
+      value: 21.144
+    - type: precision_at_1
+      value: 22.606
+    - type: precision_at_10
+      value: 7.55
+    - type: precision_at_100
+      value: 1.399
+    - type: precision_at_1000
+      value: 0.2
+    - type: precision_at_3
+      value: 14.701
+    - type: precision_at_5
+      value: 11.192
+    - type: recall_at_1
+      value: 10.079
+    - type: recall_at_10
+      value: 28.970000000000002
+    - type: recall_at_100
+      value: 50.805
+    - type: recall_at_1000
+      value: 69.378
+    - type: recall_at_3
+      value: 18.199
+    - type: recall_at_5
+      value: 22.442
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB DBPedia
+      revision: None
+      split: test
+      type: dbpedia-entity
+    metrics:
+    - type: map_at_1
+      value: 7.794
+    - type: map_at_10
+      value: 15.165999999999999
+    - type: map_at_100
+      value: 20.508000000000003
+    - type: map_at_1000
+      value: 21.809
+    - type: map_at_3
+      value: 11.568000000000001
+    - type: map_at_5
+      value: 13.059000000000001
+    - type: mrr_at_1
+      value: 56.49999999999999
+    - type: mrr_at_10
+      value: 65.90899999999999
+    - type: mrr_at_100
+      value: 66.352
+    - type: mrr_at_1000
+      value: 66.369
+    - type: mrr_at_3
+      value: 64.0
+    - type: mrr_at_5
+      value: 65.10000000000001
+    - type: ndcg_at_1
+      value: 44.25
+    - type: ndcg_at_10
+      value: 32.649
+    - type: ndcg_at_100
+      value: 36.668
+    - type: ndcg_at_1000
+      value: 43.918
+    - type: ndcg_at_3
+      value: 37.096000000000004
+    - type: ndcg_at_5
+      value: 34.048
+    - type: precision_at_1
+      value: 56.49999999999999
+    - type: precision_at_10
+      value: 25.45
+    - type: precision_at_100
+      value: 8.055
+    - type: precision_at_1000
+      value: 1.7489999999999999
+    - type: precision_at_3
+      value: 41.0
+    - type: precision_at_5
+      value: 32.85
+    - type: recall_at_1
+      value: 7.794
+    - type: recall_at_10
+      value: 20.101
+    - type: recall_at_100
+      value: 42.448
+    - type: recall_at_1000
+      value: 65.88000000000001
+    - type: recall_at_3
+      value: 12.753
+    - type: recall_at_5
+      value: 15.307
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB EmotionClassification
+      revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
+      split: test
+      type: mteb/emotion
+    metrics:
+    - type: accuracy
+      value: 44.01
+    - type: f1
+      value: 38.659680951114964
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB FEVER
+      revision: None
+      split: test
+      type: fever
+    metrics:
+    - type: map_at_1
+      value: 49.713
+    - type: map_at_10
+      value: 61.79
+    - type: map_at_100
+      value: 62.28
+    - type: map_at_1000
+      value: 62.297000000000004
+    - type: map_at_3
+      value: 59.361
+    - type: map_at_5
+      value: 60.92100000000001
+    - type: mrr_at_1
+      value: 53.405
+    - type: mrr_at_10
+      value: 65.79899999999999
+    - type: mrr_at_100
+      value: 66.219
+    - type: mrr_at_1000
+      value: 66.227
+    - type: mrr_at_3
+      value: 63.431000000000004
+    - type: mrr_at_5
+      value: 64.98
+    - type: ndcg_at_1
+      value: 53.405
+    - type: ndcg_at_10
+      value: 68.01899999999999
+    - type: ndcg_at_100
+      value: 70.197
+    - type: ndcg_at_1000
+      value: 70.571
+    - type: ndcg_at_3
+      value: 63.352
+    - type: ndcg_at_5
+      value: 66.018
+    - type: precision_at_1
+      value: 53.405
+    - type: precision_at_10
+      value: 9.119
+    - type: precision_at_100
+      value: 1.03
+    - type: precision_at_1000
+      value: 0.107
+    - type: precision_at_3
+      value: 25.602999999999998
+    - type: precision_at_5
+      value: 16.835
+    - type: recall_at_1
+      value: 49.713
+    - type: recall_at_10
+      value: 83.306
+    - type: recall_at_100
+      value: 92.92
+    - type: recall_at_1000
+      value: 95.577
+    - type: recall_at_3
+      value: 70.798
+    - type: recall_at_5
+      value: 77.254
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB FiQA2018
+      revision: None
+      split: test
+      type: fiqa
+    metrics:
+    - type: map_at_1
+      value: 15.310000000000002
+    - type: map_at_10
+      value: 26.204
+    - type: map_at_100
+      value: 27.932000000000002
+    - type: map_at_1000
+      value: 28.121000000000002
+    - type: map_at_3
+      value: 22.481
+    - type: map_at_5
+      value: 24.678
+    - type: mrr_at_1
+      value: 29.784
+    - type: mrr_at_10
+      value: 39.582
+    - type: mrr_at_100
+      value: 40.52
+    - type: mrr_at_1000
+      value: 40.568
+    - type: mrr_at_3
+      value: 37.114000000000004
+    - type: mrr_at_5
+      value: 38.596000000000004
+    - type: ndcg_at_1
+      value: 29.784
+    - type: ndcg_at_10
+      value: 33.432
+    - type: ndcg_at_100
+      value: 40.281
+    - type: ndcg_at_1000
+      value: 43.653999999999996
+    - type: ndcg_at_3
+      value: 29.612
+    - type: ndcg_at_5
+      value: 31.223
+    - type: precision_at_1
+      value: 29.784
+    - type: precision_at_10
+      value: 9.645
+    - type: precision_at_100
+      value: 1.645
+    - type: precision_at_1000
+      value: 0.22499999999999998
+    - type: precision_at_3
+      value: 20.165
+    - type: precision_at_5
+      value: 15.401000000000002
+    - type: recall_at_1
+      value: 15.310000000000002
+    - type: recall_at_10
+      value: 40.499
+    - type: recall_at_100
+      value: 66.643
+    - type: recall_at_1000
+      value: 87.059
+    - type: recall_at_3
+      value: 27.492
+    - type: recall_at_5
+      value: 33.748
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB HotpotQA
+      revision: None
+      split: test
+      type: hotpotqa
+    metrics:
+    - type: map_at_1
+      value: 33.599000000000004
+    - type: map_at_10
+      value: 47.347
+    - type: map_at_100
+      value: 48.191
+    - type: map_at_1000
+      value: 48.263
+    - type: map_at_3
+      value: 44.698
+    - type: map_at_5
+      value: 46.278999999999996
+    - type: mrr_at_1
+      value: 67.19800000000001
+    - type: mrr_at_10
+      value: 74.054
+    - type: mrr_at_100
+      value: 74.376
+    - type: mrr_at_1000
+      value: 74.392
+    - type: mrr_at_3
+      value: 72.849
+    - type: mrr_at_5
+      value: 73.643
+    - type: ndcg_at_1
+      value: 67.19800000000001
+    - type: ndcg_at_10
+      value: 56.482
+    - type: ndcg_at_100
+      value: 59.694
+    - type: ndcg_at_1000
+      value: 61.204
+    - type: ndcg_at_3
+      value: 52.43299999999999
+    - type: ndcg_at_5
+      value: 54.608000000000004
+    - type: precision_at_1
+      value: 67.19800000000001
+    - type: precision_at_10
+      value: 11.613999999999999
+    - type: precision_at_100
+      value: 1.415
+    - type: precision_at_1000
+      value: 0.16199999999999998
+    - type: precision_at_3
+      value: 32.726
+    - type: precision_at_5
+      value: 21.349999999999998
+    - type: recall_at_1
+      value: 33.599000000000004
+    - type: recall_at_10
+      value: 58.069
+    - type: recall_at_100
+      value: 70.736
+    - type: recall_at_1000
+      value: 80.804
+    - type: recall_at_3
+      value: 49.088
+    - type: recall_at_5
+      value: 53.376000000000005
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB ImdbClassification
+      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
+      split: test
+      type: mteb/imdb
+    metrics:
+    - type: accuracy
+      value: 73.64359999999999
+    - type: ap
+      value: 67.54685976014599
+    - type: f1
+      value: 73.55148707559482
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB MSMARCO
+      revision: None
+      split: dev
+      type: msmarco
+    metrics:
+    - type: map_at_1
+      value: 19.502
+    - type: map_at_10
+      value: 30.816
+    - type: map_at_100
+      value: 32.007999999999996
+    - type: map_at_1000
+      value: 32.067
+    - type: map_at_3
+      value: 27.215
+    - type: map_at_5
+      value: 29.304000000000002
+    - type: mrr_at_1
+      value: 20.072000000000003
+    - type: mrr_at_10
+      value: 31.406
+    - type: mrr_at_100
+      value: 32.549
+    - type: mrr_at_1000
+      value: 32.602
+    - type: mrr_at_3
+      value: 27.839000000000002
+    - type: mrr_at_5
+      value: 29.926000000000002
+    - type: ndcg_at_1
+      value: 20.086000000000002
+    - type: ndcg_at_10
+      value: 37.282
+    - type: ndcg_at_100
+      value: 43.206
+    - type: ndcg_at_1000
+      value: 44.690000000000005
+    - type: ndcg_at_3
+      value: 29.932
+    - type: ndcg_at_5
+      value: 33.668
+    - type: precision_at_1
+      value: 20.086000000000002
+    - type: precision_at_10
+      value: 5.961
+    - type: precision_at_100
+      value: 0.898
+    - type: precision_at_1000
+      value: 0.10200000000000001
+    - type: precision_at_3
+      value: 12.856000000000002
+    - type: precision_at_5
+      value: 9.596
+    - type: recall_at_1
+      value: 19.502
+    - type: recall_at_10
+      value: 57.182
+    - type: recall_at_100
+      value: 84.952
+    - type: recall_at_1000
+      value: 96.34700000000001
+    - type: recall_at_3
+      value: 37.193
+    - type: recall_at_5
+      value: 46.157
+    task:
+      type: Retrieval
+  - dataset:
+      config: en
+      name: MTEB MTOPDomainClassification (en)
+      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
+      split: test
+      type: mteb/mtop_domain
+    metrics:
+    - type: accuracy
+      value: 93.96488828089375
+    - type: f1
+      value: 93.32119260543482
+    task:
+      type: Classification
+  - dataset:
+      config: en
+      name: MTEB MTOPIntentClassification (en)
+      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
+      split: test
+      type: mteb/mtop_intent
+    metrics:
+    - type: accuracy
+      value: 72.4965800273598
+    - type: f1
+      value: 49.34896217536082
+    task:
+      type: Classification
+  - dataset:
+      config: en
+      name: MTEB MassiveIntentClassification (en)
+      revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
+      split: test
+      type: mteb/amazon_massive_intent
+    metrics:
+    - type: accuracy
+      value: 67.60928043039678
+    - type: f1
+      value: 64.34244712074538
+    task:
+      type: Classification
+  - dataset:
+      config: en
+      name: MTEB MassiveScenarioClassification (en)
+      revision: 7d571f92784cd94a019292a1f45445077d0ef634
+      split: test
+      type: mteb/amazon_massive_scenario
+    metrics:
+    - type: accuracy
+      value: 69.75453934095493
+    - type: f1
+      value: 68.39224867489249
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB MedrxivClusteringP2P
+      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
+      split: test
+      type: mteb/medrxiv-clustering-p2p
+    metrics:
+    - type: v_measure
+      value: 31.862573504920082
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB MedrxivClusteringS2S
+      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
+      split: test
+      type: mteb/medrxiv-clustering-s2s
+    metrics:
+    - type: v_measure
+      value: 27.511123551196803
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB MindSmallReranking
+      revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
+      split: test
+      type: mteb/mind_small
+    metrics:
+    - type: map
+      value: 30.99145104942086
+    - type: mrr
+      value: 32.03606480418627
+    task:
+      type: Reranking
+  - dataset:
+      config: default
+      name: MTEB NFCorpus
+      revision: None
+      split: test
+      type: nfcorpus
+    metrics:
+    - type: map_at_1
+      value: 5.015
+    - type: map_at_10
+      value: 11.054
+    - type: map_at_100
+      value: 13.773
+    - type: map_at_1000
+      value: 15.082999999999998
+    - type: map_at_3
+      value: 8.253
+    - type: map_at_5
+      value: 9.508999999999999
+    - type: mrr_at_1
+      value: 42.105
+    - type: mrr_at_10
+      value: 50.44499999999999
+    - type: mrr_at_100
+      value: 51.080000000000005
+    - type: mrr_at_1000
+      value: 51.129999999999995
+    - type: mrr_at_3
+      value: 48.555
+    - type: mrr_at_5
+      value: 49.84
+    - type: ndcg_at_1
+      value: 40.402
+    - type: ndcg_at_10
+      value: 30.403000000000002
+    - type: ndcg_at_100
+      value: 28.216
+    - type: ndcg_at_1000
+      value: 37.021
+    - type: ndcg_at_3
+      value: 35.53
+    - type: ndcg_at_5
+      value: 33.202999999999996
+    - type: precision_at_1
+      value: 42.105
+    - type: precision_at_10
+      value: 22.353
+    - type: precision_at_100
+      value: 7.266
+    - type: precision_at_1000
+      value: 2.011
+    - type: precision_at_3
+      value: 32.921
+    - type: precision_at_5
+      value: 28.297
+    - type: recall_at_1
+      value: 5.015
+    - type: recall_at_10
+      value: 14.393
+    - type: recall_at_100
+      value: 28.893
+    - type: recall_at_1000
+      value: 60.18
+    - type: recall_at_3
+      value: 9.184000000000001
+    - type: recall_at_5
+      value: 11.39
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB NQ
+      revision: None
+      split: test
+      type: nq
+    metrics:
+    - type: map_at_1
+      value: 29.524
+    - type: map_at_10
+      value: 44.182
+    - type: map_at_100
+      value: 45.228
+    - type: map_at_1000
+      value: 45.265
+    - type: map_at_3
+      value: 39.978
+    - type: map_at_5
+      value: 42.482
+    - type: mrr_at_1
+      value: 33.256
+    - type: mrr_at_10
+      value: 46.661
+    - type: mrr_at_100
+      value: 47.47
+    - type: mrr_at_1000
+      value: 47.496
+    - type: mrr_at_3
+      value: 43.187999999999995
+    - type: mrr_at_5
+      value: 45.330999999999996
+    - type: ndcg_at_1
+      value: 33.227000000000004
+    - type: ndcg_at_10
+      value: 51.589
+    - type: ndcg_at_100
+      value: 56.043
+    - type: ndcg_at_1000
+      value: 56.937000000000005
+    - type: ndcg_at_3
+      value: 43.751
+    - type: ndcg_at_5
+      value: 47.937000000000005
+    - type: precision_at_1
+      value: 33.227000000000004
+    - type: precision_at_10
+      value: 8.556999999999999
+    - type: precision_at_100
+      value: 1.103
+    - type: precision_at_1000
+      value: 0.11900000000000001
+    - type: precision_at_3
+      value: 19.921
+    - type: precision_at_5
+      value: 14.396999999999998
+    - type: recall_at_1
+      value: 29.524
+    - type: recall_at_10
+      value: 71.615
+    - type: recall_at_100
+      value: 91.056
+    - type: recall_at_1000
+      value: 97.72800000000001
+    - type: recall_at_3
+      value: 51.451
+    - type: recall_at_5
+      value: 61.119
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB QuoraRetrieval
+      revision: None
+      split: test
+      type: quora
+    metrics:
+    - type: map_at_1
+      value: 69.596
+    - type: map_at_10
+      value: 83.281
+    - type: map_at_100
+      value: 83.952
+    - type: map_at_1000
+      value: 83.97200000000001
+    - type: map_at_3
+      value: 80.315
+    - type: map_at_5
+      value: 82.223
+    - type: mrr_at_1
+      value: 80.17
+    - type: mrr_at_10
+      value: 86.522
+    - type: mrr_at_100
+      value: 86.644
+    - type: mrr_at_1000
+      value: 86.64500000000001
+    - type: mrr_at_3
+      value: 85.438
+    - type: mrr_at_5
+      value: 86.21799999999999
+    - type: ndcg_at_1
+      value: 80.19
+    - type: ndcg_at_10
+      value: 87.19
+    - type: ndcg_at_100
+      value: 88.567
+    - type: ndcg_at_1000
+      value: 88.70400000000001
+    - type: ndcg_at_3
+      value: 84.17999999999999
+    - type: ndcg_at_5
+      value: 85.931
+    - type: precision_at_1
+      value: 80.19
+    - type: precision_at_10
+      value: 13.209000000000001
+    - type: precision_at_100
+      value: 1.518
+    - type: precision_at_1000
+      value: 0.157
+    - type: precision_at_3
+      value: 36.717
+    - type: precision_at_5
+      value: 24.248
+    - type: recall_at_1
+      value: 69.596
+    - type: recall_at_10
+      value: 94.533
+    - type: recall_at_100
+      value: 99.322
+    - type: recall_at_1000
+      value: 99.965
+    - type: recall_at_3
+      value: 85.911
+    - type: recall_at_5
+      value: 90.809
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB RedditClustering
+      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
+      split: test
+      type: mteb/reddit-clustering
+    metrics:
+    - type: v_measure
+      value: 49.27650627571912
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB RedditClusteringP2P
+      revision: 282350215ef01743dc01b456c7f5241fa8937f16
+      split: test
+      type: mteb/reddit-clustering-p2p
+    metrics:
+    - type: v_measure
+      value: 57.08550946534183
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB SCIDOCS
+      revision: None
+      split: test
+      type: scidocs
+    metrics:
+    - type: map_at_1
+      value: 4.568
+    - type: map_at_10
+      value: 10.862
+    - type: map_at_100
+      value: 12.757
+    - type: map_at_1000
+      value: 13.031
+    - type: map_at_3
+      value: 7.960000000000001
+    - type: map_at_5
+      value: 9.337
+    - type: mrr_at_1
+      value: 22.5
+    - type: mrr_at_10
+      value: 32.6
+    - type: mrr_at_100
+      value: 33.603
+    - type: mrr_at_1000
+      value: 33.672000000000004
+    - type: mrr_at_3
+      value: 29.299999999999997
+    - type: mrr_at_5
+      value: 31.25
+    - type: ndcg_at_1
+      value: 22.5
+    - type: ndcg_at_10
+      value: 18.605
+    - type: ndcg_at_100
+      value: 26.029999999999998
+    - type: ndcg_at_1000
+      value: 31.256
+    - type: ndcg_at_3
+      value: 17.873
+    - type: ndcg_at_5
+      value: 15.511
+    - type: precision_at_1
+      value: 22.5
+    - type: precision_at_10
+      value: 9.58
+    - type: precision_at_100
+      value: 2.033
+    - type: precision_at_1000
+      value: 0.33
+    - type: precision_at_3
+      value: 16.633
+    - type: precision_at_5
+      value: 13.54
+    - type: recall_at_1
+      value: 4.568
+    - type: recall_at_10
+      value: 19.402
+    - type: recall_at_100
+      value: 41.277
+    - type: recall_at_1000
+      value: 66.963
+    - type: recall_at_3
+      value: 10.112
+    - type: recall_at_5
+      value: 13.712
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB SICK-R
+      revision: a6ea5a8cab320b040a23452cc28066d9beae2cee
+      split: test
+      type: mteb/sickr-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 83.31992291680787
+    - type: cos_sim_spearman
+      value: 76.7212346922664
+    - type: euclidean_pearson
+      value: 80.42189271706478
+    - type: euclidean_spearman
+      value: 76.7212342532493
+    - type: manhattan_pearson
+      value: 80.33171093031578
+    - type: manhattan_spearman
+      value: 76.63192883074694
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STS12
+      revision: a0d554a64d88156834ff5ae9920b964011b16384
+      split: test
+      type: mteb/sts12-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 83.16654278886763
+    - type: cos_sim_spearman
+      value: 73.66390263429565
+    - type: euclidean_pearson
+      value: 79.7485360086639
+    - type: euclidean_spearman
+      value: 73.66389870373436
+    - type: manhattan_pearson
+      value: 79.73652237443706
+    - type: manhattan_spearman
+      value: 73.65296117151647
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STS13
+      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
+      split: test
+      type: mteb/sts13-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 82.40389689929246
+    - type: cos_sim_spearman
+      value: 83.29727595993955
+    - type: euclidean_pearson
+      value: 82.23970587854079
+    - type: euclidean_spearman
+      value: 83.29727595993955
+    - type: manhattan_pearson
+      value: 82.18823600831897
+    - type: manhattan_spearman
+      value: 83.20746192209594
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STS14
+      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
+      split: test
+      type: mteb/sts14-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 81.73505246913413
+    - type: cos_sim_spearman
+      value: 79.1686548248754
+    - type: euclidean_pearson
+      value: 80.48889135993412
+    - type: euclidean_spearman
+      value: 79.16864112930354
+    - type: manhattan_pearson
+      value: 80.40720651057302
+    - type: manhattan_spearman
+      value: 79.0640155089286
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STS15
+      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
+      split: test
+      type: mteb/sts15-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 86.3953512879065
+    - type: cos_sim_spearman
+      value: 87.29947322714338
+    - type: euclidean_pearson
+      value: 86.59759438529645
+    - type: euclidean_spearman
+      value: 87.29947511092824
+    - type: manhattan_pearson
+      value: 86.52097806169155
+    - type: manhattan_spearman
+      value: 87.22987242146534
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STS16
+      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
+      split: test
+      type: mteb/sts16-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 82.48565753792056
+    - type: cos_sim_spearman
+      value: 83.6049720319893
+    - type: euclidean_pearson
+      value: 82.56452023172913
+    - type: euclidean_spearman
+      value: 83.60490168191697
+    - type: manhattan_pearson
+      value: 82.58079941137872
+    - type: manhattan_spearman
+      value: 83.60975807374051
+    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: 88.18239976618212
+    - type: cos_sim_spearman
+      value: 88.23061724730616
+    - type: euclidean_pearson
+      value: 87.78482472776658
+    - type: euclidean_spearman
+      value: 88.23061724730616
+    - type: manhattan_pearson
+      value: 87.75059641730239
+    - type: manhattan_spearman
+      value: 88.22527413524622
+    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: 63.42816418706765
+    - type: cos_sim_spearman
+      value: 63.4569864520124
+    - type: euclidean_pearson
+      value: 64.35405409953853
+    - type: euclidean_spearman
+      value: 63.4569864520124
+    - type: manhattan_pearson
+      value: 63.96649236073056
+    - type: manhattan_spearman
+      value: 63.01448583722708
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB STSBenchmark
+      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
+      split: test
+      type: mteb/stsbenchmark-sts
+    metrics:
+    - type: cos_sim_pearson
+      value: 83.41659638047614
+    - type: cos_sim_spearman
+      value: 84.03893866106175
+    - type: euclidean_pearson
+      value: 84.2251203953798
+    - type: euclidean_spearman
+      value: 84.03893866106175
+    - type: manhattan_pearson
+      value: 84.22733643205514
+    - type: manhattan_spearman
+      value: 84.06504411263612
+    task:
+      type: STS
+  - dataset:
+      config: default
+      name: MTEB SciDocsRR
+      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
+      split: test
+      type: mteb/scidocs-reranking
+    metrics:
+    - type: map
+      value: 79.75608022582414
+    - type: mrr
+      value: 94.0947732369301
+    task:
+      type: Reranking
+  - dataset:
+      config: default
+      name: MTEB SciFact
+      revision: None
+      split: test
+      type: scifact
+    metrics:
+    - type: map_at_1
+      value: 50.161
+    - type: map_at_10
+      value: 59.458999999999996
+    - type: map_at_100
+      value: 60.156
+    - type: map_at_1000
+      value: 60.194
+    - type: map_at_3
+      value: 56.45400000000001
+    - type: map_at_5
+      value: 58.165
+    - type: mrr_at_1
+      value: 53.333
+    - type: mrr_at_10
+      value: 61.050000000000004
+    - type: mrr_at_100
+      value: 61.586
+    - type: mrr_at_1000
+      value: 61.624
+    - type: mrr_at_3
+      value: 58.889
+    - type: mrr_at_5
+      value: 60.122
+    - type: ndcg_at_1
+      value: 53.333
+    - type: ndcg_at_10
+      value: 63.888999999999996
+    - type: ndcg_at_100
+      value: 66.963
+    - type: ndcg_at_1000
+      value: 68.062
+    - type: ndcg_at_3
+      value: 59.01
+    - type: ndcg_at_5
+      value: 61.373999999999995
+    - type: precision_at_1
+      value: 53.333
+    - type: precision_at_10
+      value: 8.633000000000001
+    - type: precision_at_100
+      value: 1.027
+    - type: precision_at_1000
+      value: 0.11199999999999999
+    - type: precision_at_3
+      value: 23.111
+    - type: precision_at_5
+      value: 15.467
+    - type: recall_at_1
+      value: 50.161
+    - type: recall_at_10
+      value: 75.922
+    - type: recall_at_100
+      value: 90.0
+    - type: recall_at_1000
+      value: 98.667
+    - type: recall_at_3
+      value: 62.90599999999999
+    - type: recall_at_5
+      value: 68.828
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB SprintDuplicateQuestions
+      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
+      split: test
+      type: mteb/sprintduplicatequestions-pairclassification
+    metrics:
+    - type: cos_sim_accuracy
+      value: 99.81188118811882
+    - type: cos_sim_ap
+      value: 95.11619225962413
+    - type: cos_sim_f1
+      value: 90.35840484603736
+    - type: cos_sim_precision
+      value: 91.23343527013252
+    - type: cos_sim_recall
+      value: 89.5
+    - type: dot_accuracy
+      value: 99.81188118811882
+    - type: dot_ap
+      value: 95.11619225962413
+    - type: dot_f1
+      value: 90.35840484603736
+    - type: dot_precision
+      value: 91.23343527013252
+    - type: dot_recall
+      value: 89.5
+    - type: euclidean_accuracy
+      value: 99.81188118811882
+    - type: euclidean_ap
+      value: 95.11619225962413
+    - type: euclidean_f1
+      value: 90.35840484603736
+    - type: euclidean_precision
+      value: 91.23343527013252
+    - type: euclidean_recall
+      value: 89.5
+    - type: manhattan_accuracy
+      value: 99.80891089108911
+    - type: manhattan_ap
+      value: 95.07294266220966
+    - type: manhattan_f1
+      value: 90.21794221996959
+    - type: manhattan_precision
+      value: 91.46968139773895
+    - type: manhattan_recall
+      value: 89.0
+    - type: max_accuracy
+      value: 99.81188118811882
+    - type: max_ap
+      value: 95.11619225962413
+    - type: max_f1
+      value: 90.35840484603736
+    task:
+      type: PairClassification
+  - dataset:
+      config: default
+      name: MTEB StackExchangeClustering
+      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
+      split: test
+      type: mteb/stackexchange-clustering
+    metrics:
+    - type: v_measure
+      value: 55.3481874105239
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB StackExchangeClusteringP2P
+      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
+      split: test
+      type: mteb/stackexchange-clustering-p2p
+    metrics:
+    - type: v_measure
+      value: 34.421291695525
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB StackOverflowDupQuestions
+      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
+      split: test
+      type: mteb/stackoverflowdupquestions-reranking
+    metrics:
+    - type: map
+      value: 49.98746633276634
+    - type: mrr
+      value: 50.63143249724133
+    task:
+      type: Reranking
+  - dataset:
+      config: default
+      name: MTEB SummEval
+      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
+      split: test
+      type: mteb/summeval
+    metrics:
+    - type: cos_sim_pearson
+      value: 31.009961979844036
+    - type: cos_sim_spearman
+      value: 30.558416108881044
+    - type: dot_pearson
+      value: 31.009964941134253
+    - type: dot_spearman
+      value: 30.545760761761393
+    task:
+      type: Summarization
+  - dataset:
+      config: default
+      name: MTEB TRECCOVID
+      revision: None
+      split: test
+      type: trec-covid
+    metrics:
+    - type: map_at_1
+      value: 0.207
+    - type: map_at_10
+      value: 1.6
+    - type: map_at_100
+      value: 8.594
+    - type: map_at_1000
+      value: 20.213
+    - type: map_at_3
+      value: 0.585
+    - type: map_at_5
+      value: 0.9039999999999999
+    - type: mrr_at_1
+      value: 78.0
+    - type: mrr_at_10
+      value: 87.4
+    - type: mrr_at_100
+      value: 87.4
+    - type: mrr_at_1000
+      value: 87.4
+    - type: mrr_at_3
+      value: 86.667
+    - type: mrr_at_5
+      value: 87.06700000000001
+    - type: ndcg_at_1
+      value: 73.0
+    - type: ndcg_at_10
+      value: 65.18
+    - type: ndcg_at_100
+      value: 49.631
+    - type: ndcg_at_1000
+      value: 43.498999999999995
+    - type: ndcg_at_3
+      value: 71.83800000000001
+    - type: ndcg_at_5
+      value: 69.271
+    - type: precision_at_1
+      value: 78.0
+    - type: precision_at_10
+      value: 69.19999999999999
+    - type: precision_at_100
+      value: 50.980000000000004
+    - type: precision_at_1000
+      value: 19.426
+    - type: precision_at_3
+      value: 77.333
+    - type: precision_at_5
+      value: 74.0
+    - type: recall_at_1
+      value: 0.207
+    - type: recall_at_10
+      value: 1.822
+    - type: recall_at_100
+      value: 11.849
+    - type: recall_at_1000
+      value: 40.492
+    - type: recall_at_3
+      value: 0.622
+    - type: recall_at_5
+      value: 0.9809999999999999
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB Touche2020
+      revision: None
+      split: test
+      type: webis-touche2020
+    metrics:
+    - type: map_at_1
+      value: 2.001
+    - type: map_at_10
+      value: 10.376000000000001
+    - type: map_at_100
+      value: 16.936999999999998
+    - type: map_at_1000
+      value: 18.615000000000002
+    - type: map_at_3
+      value: 5.335999999999999
+    - type: map_at_5
+      value: 7.374
+    - type: mrr_at_1
+      value: 20.408
+    - type: mrr_at_10
+      value: 38.29
+    - type: mrr_at_100
+      value: 39.33
+    - type: mrr_at_1000
+      value: 39.347
+    - type: mrr_at_3
+      value: 32.993
+    - type: mrr_at_5
+      value: 36.973
+    - type: ndcg_at_1
+      value: 17.347
+    - type: ndcg_at_10
+      value: 23.515
+    - type: ndcg_at_100
+      value: 37.457
+    - type: ndcg_at_1000
+      value: 49.439
+    - type: ndcg_at_3
+      value: 22.762999999999998
+    - type: ndcg_at_5
+      value: 22.622
+    - type: precision_at_1
+      value: 20.408
+    - type: precision_at_10
+      value: 22.448999999999998
+    - type: precision_at_100
+      value: 8.184
+    - type: precision_at_1000
+      value: 1.608
+    - type: precision_at_3
+      value: 25.85
+    - type: precision_at_5
+      value: 25.306
+    - type: recall_at_1
+      value: 2.001
+    - type: recall_at_10
+      value: 17.422
+    - type: recall_at_100
+      value: 51.532999999999994
+    - type: recall_at_1000
+      value: 87.466
+    - type: recall_at_3
+      value: 6.861000000000001
+    - type: recall_at_5
+      value: 10.502
+    task:
+      type: Retrieval
+  - dataset:
+      config: default
+      name: MTEB ToxicConversationsClassification
+      revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c
+      split: test
+      type: mteb/toxic_conversations_50k
+    metrics:
+    - type: accuracy
+      value: 71.54419999999999
+    - type: ap
+      value: 14.372170450843907
+    - type: f1
+      value: 54.94420257390529
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB TweetSentimentExtractionClassification
+      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
+      split: test
+      type: mteb/tweet_sentiment_extraction
+    metrics:
+    - type: accuracy
+      value: 59.402942840973395
+    - type: f1
+      value: 59.4166538875571
+    task:
+      type: Classification
+  - dataset:
+      config: default
+      name: MTEB TwentyNewsgroupsClustering
+      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
+      split: test
+      type: mteb/twentynewsgroups-clustering
+    metrics:
+    - type: v_measure
+      value: 41.569064336457906
+    task:
+      type: Clustering
+  - dataset:
+      config: default
+      name: MTEB TwitterSemEval2015
+      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
+      split: test
+      type: mteb/twittersemeval2015-pairclassification
+    metrics:
+    - type: cos_sim_accuracy
+      value: 85.31322644096085
+    - type: cos_sim_ap
+      value: 72.14518894837381
+    - type: cos_sim_f1
+      value: 66.67489813557229
+    - type: cos_sim_precision
+      value: 62.65954977953121
+    - type: cos_sim_recall
+      value: 71.2401055408971
+    - type: dot_accuracy
+      value: 85.31322644096085
+    - type: dot_ap
+      value: 72.14521480685293
+    - type: dot_f1
+      value: 66.67489813557229
+    - type: dot_precision
+      value: 62.65954977953121
+    - type: dot_recall
+      value: 71.2401055408971
+    - type: euclidean_accuracy
+      value: 85.31322644096085
+    - type: euclidean_ap
+      value: 72.14520820485349
+    - type: euclidean_f1
+      value: 66.67489813557229
+    - type: euclidean_precision
+      value: 62.65954977953121
+    - type: euclidean_recall
+      value: 71.2401055408971
+    - type: manhattan_accuracy
+      value: 85.21785778148656
+    - type: manhattan_ap
+      value: 72.01177147657364
+    - type: manhattan_f1
+      value: 66.62594673833374
+    - type: manhattan_precision
+      value: 62.0336669699727
+    - type: manhattan_recall
+      value: 71.95250659630607
+    - type: max_accuracy
+      value: 85.31322644096085
+    - type: max_ap
+      value: 72.14521480685293
+    - type: max_f1
+      value: 66.67489813557229
+    task:
+      type: PairClassification
+  - dataset:
+      config: default
+      name: MTEB TwitterURLCorpus
+      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
+      split: test
+      type: mteb/twitterurlcorpus-pairclassification
+    metrics:
+    - type: cos_sim_accuracy
+      value: 89.12756626693057
+    - type: cos_sim_ap
+      value: 86.05430786440826
+    - type: cos_sim_f1
+      value: 78.27759692216631
+    - type: cos_sim_precision
+      value: 75.33466248931929
+    - type: cos_sim_recall
+      value: 81.45980905451185
+    - type: dot_accuracy
+      value: 89.12950673341872
+    - type: dot_ap
+      value: 86.05431161145492
+    - type: dot_f1
+      value: 78.27759692216631
+    - type: dot_precision
+      value: 75.33466248931929
+    - type: dot_recall
+      value: 81.45980905451185
+    - type: euclidean_accuracy
+      value: 89.12756626693057
+    - type: euclidean_ap
+      value: 86.05431303247397
+    - type: euclidean_f1
+      value: 78.27759692216631
+    - type: euclidean_precision
+      value: 75.33466248931929
+    - type: euclidean_recall
+      value: 81.45980905451185
+    - type: manhattan_accuracy
+      value: 89.04994760740482
+    - type: manhattan_ap
+      value: 86.00860610892074
+    - type: manhattan_f1
+      value: 78.1846776005392
+    - type: manhattan_precision
+      value: 76.10438839480975
+    - type: manhattan_recall
+      value: 80.3818909762858
+    - type: max_accuracy
+      value: 89.12950673341872
+    - type: max_ap
+      value: 86.05431303247397
+    - type: max_f1
+      value: 78.27759692216631
+    task:
+      type: PairClassification
+tags:
+- sentence-transformers
+- feature-extraction
+- sentence-similarity
+- mteb
+- onnx
+- teradata
+---
+# A Teradata Vantage compatible Embeddings Model
+# jinaai/jina-embeddings-v2-small-en
+## Overview of this Model
+An Embedding Model which maps text (sentence/ paragraphs) into a vector.  The [jinaai/jina-embeddings-v2-small-en](https://huggingface.co/jinaai/jina-embeddings-v2-small-en) model well known for its effectiveness in capturing semantic meanings in text data. It's a state-of-the-art model trained on a large corpus, capable of generating high-quality text embeddings.
+- 32.69M params (Sizes in ONNX format - "fp32": 123.8MB, "int8": 31.14MB, "uint8": 31.14MB)
+- 8192 maximum input tokens
+- 512 dimensions of output vector
+- Licence: apache-2.0. The released models can be used for commercial purposes free of charge.
+- Reference to Original Model: https://huggingface.co/jinaai/jina-embeddings-v2-small-en
+## Quickstart: Deploying this Model in Teradata Vantage
+We have pre-converted the model into the ONNX format compatible with BYOM 6.0, eliminating the need for manual conversion.
+**Note:** Ensure you have access to a Teradata Database with BYOM 6.0 installed.
+To get started, clone the pre-converted model directly from the Teradata HuggingFace repository.
+```python
+import teradataml as tdml
+import getpass
+from huggingface_hub import hf_hub_download
+model_name = "jina-embeddings-v2-small-en"
+number_dimensions_output = 512
+model_file_name = "model.onnx"
+# Step 1: Download Model from Teradata HuggingFace Page
+hf_hub_download(repo_id=f"Teradata/{model_name}", filename=f"onnx/{model_file_name}", local_dir="./")
+hf_hub_download(repo_id=f"Teradata/{model_name}", filename=f"tokenizer.json", local_dir="./")
+# Step 2: Create Connection to Vantage
+tdml.create_context(host = input('enter your hostname'),
+                    username=input('enter your username'),
+                    password = getpass.getpass("enter your password"))
+# Step 3: Load Models into Vantage
+# a) Embedding model
+tdml.save_byom(model_id = model_name, # must be unique in the models table
+               model_file = model_file_name,
+               table_name = 'embeddings_models' )
+# b) Tokenizer
+tdml.save_byom(model_id = model_name, # must be unique in the models table
+              model_file = 'tokenizer.json',
+              table_name = 'embeddings_tokenizers')
+# Step 4: Test ONNXEmbeddings Function
+# Note that ONNXEmbeddings expects the 'payload' column to be 'txt'.
+# If it has got a different name, just rename it in a subquery/CTE.
+input_table = "emails.emails"
+embeddings_query = f"""
+SELECT
+        *
+from mldb.ONNXEmbeddings(
+        on {input_table} as InputTable
+        on (select * from embeddings_models where model_id = '{model_name}') as ModelTable DIMENSION
+        on (select model as tokenizer from embeddings_tokenizers where model_id = '{model_name}') as TokenizerTable DIMENSION
+        using
+            Accumulate('id', 'txt')
+            ModelOutputTensor('sentence_embedding')
+            EnableMemoryCheck('false')
+            OutputFormat('FLOAT32({number_dimensions_output})')
+            OverwriteCachedModel('true')
+    ) a
+"""
+DF_embeddings = tdml.DataFrame.from_query(embeddings_query)
+DF_embeddings
+```
+## What Can I Do with the Embeddings?
+Teradata Vantage includes pre-built in-database functions to process embeddings further. Explore the following examples:
+- **Semantic Clustering with TD_KMeans:** [Semantic Clustering Python Notebook](https://github.com/Teradata/jupyter-demos/blob/main/UseCases/Language_Models_InVantage/Semantic_Clustering_Python.ipynb)
+- **Semantic Distance with TD_VectorDistance:** [Semantic Similarity Python Notebook](https://github.com/Teradata/jupyter-demos/blob/main/UseCases/Language_Models_InVantage/Semantic_Similarity_Python.ipynb)
+- **RAG-Based Application with TD_VectorDistance:** [RAG and Bedrock Query PDF Notebook](https://github.com/Teradata/jupyter-demos/blob/main/UseCases/Language_Models_InVantage/RAG_and_Bedrock_QueryPDF.ipynb)
+## Deep Dive into Model Conversion to ONNX
+**The steps below outline how we converted the open-source Hugging Face model into an ONNX file compatible with the in-database ONNXEmbeddings function.**
+You do not need to perform these steps—they are provided solely for documentation and transparency. However, they may be helpful if you wish to convert another model to the required format.
+### Part 1. Importing and Converting Model using optimum
+We start by importing the pre-trained [jinaai/jina-embeddings-v2-small-en](https://huggingface.co/jinaai/jina-embeddings-v2-small-en) model from Hugging Face.
+We are downloading the ONNX files from the repository prepared by the model authors.
+After downloading, we are fixing the opset in the ONNX file for compatibility with ONNX runtime used in Teradata Vantage
+Also we adding the man pooling and normalization layers to the ONNX file
+We are generating ONNX files for multiple different precisions: fp32, int8, uint8
+You can find the detailed conversion steps in the file [convert.py](./convert.py)
+### Part 2. Running the model in Python with onnxruntime & compare results
+Once the fixes are applied, we proceed to test the correctness of the ONNX model by calculating cosine similarity between two texts using native SentenceTransformers and ONNX runtime, comparing the results.
+If the results are identical, it confirms that the ONNX model gives the same result as the native models, validating its correctness and suitability for further use in the database.
+```python
+import onnxruntime as rt
+from sentence_transformers.util import cos_sim
+from sentence_transformers import SentenceTransformer
+import transformers
+sentences_1 = 'How is the weather today?'
+sentences_2 = 'What is the current weather like today?'
+# Calculate ONNX result
+tokenizer = transformers.AutoTokenizer.from_pretrained("jinaai/jina-embeddings-v2-small-en")
+predef_sess = rt.InferenceSession("onnx/model.onnx")
+enc1 = tokenizer(sentences_1)
+embeddings_1_onnx = predef_sess.run(None,     {"input_ids": [enc1.input_ids],
+     "attention_mask": [enc1.attention_mask]})
+enc2 = tokenizer(sentences_2)
+embeddings_2_onnx = predef_sess.run(None,     {"input_ids": [enc2.input_ids],
+     "attention_mask": [enc2.attention_mask]})
+# Calculate embeddings with SentenceTransformer
+model = SentenceTransformer(model_id, trust_remote_code=True)
+embeddings_1_sentence_transformer = model.encode(sentences_1, normalize_embeddings=True, trust_remote_code=True)
+embeddings_2_sentence_transformer = model.encode(sentences_2, normalize_embeddings=True, trust_remote_code=True)
+# Compare results
+print("Cosine similiarity for embeddings calculated with ONNX:" + str(cos_sim(embeddings_1_onnx[1][0], embeddings_2_onnx[1][0])))
+print("Cosine similiarity for embeddings calculated with SentenceTransformer:" + str(cos_sim(embeddings_1_sentence_transformer, embeddings_2_sentence_transformer)))
+```
+You can find the detailed ONNX vs. SentenceTransformer result comparison steps in the file [test_local.py](./test_local.py)

config.json ADDED Viewed

	@@ -0,0 +1,38 @@

+{
+  "_attn_implementation_autoset": true,
+  "_name_or_path": "jinaai/jina-embeddings-v2-small-en",
+  "architectures": [
+    "JinaBertForMaskedLM"
+  ],
+  "attention_probs_dropout_prob": 0.0,
+  "attn_implementation": null,
+  "auto_map": {
+    "AutoConfig": "jinaai/jina-bert-implementation--configuration_bert.JinaBertConfig",
+    "AutoModel": "jinaai/jina-bert-implementation--modeling_bert.JinaBertModel",
+    "AutoModelForMaskedLM": "jinaai/jina-bert-implementation--modeling_bert.JinaBertForMaskedLM",
+    "AutoModelForSequenceClassification": "jinaai/jina-bert-implementation--modeling_bert.JinaBertForSequenceClassification"
+  },
+  "classifier_dropout": null,
+  "emb_pooler": "mean",
+  "export_model_type": "transformer",
+  "feed_forward_type": "geglu",
+  "gradient_checkpointing": false,
+  "hidden_act": "gelu",
+  "hidden_dropout_prob": 0.1,
+  "hidden_size": 512,
+  "initializer_range": 0.02,
+  "intermediate_size": 2048,
+  "layer_norm_eps": 1e-12,
+  "max_position_embeddings": 8192,
+  "model_max_length": 8192,
+  "model_type": "bert",
+  "num_attention_heads": 8,
+  "num_hidden_layers": 4,
+  "pad_token_id": 0,
+  "position_embedding_type": "alibi",
+  "torch_dtype": "float32",
+  "transformers_version": "4.47.1",
+  "type_vocab_size": 2,
+  "use_cache": true,
+  "vocab_size": 30528
+}

conversion_config.json ADDED Viewed

	@@ -0,0 +1,12 @@

+{
+    "model_id": "jinaai/jina-embeddings-v2-small-en",
+    "number_of_generated_embeddings": 512,
+    "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 +1,198 @@

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

onnx/model.onnx ADDED Viewed

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

+version https://git-lfs.github.com/spec/v1
+oid sha256:826db608d156761e86f892250600aae670b31c886b392c8badc35a0f80c88945
+size 129810013

onnx/model_int8.onnx ADDED Viewed

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

+version https://git-lfs.github.com/spec/v1
+oid sha256:8251c0f52cde601ee024eca8203cb0f754a2ac0c813b8dfdf300c9986836b049
+size 32653121

onnx/model_uint8.onnx ADDED Viewed

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

+version https://git-lfs.github.com/spec/v1
+oid sha256:9a5c7f6eb8377efa7428443ffad5a39a68ebb3ffc0c1b77e658ef72aaa068b70
+size 32653134

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("./", trust_remote_code=True)
+    enc1 = tokenizer(sentences_1)
+    enc2 = tokenizer(sentences_2)
+    for precision, file_name in precision_to_filename_map.items():
+        onnx_session = rt.InferenceSession(file_name)
+        embeddings_1_onnx = onnx_session.run(None,     {"input_ids": [enc1.input_ids],
+             "attention_mask": [enc1.attention_mask], "token_type_ids": [enc1.token_type_ids] })[0][0]
+        embeddings_2_onnx = onnx_session.run(None,     {"input_ids": [enc2.input_ids],
+             "attention_mask": [enc2.attention_mask], "token_type_ids": [enc2.token_type_ids]})[0][0]
+        del onnx_session
+        gc.collect()
+        print(f'Cosine similiarity for ONNX model with precision "{precision}" is {str(cos_sim(embeddings_1_onnx, embeddings_2_onnx))}')
+    model = SentenceTransformer(model_id, trust_remote_code=True)
+    embeddings_1_sentence_transformer = model.encode(sentences_1, normalize_embeddings=True, trust_remote_code=True)
+    embeddings_2_sentence_transformer = model.encode(sentences_2, normalize_embeddings=True, trust_remote_code=True)
+    print('Cosine similiarity for original sentence transformer model is '+str(cos_sim(embeddings_1_sentence_transformer, embeddings_2_sentence_transformer)))

test_teradata.py 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

The diff for this file is too large to render. See raw diff

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": 2147483648,
+  "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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