onnx feature-extraction mteb arctic snowflake-arctic-embed teradata sentence-similarity af ar az be bg bn ca ceb cs cy da de el en es et eu fa fi fr gl gu he hi hr ht hu hy id is it ja jv ka kk km kn ko ky lo lt lv mk ml mn mr ms my ne nl pa pl pt qu ro ru si sk sl so sq sr sv sw ta te th tl tr uk ur vi yo zh license:apache-2.0 model-index region:us
---
language:
- af
- ar
- az
- be
- bg
- bn
- ca
- ceb
- cs
- cy
- da
- de
- el
- en
- es
- et
- eu
- fa
- fi
- fr
- gl
- gu
- he
- hi
- hr
- ht
- hu
- hy
- id
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ky
- lo
- lt
- lv
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- pa
- pl
- pt
- qu
- ro
- ru
- si
- sk
- sl
- so
- sq
- sr
- sv
- sw
- ta
- te
- th
- tl
- tr
- uk
- ur
- vi
- yo
- zh
license: apache-2.0
pipeline_tag: sentence-similarity
tags:
- feature-extraction
- mteb
- arctic
- snowflake-arctic-embed
- onnx
- teradata
model-index:
- name: snowflake-arctic-embed-m-v2.0
  results:
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonCounterfactualClassification (en-ext)
      type: mteb/amazon_counterfactual
      config: en-ext
      split: test
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
    metrics:
    - type: accuracy
      value: 66.6867
    - type: f1
      value: 55.0373
    - type: f1_weighted
      value: 73.07430000000001
    - type: ap
      value: 18.077399999999997
    - type: ap_weighted
      value: 18.077399999999997
    - type: main_score
      value: 66.6867
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonCounterfactualClassification (en)
      type: mteb/amazon_counterfactual
      config: en
      split: test
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
    metrics:
    - type: accuracy
      value: 66.194
    - type: f1
      value: 60.854299999999995
    - type: f1_weighted
      value: 69.57339999999999
    - type: ap
      value: 30.279099999999996
    - type: ap_weighted
      value: 30.279099999999996
    - type: main_score
      value: 66.194
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonPolarityClassification (default)
      type: mteb/amazon_polarity
      config: default
      split: test
      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
    metrics:
    - type: accuracy
      value: 70.3589
    - type: f1
      value: 70.0409
    - type: f1_weighted
      value: 70.0409
    - type: ap
      value: 64.81949999999999
    - type: ap_weighted
      value: 64.81949999999999
    - type: main_score
      value: 70.3589
  - task:
      type: Classification
    dataset:
      name: MTEB AmazonReviewsClassification (en)
      type: mteb/amazon_reviews_multi
      config: en
      split: test
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
    metrics:
    - type: accuracy
      value: 33.766
    - type: f1
      value: 33.3656
    - type: f1_weighted
      value: 33.3656
    - type: main_score
      value: 33.766
  - task:
      type: Retrieval
    dataset:
      name: MTEB ArguAna (default)
      type: mteb/arguana
      config: default
      split: test
      revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
    metrics:
    - type: ndcg_at_1
      value: 33.144
    - type: ndcg_at_3
      value: 47.909
    - type: ndcg_at_5
      value: 52.932
    - type: ndcg_at_10
      value: 58.011
    - type: ndcg_at_20
      value: 60.168
    - type: ndcg_at_100
      value: 60.928000000000004
    - type: ndcg_at_1000
      value: 61.046
    - type: map_at_1
      value: 33.144
    - type: map_at_3
      value: 44.156
    - type: map_at_5
      value: 46.951
    - type: map_at_10
      value: 49.071999999999996
    - type: map_at_20
      value: 49.692
    - type: map_at_100
      value: 49.809
    - type: map_at_1000
      value: 49.815
    - type: recall_at_1
      value: 33.144
    - type: recall_at_3
      value: 58.819
    - type: recall_at_5
      value: 70.982
    - type: recall_at_10
      value: 86.558
    - type: recall_at_20
      value: 94.879
    - type: recall_at_100
      value: 98.791
    - type: recall_at_1000
      value: 99.644
    - type: precision_at_1
      value: 33.144
    - type: precision_at_3
      value: 19.606
    - type: precision_at_5
      value: 14.196
    - type: precision_at_10
      value: 8.656
    - type: precision_at_20
      value: 4.744000000000001
    - type: precision_at_100
      value: 0.988
    - type: precision_at_1000
      value: 0.1
    - type: mrr_at_1
      value: 33.4993
    - type: mrr_at_3
      value: 44.393100000000004
    - type: mrr_at_5
      value: 47.131299999999996
    - type: mrr_at_10
      value: 49.264599999999994
    - type: mrr_at_20
      value: 49.8707
    - type: mrr_at_100
      value: 49.987700000000004
    - type: mrr_at_1000
      value: 49.993700000000004
    - type: nauc_ndcg_at_1_max
      value: -10.8287
    - type: nauc_ndcg_at_1_std
      value: -17.1177
    - type: nauc_ndcg_at_1_diff1
      value: 14.4508
    - type: nauc_ndcg_at_3_max
      value: -7.7004
    - type: nauc_ndcg_at_3_std
      value: -16.6705
    - type: nauc_ndcg_at_3_diff1
      value: 10.0448
    - type: nauc_ndcg_at_5_max
      value: -7.0436
    - type: nauc_ndcg_at_5_std
      value: -15.8744
    - type: nauc_ndcg_at_5_diff1
      value: 9.1132
    - type: nauc_ndcg_at_10_max
      value: -7.4729
    - type: nauc_ndcg_at_10_std
      value: -14.9349
    - type: nauc_ndcg_at_10_diff1
      value: 8.527700000000001
    - type: nauc_ndcg_at_20_max
      value: -6.997000000000001
    - type: nauc_ndcg_at_20_std
      value: -14.688399999999998
    - type: nauc_ndcg_at_20_diff1
      value: 9.7605
    - type: nauc_ndcg_at_100_max
      value: -7.5599
    - type: nauc_ndcg_at_100_std
      value: -15.0565
    - type: nauc_ndcg_at_100_diff1
      value: 10.2688
    - type: nauc_ndcg_at_1000_max
      value: -7.675800000000001
    - type: nauc_ndcg_at_1000_std
      value: -15.223500000000001
    - type: nauc_ndcg_at_1000_diff1
      value: 10.32
    - type: nauc_map_at_1_max
      value: -10.8287
    - type: nauc_map_at_1_std
      value: -17.1177
    - type: nauc_map_at_1_diff1
      value: 14.4508
    - type: nauc_map_at_3_max
      value: -8.5473
    - type: nauc_map_at_3_std
      value: -16.6674
    - type: nauc_map_at_3_diff1
      value: 11.1004
    - type: nauc_map_at_5_max
      value: -8.1927
    - type: nauc_map_at_5_std
      value: -16.2275
    - type: nauc_map_at_5_diff1
      value: 10.678600000000001
    - type: nauc_map_at_10_max
      value: -8.3855
    - type: nauc_map_at_10_std
      value: -15.8309
    - type: nauc_map_at_10_diff1
      value: 10.5414
    - type: nauc_map_at_20_max
      value: -8.277700000000001
    - type: nauc_map_at_20_std
      value: -15.824
    - type: nauc_map_at_20_diff1
      value: 10.8494
    - type: nauc_map_at_100_max
      value: -8.3178
    - type: nauc_map_at_100_std
      value: -15.848300000000002
    - type: nauc_map_at_100_diff1
      value: 10.9384
    - type: nauc_map_at_1000_max
      value: -8.319799999999999
    - type: nauc_map_at_1000_std
      value: -15.8522
    - type: nauc_map_at_1000_diff1
      value: 10.9401
    - type: nauc_recall_at_1_max
      value: -10.8287
    - type: nauc_recall_at_1_std
      value: -17.1177
    - type: nauc_recall_at_1_diff1
      value: 14.4508
    - type: nauc_recall_at_3_max
      value: -5.0587
    - type: nauc_recall_at_3_std
      value: -16.730800000000002
    - type: nauc_recall_at_3_diff1
      value: 6.8079
    - type: nauc_recall_at_5_max
      value: -2.6783
    - type: nauc_recall_at_5_std
      value: -14.5046
    - type: nauc_recall_at_5_diff1
      value: 3.096
    - type: nauc_recall_at_10_max
      value: -1.5855000000000001
    - type: nauc_recall_at_10_std
      value: -8.2276
    - type: nauc_recall_at_10_diff1
      value: -6.1741
    - type: nauc_recall_at_20_max
      value: 15.754299999999999
    - type: nauc_recall_at_20_std
      value: 8.1974
    - type: nauc_recall_at_20_diff1
      value: -4.9207
    - type: nauc_recall_at_100_max
      value: 20.4574
    - type: nauc_recall_at_100_std
      value: 36.3741
    - type: nauc_recall_at_100_diff1
      value: -7.9483
    - type: nauc_recall_at_1000_max
      value: 21.6023
    - type: nauc_recall_at_1000_std
      value: 68.7296
    - type: nauc_recall_at_1000_diff1
      value: -24.9261
    - type: nauc_precision_at_1_max
      value: -10.8287
    - type: nauc_precision_at_1_std
      value: -17.1177
    - type: nauc_precision_at_1_diff1
      value: 14.4508
    - type: nauc_precision_at_3_max
      value: -5.0587
    - type: nauc_precision_at_3_std
      value: -16.730800000000002
    - type: nauc_precision_at_3_diff1
      value: 6.8079
    - type: nauc_precision_at_5_max
      value: -2.6783
    - type: nauc_precision_at_5_std
      value: -14.5046
    - type: nauc_precision_at_5_diff1
      value: 3.096
    - type: nauc_precision_at_10_max
      value: -1.5855000000000001
    - type: nauc_precision_at_10_std
      value: -8.2276
    - type: nauc_precision_at_10_diff1
      value: -6.1741
    - type: nauc_precision_at_20_max
      value: 15.754299999999999
    - type: nauc_precision_at_20_std
      value: 8.1974
    - type: nauc_precision_at_20_diff1
      value: -4.9207
    - type: nauc_precision_at_100_max
      value: 20.4574
    - type: nauc_precision_at_100_std
      value: 36.3741
    - type: nauc_precision_at_100_diff1
      value: -7.9483
    - type: nauc_precision_at_1000_max
      value: 21.6023
    - type: nauc_precision_at_1000_std
      value: 68.7296
    - type: nauc_precision_at_1000_diff1
      value: -24.9261
    - type: nauc_mrr_at_1_max
      value: -11.251999999999999
    - type: nauc_mrr_at_1_std
      value: -17.4386
    - type: nauc_mrr_at_1_diff1
      value: 13.414200000000001
    - type: nauc_mrr_at_3_max
      value: -9.7985
    - type: nauc_mrr_at_3_std
      value: -16.650000000000002
    - type: nauc_mrr_at_3_diff1
      value: 9.5099
    - type: nauc_mrr_at_5_max
      value: -9.064
    - type: nauc_mrr_at_5_std
      value: -16.4409
    - type: nauc_mrr_at_5_diff1
      value: 9.4773
    - type: nauc_mrr_at_10_max
      value: -9.310400000000001
    - type: nauc_mrr_at_10_std
      value: -16.0546
    - type: nauc_mrr_at_10_diff1
      value: 9.2528
    - type: nauc_mrr_at_20_max
      value: -9.223099999999999
    - type: nauc_mrr_at_20_std
      value: -16.0659
    - type: nauc_mrr_at_20_diff1
      value: 9.5259
    - type: nauc_mrr_at_100_max
      value: -9.2678
    - type: nauc_mrr_at_100_std
      value: -16.0911
    - type: nauc_mrr_at_100_diff1
      value: 9.608600000000001
    - type: nauc_mrr_at_1000_max
      value: -9.2699
    - type: nauc_mrr_at_1000_std
      value: -16.095100000000002
    - type: nauc_mrr_at_1000_diff1
      value: 9.6099
    - type: main_score
      value: 58.011
  - task:
      type: Clustering
    dataset:
      name: MTEB ArxivClusteringP2P (default)
      type: mteb/arxiv-clustering-p2p
      config: default
      split: test
      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
    metrics:
    - type: v_measure
      value: 44.684400000000004
    - type: v_measure_std
      value: 13.5064
    - type: main_score
      value: 44.684400000000004
  - task:
      type: Clustering
    dataset:
      name: MTEB ArxivClusteringS2S (default)
      type: mteb/arxiv-clustering-s2s
      config: default
      split: test
      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
    metrics:
    - type: v_measure
      value: 35.0503
    - type: v_measure_std
      value: 13.9543
    - type: main_score
      value: 35.0503
  - task:
      type: Reranking
    dataset:
      name: MTEB AskUbuntuDupQuestions (default)
      type: mteb/askubuntudupquestions-reranking
      config: default
      split: test
      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
    metrics:
    - type: map
      value: 60.648500000000006
    - type: mrr
      value: 74.528
    - type: nAUC_map_max
      value: 19.4239
    - type: nAUC_map_std
      value: 20.0729
    - type: nAUC_map_diff1
      value: 10.0382
    - type: nAUC_mrr_max
      value: 30.693199999999997
    - type: nAUC_mrr_std
      value: 27.1279
    - type: nAUC_mrr_diff1
      value: 23.0291
    - type: main_score
      value: 60.648500000000006
  - task:
      type: STS
    dataset:
      name: MTEB BIOSSES (default)
      type: mteb/biosses-sts
      config: default
      split: test
      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
    metrics:
    - type: pearson
      value: 89.5081
    - type: spearman
      value: 87.0568
    - type: cosine_pearson
      value: 89.5081
    - type: cosine_spearman
      value: 87.0568
    - type: manhattan_pearson
      value: 88.1247
    - type: manhattan_spearman
      value: 87.2556
    - type: euclidean_pearson
      value: 88.3266
    - type: euclidean_spearman
      value: 87.0568
    - type: main_score
      value: 87.0568
  - task:
      type: Classification
    dataset:
      name: MTEB Banking77Classification (default)
      type: mteb/banking77
      config: default
      split: test
      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
    metrics:
    - type: accuracy
      value: 80.18180000000001
    - type: f1
      value: 79.5538
    - type: f1_weighted
      value: 79.5538
    - type: main_score
      value: 80.18180000000001
  - task:
      type: Clustering
    dataset:
      name: MTEB BiorxivClusteringP2P (default)
      type: mteb/biorxiv-clustering-p2p
      config: default
      split: test
      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
    metrics:
    - type: v_measure
      value: 36.0126
    - type: v_measure_std
      value: 0.47019999999999995
    - type: main_score
      value: 36.0126
  - task:
      type: Clustering
    dataset:
      name: MTEB BiorxivClusteringS2S (default)
      type: mteb/biorxiv-clustering-s2s
      config: default
      split: test
      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
    metrics:
    - type: v_measure
      value: 28.6331
    - type: v_measure_std
      value: 0.8607999999999999
    - type: main_score
      value: 28.6331
  - task:
      type: Retrieval
    dataset:
      name: MTEB CQADupstackAndroidRetrieval (default)
      type: mteb/cqadupstack-android
      config: default
      split: test
      revision: f46a197baaae43b4f621051089b82a364682dfeb
    metrics:
    - type: ndcg_at_1
      value: 45.207
    - type: ndcg_at_3
      value: 51.31400000000001
    - type: ndcg_at_5
      value: 54.093999999999994
    - type: ndcg_at_10
      value: 56.31
    - type: ndcg_at_20
      value: 58.378
    - type: ndcg_at_100
      value: 61.307
    - type: ndcg_at_1000
      value: 62.724999999999994
    - type: map_at_1
      value: 37.732
    - type: map_at_3
      value: 46.263
    - type: map_at_5
      value: 48.553000000000004
    - type: map_at_10
      value: 49.984
    - type: map_at_20
      value: 50.888999999999996
    - type: map_at_100
      value: 51.568999999999996
    - type: map_at_1000
      value: 51.666999999999994
    - type: recall_at_1
      value: 37.732
    - type: recall_at_3
      value: 53.736
    - type: recall_at_5
      value: 60.95399999999999
    - type: recall_at_10
      value: 68.062
    - type: recall_at_20
      value: 75.149
    - type: recall_at_100
      value: 88.075
    - type: recall_at_1000
      value: 96.878
    - type: precision_at_1
      value: 45.207
    - type: precision_at_3
      value: 24.368000000000002
    - type: precision_at_5
      value: 17.854
    - type: precision_at_10
      value: 10.558
    - type: precision_at_20
      value: 6.23
    - type: precision_at_100
      value: 1.614
    - type: precision_at_1000
      value: 0.202
    - type: mrr_at_1
      value: 45.2074
    - type: mrr_at_3
      value: 52.9804
    - type: mrr_at_5
      value: 54.718599999999995
    - type: mrr_at_10
      value: 55.5713
    - type: mrr_at_20
      value: 55.94
    - type: mrr_at_100
      value: 56.21699999999999
    - type: mrr_at_1000
      value: 56.2504
    - type: nauc_ndcg_at_1_max
      value: 43.7697
    - type: nauc_ndcg_at_1_std
      value: -3.9530000000000003
    - type: nauc_ndcg_at_1_diff1
      value: 57.75320000000001
    - type: nauc_ndcg_at_3_max
      value: 42.7238
    - type: nauc_ndcg_at_3_std
      value: -3.5654
    - type: nauc_ndcg_at_3_diff1
      value: 53.552299999999995
    - type: nauc_ndcg_at_5_max
      value: 43.115500000000004
    - type: nauc_ndcg_at_5_std
      value: -2.1444
    - type: nauc_ndcg_at_5_diff1
      value: 53.130500000000005
    - type: nauc_ndcg_at_10_max
      value: 43.0188
    - type: nauc_ndcg_at_10_std
      value: -3.1515
    - type: nauc_ndcg_at_10_diff1
      value: 53.593199999999996
    - type: nauc_ndcg_at_20_max
      value: 43.4617
    - type: nauc_ndcg_at_20_std
      value: -2.9284
    - type: nauc_ndcg_at_20_diff1
      value: 53.28000000000001
    - type: nauc_ndcg_at_100_max
      value: 44.0704
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      value: -0.5772
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      value: 53.439899999999994
    - type: nauc_ndcg_at_1000_max
      value: 44.256099999999996
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      value: -1.1407
    - type: nauc_ndcg_at_1000_diff1
      value: 53.8728
    - type: nauc_map_at_1_max
      value: 36.613800000000005
    - type: nauc_map_at_1_std
      value: -5.8014
    - type: nauc_map_at_1_diff1
      value: 59.0186
    - type: nauc_map_at_3_max
      value: 40.8666
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      value: -4.886299999999999
    - type: nauc_map_at_3_diff1
      value: 55.324600000000004
    - type: nauc_map_at_5_max
      value: 41.9942
    - type: nauc_map_at_5_std
      value: -3.9361
    - type: nauc_map_at_5_diff1
      value: 54.8805
    - type: nauc_map_at_10_max
      value: 42.1621
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      value: -4.3264
    - type: nauc_map_at_10_diff1
      value: 55.0133
    - type: nauc_map_at_20_max
      value: 42.5837
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      value: -3.8526
    - type: nauc_map_at_20_diff1
      value: 54.895700000000005
    - type: nauc_map_at_100_max
      value: 42.7645
    - type: nauc_map_at_100_std
      value: -3.4568000000000003
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  - task:
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  - task:
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      type: CQADupstackRetrieval_is_a_combined_dataset
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      revision: CQADupstackRetrieval_is_a_combined_dataset
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  - task:
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      type: mteb/emotion
      config: default
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  - task:
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  - task:
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  - task:
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    dataset:
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      type: mteb/imdb
      config: default
      split: test
      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
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  - task:
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      type: mteb/msmarco
      config: default
      split: dev
      revision: c5a29a104738b98a9e76336939199e264163d4a0
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  - task:
      type: Classification
    dataset:
      name: MTEB MTOPDomainClassification (en)
      type: mteb/mtop_domain
      config: en
      split: test
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
    metrics:
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  - task:
      type: Classification
    dataset:
      name: MTEB MTOPIntentClassification (en)
      type: mteb/mtop_intent
      config: en
      split: test
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
    metrics:
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      value: 65.2668
    - type: f1
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    - type: f1_weighted
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      value: 65.2668
  - task:
      type: Classification
    dataset:
      name: MTEB MassiveIntentClassification (en)
      type: mteb/amazon_massive_intent
      config: en
      split: test
      revision: 4672e20407010da34463acc759c162ca9734bca6
    metrics:
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      value: 68.0128
    - type: f1
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    - type: f1_weighted
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  - task:
      type: Classification
    dataset:
      name: MTEB MassiveScenarioClassification (en)
      type: mteb/amazon_massive_scenario
      config: en
      split: test
      revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
    metrics:
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      value: 72.67320000000001
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    - type: f1_weighted
      value: 72.9092
    - type: main_score
      value: 72.67320000000001
  - task:
      type: Clustering
    dataset:
      name: MTEB MedrxivClusteringP2P (default)
      type: mteb/medrxiv-clustering-p2p
      config: default
      split: test
      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
    metrics:
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      value: 31.5764
    - type: v_measure_std
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    - type: main_score
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  - task:
      type: Clustering
    dataset:
      name: MTEB MedrxivClusteringS2S (default)
      type: mteb/medrxiv-clustering-s2s
      config: default
      split: test
      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
    metrics:
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      value: 28.006999999999998
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      value: 1.4235
    - type: main_score
      value: 28.006999999999998
  - task:
      type: Reranking
    dataset:
      name: MTEB MindSmallReranking (default)
      type: mteb/mind_small
      config: default
      split: test
      revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7
    metrics:
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    - type: mrr
      value: 31.168699999999998
    - type: nAUC_map_max
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    - type: nAUC_map_std
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    - type: nAUC_map_diff1
      value: 12.437199999999999
    - type: nAUC_mrr_max
      value: -19.5255
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      value: -6.1112
    - type: nAUC_mrr_diff1
      value: 12.1585
    - type: main_score
      value: 30.3039
  - task:
      type: Retrieval
    dataset:
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      type: mteb/nfcorpus
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  - task:
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  - task:
      type: Clustering
    dataset:
      name: MTEB RedditClustering (default)
      type: mteb/reddit-clustering
      config: default
      split: test
      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
    metrics:
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    - type: v_measure_std
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    - type: main_score
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  - task:
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    dataset:
      name: MTEB RedditClusteringP2P (default)
      type: mteb/reddit-clustering-p2p
      config: default
      split: test
      revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
    metrics:
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    - type: main_score
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  - task:
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    dataset:
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      type: mteb/scidocs
      config: default
      split: test
      revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
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    - type: map_at_1000
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    - type: recall_at_10
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    - type: recall_at_20
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    - type: recall_at_1000
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  - task:
      type: STS
    dataset:
      name: MTEB SICK-R (default)
      type: mteb/sickr-sts
      config: default
      split: test
      revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
    metrics:
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    - type: cosine_pearson
      value: 75.7747
    - type: cosine_spearman
      value: 71.3142
    - type: manhattan_pearson
      value: 73.8759
    - type: manhattan_spearman
      value: 71.1003
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      value: 74.088
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      value: 71.3142
    - type: main_score
      value: 71.3142
  - task:
      type: STS
    dataset:
      name: MTEB STS12 (default)
      type: mteb/sts12-sts
      config: default
      split: test
      revision: a0d554a64d88156834ff5ae9920b964011b16384
    metrics:
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    - type: spearman
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    - type: cosine_pearson
      value: 72.5903
    - type: cosine_spearman
      value: 70.6581
    - type: manhattan_pearson
      value: 69.2077
    - type: manhattan_spearman
      value: 70.4521
    - type: euclidean_pearson
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    - type: euclidean_spearman
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    - type: main_score
      value: 70.6581
  - task:
      type: STS
    dataset:
      name: MTEB STS13 (default)
      type: mteb/sts13-sts
      config: default
      split: test
      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
    metrics:
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      value: 73.1686
    - type: spearman
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    - type: cosine_pearson
      value: 73.1686
    - type: cosine_spearman
      value: 77.4225
    - type: manhattan_pearson
      value: 76.2481
    - type: manhattan_spearman
      value: 77.325
    - type: euclidean_pearson
      value: 76.3568
    - type: euclidean_spearman
      value: 77.4225
    - type: main_score
      value: 77.4225
  - task:
      type: STS
    dataset:
      name: MTEB STS14 (default)
      type: mteb/sts14-sts
      config: default
      split: test
      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
    metrics:
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      value: 74.46340000000001
    - type: spearman
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    - type: cosine_pearson
      value: 74.46340000000001
    - type: cosine_spearman
      value: 72.9162
    - type: manhattan_pearson
      value: 73.8079
    - type: manhattan_spearman
      value: 72.8704
    - type: euclidean_pearson
      value: 73.8244
    - type: euclidean_spearman
      value: 72.9162
    - type: main_score
      value: 72.9162
  - task:
      type: STS
    dataset:
      name: MTEB STS15 (default)
      type: mteb/sts15-sts
      config: default
      split: test
      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
    metrics:
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      value: 80.1161
    - type: spearman
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    - type: cosine_pearson
      value: 80.1161
    - type: cosine_spearman
      value: 81.83200000000001
    - type: manhattan_pearson
      value: 81.573
    - type: manhattan_spearman
      value: 81.807
    - type: euclidean_pearson
      value: 81.59490000000001
    - type: euclidean_spearman
      value: 81.83200000000001
    - type: main_score
      value: 81.83200000000001
  - task:
      type: STS
    dataset:
      name: MTEB STS16 (default)
      type: mteb/sts16-sts
      config: default
      split: test
      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
    metrics:
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      value: 78.8244
    - type: spearman
      value: 81.2262
    - type: cosine_pearson
      value: 78.8244
    - type: cosine_spearman
      value: 81.2262
    - type: manhattan_pearson
      value: 80.6177
    - type: manhattan_spearman
      value: 81.1361
    - type: euclidean_pearson
      value: 80.7347
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      value: 81.2262
    - type: main_score
      value: 81.2262
  - task:
      type: STS
    dataset:
      name: MTEB STS17 (es-en)
      type: mteb/sts17-crosslingual-sts
      config: es-en
      split: test
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
    metrics:
    - type: pearson
      value: 67.9751
    - type: spearman
      value: 68.92099999999999
    - type: cosine_pearson
      value: 67.9751
    - type: cosine_spearman
      value: 68.92099999999999
    - type: manhattan_pearson
      value: 68.9355
    - type: manhattan_spearman
      value: 68.777
    - type: euclidean_pearson
      value: 69.11410000000001
    - type: euclidean_spearman
      value: 68.92099999999999
    - type: main_score
      value: 68.92099999999999
  - task:
      type: STS
    dataset:
      name: MTEB STS17 (fr-en)
      type: mteb/sts17-crosslingual-sts
      config: fr-en
      split: test
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
    metrics:
    - type: pearson
      value: 72.08449999999999
    - type: spearman
      value: 74.6931
    - type: cosine_pearson
      value: 72.08449999999999
    - type: cosine_spearman
      value: 74.6931
    - type: manhattan_pearson
      value: 73.52
    - type: manhattan_spearman
      value: 74.7097
    - type: euclidean_pearson
      value: 73.62180000000001
    - type: euclidean_spearman
      value: 74.6931
    - type: main_score
      value: 74.6931
  - task:
      type: STS
    dataset:
      name: MTEB STS17 (en-en)
      type: mteb/sts17-crosslingual-sts
      config: en-en
      split: test
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
    metrics:
    - type: pearson
      value: 80.528
    - type: spearman
      value: 84.10459999999999
    - type: cosine_pearson
      value: 80.528
    - type: cosine_spearman
      value: 84.10459999999999
    - type: manhattan_pearson
      value: 83.1537
    - type: manhattan_spearman
      value: 84.0952
    - type: euclidean_pearson
      value: 83.337
    - type: euclidean_spearman
      value: 84.10459999999999
    - type: main_score
      value: 84.10459999999999
  - task:
      type: STS
    dataset:
      name: MTEB STS17 (en-tr)
      type: mteb/sts17-crosslingual-sts
      config: en-tr
      split: test
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
    metrics:
    - type: pearson
      value: 49.641400000000004
    - type: spearman
      value: 48.9413
    - type: cosine_pearson
      value: 49.641400000000004
    - type: cosine_spearman
      value: 48.9413
    - type: manhattan_pearson
      value: 51.434000000000005
    - type: manhattan_spearman
      value: 49.1595
    - type: euclidean_pearson
      value: 50.867799999999995
    - type: euclidean_spearman
      value: 48.9413
    - type: main_score
      value: 48.9413
  - task:
      type: STS
    dataset:
      name: MTEB STS17 (it-en)
      type: mteb/sts17-crosslingual-sts
      config: it-en
      split: test
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
    metrics:
    - type: pearson
      value: 71.2577
    - type: spearman
      value: 73.82419999999999
    - type: cosine_pearson
      value: 71.2577
    - type: cosine_spearman
      value: 73.82419999999999
    - type: manhattan_pearson
      value: 71.9329
    - type: manhattan_spearman
      value: 73.4651
    - type: euclidean_pearson
      value: 72.2771
    - type: euclidean_spearman
      value: 73.82419999999999
    - type: main_score
      value: 73.82419999999999
  - task:
      type: STS
    dataset:
      name: MTEB STS17 (nl-en)
      type: mteb/sts17-crosslingual-sts
      config: nl-en
      split: test
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
    metrics:
    - type: pearson
      value: 64.1562
    - type: spearman
      value: 64.8766
    - type: cosine_pearson
      value: 64.1562
    - type: cosine_spearman
      value: 64.8766
    - type: manhattan_pearson
      value: 64.16579999999999
    - type: manhattan_spearman
      value: 64.1931
    - type: euclidean_pearson
      value: 64.6169
    - type: euclidean_spearman
      value: 64.8766
    - type: main_score
      value: 64.8766
  - task:
      type: STS
    dataset:
      name: MTEB STS17 (en-ar)
      type: mteb/sts17-crosslingual-sts
      config: en-ar
      split: test
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
    metrics:
    - type: pearson
      value: 42.257400000000004
    - type: spearman
      value: 43.2176
    - type: cosine_pearson
      value: 42.257400000000004
    - type: cosine_spearman
      value: 43.2176
    - type: manhattan_pearson
      value: 43.5359
    - type: manhattan_spearman
      value: 42.4143
    - type: euclidean_pearson
      value: 43.6717
    - type: euclidean_spearman
      value: 43.2176
    - type: main_score
      value: 43.2176
  - task:
      type: STS
    dataset:
      name: MTEB STS17 (en-de)
      type: mteb/sts17-crosslingual-sts
      config: en-de
      split: test
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
    metrics:
    - type: pearson
      value: 74.0088
    - type: spearman
      value: 75.8687
    - type: cosine_pearson
      value: 74.0088
    - type: cosine_spearman
      value: 75.8687
    - type: manhattan_pearson
      value: 74.8505
    - type: manhattan_spearman
      value: 75.6101
    - type: euclidean_pearson
      value: 75.1303
    - type: euclidean_spearman
      value: 75.8687
    - type: main_score
      value: 75.8687
  - task:
      type: STS
    dataset:
      name: MTEB STS22 (zh-en)
      type: mteb/sts22-crosslingual-sts
      config: zh-en
      split: test
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
    metrics:
    - type: pearson
      value: 68.0842
    - type: spearman
      value: 69.4346
    - type: cosine_pearson
      value: 68.0842
    - type: cosine_spearman
      value: 69.4346
    - type: manhattan_pearson
      value: 69.9982
    - type: manhattan_spearman
      value: 69.8952
    - type: euclidean_pearson
      value: 69.6375
    - type: euclidean_spearman
      value: 69.4346
    - type: main_score
      value: 69.4346
  - task:
      type: STS
    dataset:
      name: MTEB STS22 (es-en)
      type: mteb/sts22-crosslingual-sts
      config: es-en
      split: test
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
    metrics:
    - type: pearson
      value: 76.3695
    - type: spearman
      value: 78.88730000000001
    - type: cosine_pearson
      value: 76.3695
    - type: cosine_spearman
      value: 78.88730000000001
    - type: manhattan_pearson
      value: 79.0721
    - type: manhattan_spearman
      value: 79.1151
    - type: euclidean_pearson
      value: 78.783
    - type: euclidean_spearman
      value: 78.88730000000001
    - type: main_score
      value: 78.88730000000001
  - task:
      type: STS
    dataset:
      name: MTEB STS22 (de-en)
      type: mteb/sts22-crosslingual-sts
      config: de-en
      split: test
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
    metrics:
    - type: pearson
      value: 60.59139999999999
    - type: spearman
      value: 52.692099999999996
    - type: cosine_pearson
      value: 60.59139999999999
    - type: cosine_spearman
      value: 52.692099999999996
    - type: manhattan_pearson
      value: 64.66499999999999
    - type: manhattan_spearman
      value: 53.09009999999999
    - type: euclidean_pearson
      value: 64.5541
    - type: euclidean_spearman
      value: 52.692099999999996
    - type: main_score
      value: 52.692099999999996
  - task:
      type: STS
    dataset:
      name: MTEB STS22 (pl-en)
      type: mteb/sts22-crosslingual-sts
      config: pl-en
      split: test
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
    metrics:
    - type: pearson
      value: 77.8405
    - type: spearman
      value: 76.6188
    - type: cosine_pearson
      value: 77.8405
    - type: cosine_spearman
      value: 76.6188
    - type: manhattan_pearson
      value: 76.6598
    - type: manhattan_spearman
      value: 76.3583
    - type: euclidean_pearson
      value: 77.1442
    - type: euclidean_spearman
      value: 76.6188
    - type: main_score
      value: 76.6188
  - task:
      type: STS
    dataset:
      name: MTEB STS22 (en)
      type: mteb/sts22-crosslingual-sts
      config: en
      split: test
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
    metrics:
    - type: pearson
      value: 69.8017
    - type: spearman
      value: 68.7734
    - type: cosine_pearson
      value: 69.8017
    - type: cosine_spearman
      value: 68.7734
    - type: manhattan_pearson
      value: 70.6884
    - type: manhattan_spearman
      value: 68.2974
    - type: euclidean_pearson
      value: 70.7968
    - type: euclidean_spearman
      value: 68.7734
    - type: main_score
      value: 68.7734
  - task:
      type: STS
    dataset:
      name: MTEB STSBenchmark (default)
      type: mteb/stsbenchmark-sts
      config: default
      split: test
      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
    metrics:
    - type: pearson
      value: 73.3293
    - type: spearman
      value: 76.00919999999999
    - type: cosine_pearson
      value: 73.3293
    - type: cosine_spearman
      value: 76.00919999999999
    - type: manhattan_pearson
      value: 75.0184
    - type: manhattan_spearman
      value: 75.8014
    - type: euclidean_pearson
      value: 75.2638
    - type: euclidean_spearman
      value: 76.00919999999999
    - type: main_score
      value: 76.00919999999999
  - task:
      type: Reranking
    dataset:
      name: MTEB SciDocsRR (default)
      type: mteb/scidocs-reranking
      config: default
      split: test
      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
    metrics:
    - type: map
      value: 77.3669
    - type: mrr
      value: 93.5985
    - type: nAUC_map_max
      value: 50.2355
    - type: nAUC_map_std
      value: 65.5401
    - type: nAUC_map_diff1
      value: 9.6333
    - type: nAUC_mrr_max
      value: 76.5201
    - type: nAUC_mrr_std
      value: 74.7401
    - type: nAUC_mrr_diff1
      value: 53.170899999999996
    - type: main_score
      value: 77.3669
  - task:
      type: Retrieval
    dataset:
      name: MTEB SciFact (default)
      type: mteb/scifact
      config: default
      split: test
      revision: 0228b52cf27578f30900b9e5271d331663a030d7
    metrics:
    - type: ndcg_at_1
      value: 61
    - type: ndcg_at_3
      value: 67.589
    - type: ndcg_at_5
      value: 68.948
    - type: ndcg_at_10
      value: 71.8
    - type: ndcg_at_20
      value: 72.595
    - type: ndcg_at_100
      value: 74.138
    - type: ndcg_at_1000
      value: 74.83800000000001
    - type: map_at_1
      value: 57.74399999999999
    - type: map_at_3
      value: 64.866
    - type: map_at_5
      value: 66.018
    - type: map_at_10
      value: 67.535
    - type: map_at_20
      value: 67.77
    - type: map_at_100
      value: 68.011
    - type: map_at_1000
      value: 68.042
    - type: recall_at_1
      value: 57.74399999999999
    - type: recall_at_3
      value: 71.906
    - type: recall_at_5
      value: 75.344
    - type: recall_at_10
      value: 83.2
    - type: recall_at_20
      value: 86.26700000000001
    - type: recall_at_100
      value: 94.333
    - type: recall_at_1000
      value: 99.667
    - type: precision_at_1
      value: 61
    - type: precision_at_3
      value: 26.111
    - type: precision_at_5
      value: 16.8
    - type: precision_at_10
      value: 9.5
    - type: precision_at_20
      value: 4.933
    - type: precision_at_100
      value: 1.073
    - type: precision_at_1000
      value: 0.11299999999999999
    - type: mrr_at_1
      value: 61
    - type: mrr_at_3
      value: 67.4444
    - type: mrr_at_5
      value: 68.0778
    - type: mrr_at_10
      value: 69.0483
    - type: mrr_at_20
      value: 69.2333
    - type: mrr_at_100
      value: 69.4403
    - type: mrr_at_1000
      value: 69.4708
    - type: nauc_ndcg_at_1_max
      value: 53.481500000000004
    - type: nauc_ndcg_at_1_std
      value: 8.227
    - type: nauc_ndcg_at_1_diff1
      value: 72.0771
    - type: nauc_ndcg_at_3_max
      value: 57.0147
    - type: nauc_ndcg_at_3_std
      value: 5.2435
    - type: nauc_ndcg_at_3_diff1
      value: 68.8841
    - type: nauc_ndcg_at_5_max
      value: 57.4675
    - type: nauc_ndcg_at_5_std
      value: 8.4709
    - type: nauc_ndcg_at_5_diff1
      value: 67.2977
    - type: nauc_ndcg_at_10_max
      value: 60.3957
    - type: nauc_ndcg_at_10_std
      value: 11.3174
    - type: nauc_ndcg_at_10_diff1
      value: 67.8332
    - type: nauc_ndcg_at_20_max
      value: 60.3607
    - type: nauc_ndcg_at_20_std
      value: 11.9948
    - type: nauc_ndcg_at_20_diff1
      value: 68.1122
    - type: nauc_ndcg_at_100_max
      value: 59.5293
    - type: nauc_ndcg_at_100_std
      value: 11.697799999999999
    - type: nauc_ndcg_at_100_diff1
      value: 68.453
    - type: nauc_ndcg_at_1000_max
      value: 58.8931
    - type: nauc_ndcg_at_1000_std
      value: 10.876199999999999
    - type: nauc_ndcg_at_1000_diff1
      value: 68.5746
    - type: nauc_map_at_1_max
      value: 49.762299999999996
    - type: nauc_map_at_1_std
      value: -0.2785
    - type: nauc_map_at_1_diff1
      value: 71.9072
    - type: nauc_map_at_3_max
      value: 54.108599999999996
    - type: nauc_map_at_3_std
      value: 2.0995
    - type: nauc_map_at_3_diff1
      value: 69.3459
    - type: nauc_map_at_5_max
      value: 55.257
    - type: nauc_map_at_5_std
      value: 5.5776
    - type: nauc_map_at_5_diff1
      value: 68.3314
    - type: nauc_map_at_10_max
      value: 57.1506
    - type: nauc_map_at_10_std
      value: 7.4561
    - type: nauc_map_at_10_diff1
      value: 68.8482
    - type: nauc_map_at_20_max
      value: 57.126200000000004
    - type: nauc_map_at_20_std
      value: 7.6833
    - type: nauc_map_at_20_diff1
      value: 68.9132
    - type: nauc_map_at_100_max
      value: 56.9874
    - type: nauc_map_at_100_std
      value: 7.7405
    - type: nauc_map_at_100_diff1
      value: 68.9371
    - type: nauc_map_at_1000_max
      value: 56.959199999999996
    - type: nauc_map_at_1000_std
      value: 7.709499999999999
    - type: nauc_map_at_1000_diff1
      value: 68.9444
    - type: nauc_recall_at_1_max
      value: 49.762299999999996
    - type: nauc_recall_at_1_std
      value: -0.2785
    - type: nauc_recall_at_1_diff1
      value: 71.9072
    - type: nauc_recall_at_3_max
      value: 58.22580000000001
    - type: nauc_recall_at_3_std
      value: 2.3135
    - type: nauc_recall_at_3_diff1
      value: 65.5868
    - type: nauc_recall_at_5_max
      value: 60.4096
    - type: nauc_recall_at_5_std
      value: 11.7662
    - type: nauc_recall_at_5_diff1
      value: 61.5815
    - type: nauc_recall_at_10_max
      value: 72.74629999999999
    - type: nauc_recall_at_10_std
      value: 22.148
    - type: nauc_recall_at_10_diff1
      value: 62.2401
    - type: nauc_recall_at_20_max
      value: 74.9625
    - type: nauc_recall_at_20_std
      value: 28.1358
    - type: nauc_recall_at_20_diff1
      value: 63.240700000000004
    - type: nauc_recall_at_100_max
      value: 79.15910000000001
    - type: nauc_recall_at_100_std
      value: 39.4162
    - type: nauc_recall_at_100_diff1
      value: 65.733
    - type: nauc_recall_at_1000_max
      value: 100
    - type: nauc_recall_at_1000_std
      value: 72.2222
    - type: nauc_recall_at_1000_diff1
      value: 72.2222
    - type: nauc_precision_at_1_max
      value: 53.481500000000004
    - type: nauc_precision_at_1_std
      value: 8.227
    - type: nauc_precision_at_1_diff1
      value: 72.0771
    - type: nauc_precision_at_3_max
      value: 55.675799999999995
    - type: nauc_precision_at_3_std
      value: 23.9615
    - type: nauc_precision_at_3_diff1
      value: 48.1199
    - type: nauc_precision_at_5_max
      value: 50.503299999999996
    - type: nauc_precision_at_5_std
      value: 36.9259
    - type: nauc_precision_at_5_diff1
      value: 31.769399999999997
    - type: nauc_precision_at_10_max
      value: 45.4878
    - type: nauc_precision_at_10_std
      value: 44.0469
    - type: nauc_precision_at_10_diff1
      value: 16.666900000000002
    - type: nauc_precision_at_20_max
      value: 40.2908
    - type: nauc_precision_at_20_std
      value: 47.330600000000004
    - type: nauc_precision_at_20_diff1
      value: 11.0043
    - type: nauc_precision_at_100_max
      value: 27.4643
    - type: nauc_precision_at_100_std
      value: 53.0014
    - type: nauc_precision_at_100_diff1
      value: -4.8238
    - type: nauc_precision_at_1000_max
      value: 15.755099999999999
    - type: nauc_precision_at_1000_std
      value: 56.634499999999996
    - type: nauc_precision_at_1000_diff1
      value: -21.124100000000002
    - type: nauc_mrr_at_1_max
      value: 53.481500000000004
    - type: nauc_mrr_at_1_std
      value: 8.227
    - type: nauc_mrr_at_1_diff1
      value: 72.0771
    - type: nauc_mrr_at_3_max
      value: 57.6662
    - type: nauc_mrr_at_3_std
      value: 9.2816
    - type: nauc_mrr_at_3_diff1
      value: 69.8276
    - type: nauc_mrr_at_5_max
      value: 57.6565
    - type: nauc_mrr_at_5_std
      value: 10.422099999999999
    - type: nauc_mrr_at_5_diff1
      value: 69.0964
    - type: nauc_mrr_at_10_max
      value: 58.000099999999996
    - type: nauc_mrr_at_10_std
      value: 10.957600000000001
    - type: nauc_mrr_at_10_diff1
      value: 69.0098
    - type: nauc_mrr_at_20_max
      value: 58.0066
    - type: nauc_mrr_at_20_std
      value: 11.0139
    - type: nauc_mrr_at_20_diff1
      value: 69.1278
    - type: nauc_mrr_at_100_max
      value: 57.9072
    - type: nauc_mrr_at_100_std
      value: 10.9621
    - type: nauc_mrr_at_100_diff1
      value: 69.1925
    - type: nauc_mrr_at_1000_max
      value: 57.87949999999999
    - type: nauc_mrr_at_1000_std
      value: 10.934199999999999
    - type: nauc_mrr_at_1000_diff1
      value: 69.2004
    - type: main_score
      value: 71.8
  - task:
      type: PairClassification
    dataset:
      name: MTEB SprintDuplicateQuestions (default)
      type: mteb/sprintduplicatequestions-pairclassification
      config: default
      split: test
      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
    metrics:
    - type: similarity_accuracy
      value: 99.8248
    - type: similarity_accuracy_threshold
      value: 74.6155
    - type: similarity_f1
      value: 91.12780000000001
    - type: similarity_f1_threshold
      value: 74.2422
    - type: similarity_precision
      value: 91.3568
    - type: similarity_recall
      value: 90.9
    - type: similarity_ap
      value: 96.00319999999999
    - type: cosine_accuracy
      value: 99.8248
    - type: cosine_accuracy_threshold
      value: 74.6155
    - type: cosine_f1
      value: 91.12780000000001
    - type: cosine_f1_threshold
      value: 74.2422
    - type: cosine_precision
      value: 91.3568
    - type: cosine_recall
      value: 90.9
    - type: cosine_ap
      value: 96.00319999999999
    - type: manhattan_accuracy
      value: 99.8257
    - type: manhattan_accuracy_threshold
      value: 1574.1653
    - type: manhattan_f1
      value: 91.1531
    - type: manhattan_f1_threshold
      value: 1595.7924
    - type: manhattan_precision
      value: 90.6126
    - type: manhattan_recall
      value: 91.7
    - type: manhattan_ap
      value: 95.9848
    - type: euclidean_accuracy
      value: 99.8248
    - type: euclidean_accuracy_threshold
      value: 71.2523
    - type: euclidean_f1
      value: 91.12780000000001
    - type: euclidean_f1_threshold
      value: 71.7744
    - type: euclidean_precision
      value: 91.3568
    - type: euclidean_recall
      value: 90.9
    - type: euclidean_ap
      value: 96.00319999999999
    - type: dot_accuracy
      value: 99.8248
    - type: dot_accuracy_threshold
      value: 74.6155
    - type: dot_f1
      value: 91.12780000000001
    - type: dot_f1_threshold
      value: 74.2422
    - type: dot_precision
      value: 91.3568
    - type: dot_recall
      value: 90.9
    - type: dot_ap
      value: 96.00319999999999
    - type: max_accuracy
      value: 99.8257
    - type: max_f1
      value: 91.1531
    - type: max_precision
      value: 91.3568
    - type: max_recall
      value: 91.7
    - type: max_ap
      value: 96.00319999999999
    - type: main_score
      value: 96.00319999999999
  - task:
      type: Clustering
    dataset:
      name: MTEB StackExchangeClustering (default)
      type: mteb/stackexchange-clustering
      config: default
      split: test
      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
    metrics:
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      value: 61.3985
    - type: v_measure_std
      value: 5.2151000000000005
    - type: main_score
      value: 61.3985
  - task:
      type: Clustering
    dataset:
      name: MTEB StackExchangeClusteringP2P (default)
      type: mteb/stackexchange-clustering-p2p
      config: default
      split: test
      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
    metrics:
    - type: v_measure
      value: 36.1433
    - type: v_measure_std
      value: 1.5853
    - type: main_score
      value: 36.1433
  - task:
      type: Reranking
    dataset:
      name: MTEB StackOverflowDupQuestions (default)
      type: mteb/stackoverflowdupquestions-reranking
      config: default
      split: test
      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
    metrics:
    - type: map
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    - type: mrr
      value: 51.221399999999996
    - type: nAUC_map_max
      value: 10.1311
    - type: nAUC_map_std
      value: 6.239999999999999
    - type: nAUC_map_diff1
      value: 36.3486
    - type: nAUC_mrr_max
      value: 10.9306
    - type: nAUC_mrr_std
      value: 6.7909
    - type: nAUC_mrr_diff1
      value: 36.5536
    - type: main_score
      value: 50.47580000000001
  - task:
      type: Summarization
    dataset:
      name: MTEB SummEval (default)
      type: mteb/summeval
      config: default
      split: test
      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
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    - type: spearman
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    - type: cosine_spearman
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    - type: cosine_pearson
      value: 29.8474
    - type: dot_spearman
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    - type: dot_pearson
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  - task:
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    dataset:
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      type: mteb/trec-covid
      config: default
      split: test
      revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
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      value: 83.573
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      value: 80.285
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      value: 1.078
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      value: 1.151
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      value: 20.0578
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      value: 49.8621
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    - type: nauc_precision_at_1000_std
      value: 53.815900000000006
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    - type: nauc_mrr_at_1_max
      value: -7.077500000000001
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      value: -2.1212999999999997
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      value: 21.9859
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      value: 25.0584
    - type: nauc_mrr_at_5_max
      value: -2.1212999999999997
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      value: 25.0584
    - type: nauc_mrr_at_10_max
      value: -2.1212999999999997
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    - type: nauc_mrr_at_20_max
      value: -2.1212999999999997
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      value: 21.9859
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    - type: main_score
      value: 80.285
  - task:
      type: Retrieval
    dataset:
      name: MTEB Touche2020 (default)
      type: mteb/touche2020
      config: default
      split: test
      revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
    metrics:
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      value: 33.672999999999995
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      value: 34.392
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      value: 32.606
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      value: 41.094
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    - type: map_at_1
      value: 2.64
    - type: map_at_3
      value: 6.428000000000001
    - type: map_at_5
      value: 8.792
    - type: map_at_10
      value: 11.882
    - type: map_at_20
      value: 14.818000000000001
    - type: map_at_100
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      value: 20.233
    - type: recall_at_1
      value: 2.64
    - type: recall_at_3
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    - type: recall_at_5
      value: 11.898
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      value: 18.782
    - type: recall_at_20
      value: 27.488
    - type: recall_at_100
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    - type: recall_at_1000
      value: 84.399
    - type: precision_at_1
      value: 36.735
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      value: 36.735
    - type: precision_at_5
      value: 33.061
    - type: precision_at_10
      value: 26.122
    - type: precision_at_20
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    - type: precision_at_100
      value: 8.429
    - type: precision_at_1000
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      value: 36.7347
    - type: mrr_at_3
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    - type: mrr_at_10
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    - type: mrr_at_20
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    - type: nauc_ndcg_at_1_max
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    - type: nauc_mrr_at_3_diff1
      value: 20.288
    - type: nauc_mrr_at_5_max
      value: -33.218599999999995
    - type: nauc_mrr_at_5_std
      value: -23.857400000000002
    - type: nauc_mrr_at_5_diff1
      value: 15.978200000000001
    - type: nauc_mrr_at_10_max
      value: -31.7904
    - type: nauc_mrr_at_10_std
      value: -19.169900000000002
    - type: nauc_mrr_at_10_diff1
      value: 17.762700000000002
    - type: nauc_mrr_at_20_max
      value: -30.44
    - type: nauc_mrr_at_20_std
      value: -20.2867
    - type: nauc_mrr_at_20_diff1
      value: 18.895500000000002
    - type: nauc_mrr_at_100_max
      value: -30.5404
    - type: nauc_mrr_at_100_std
      value: -20.5699
    - type: nauc_mrr_at_100_diff1
      value: 18.7046
    - type: nauc_mrr_at_1000_max
      value: -30.5404
    - type: nauc_mrr_at_1000_std
      value: -20.5699
    - type: nauc_mrr_at_1000_diff1
      value: 18.7046
    - type: main_score
      value: 29.767
  - task:
      type: Classification
    dataset:
      name: MTEB ToxicConversationsClassification (default)
      type: mteb/toxic_conversations_50k
      config: default
      split: test
      revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
    metrics:
    - type: accuracy
      value: 64.8096
    - type: f1
      value: 49.844300000000004
    - type: f1_weighted
      value: 72.5251
    - type: ap
      value: 11.7519
    - type: ap_weighted
      value: 11.7519
    - type: main_score
      value: 64.8096
  - task:
      type: Classification
    dataset:
      name: MTEB TweetSentimentExtractionClassification (default)
      type: mteb/tweet_sentiment_extraction
      config: default
      split: test
      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
    metrics:
    - type: accuracy
      value: 58.1692
    - type: f1
      value: 58.4408
    - type: f1_weighted
      value: 57.565599999999996
    - type: main_score
      value: 58.1692
  - task:
      type: Clustering
    dataset:
      name: MTEB TwentyNewsgroupsClustering (default)
      type: mteb/twentynewsgroups-clustering
      config: default
      split: test
      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
    metrics:
    - type: v_measure
      value: 39.293
    - type: v_measure_std
      value: 1.5684
    - type: main_score
      value: 39.293
  - task:
      type: PairClassification
    dataset:
      name: MTEB TwitterSemEval2015 (default)
      type: mteb/twittersemeval2015-pairclassification
      config: default
      split: test
      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
    metrics:
    - type: similarity_accuracy
      value: 83.29260000000001
    - type: similarity_accuracy_threshold
      value: 78.2732
    - type: similarity_f1
      value: 60.656600000000005
    - type: similarity_f1_threshold
      value: 73.4961
    - type: similarity_precision
      value: 59.007
    - type: similarity_recall
      value: 62.4011
    - type: similarity_ap
      value: 64.7501
    - type: cosine_accuracy
      value: 83.29260000000001
    - type: cosine_accuracy_threshold
      value: 78.2732
    - type: cosine_f1
      value: 60.656600000000005
    - type: cosine_f1_threshold
      value: 73.4961
    - type: cosine_precision
      value: 59.007
    - type: cosine_recall
      value: 62.4011
    - type: cosine_ap
      value: 64.7501
    - type: manhattan_accuracy
      value: 83.2986
    - type: manhattan_accuracy_threshold
      value: 1476.7148
    - type: manhattan_f1
      value: 60.7459
    - type: manhattan_f1_threshold
      value: 1607.9180000000001
    - type: manhattan_precision
      value: 59.0581
    - type: manhattan_recall
      value: 62.53300000000001
    - type: manhattan_ap
      value: 64.76859999999999
    - type: euclidean_accuracy
      value: 83.29260000000001
    - type: euclidean_accuracy_threshold
      value: 65.9194
    - type: euclidean_f1
      value: 60.656600000000005
    - type: euclidean_f1_threshold
      value: 72.8065
    - type: euclidean_precision
      value: 59.007
    - type: euclidean_recall
      value: 62.4011
    - type: euclidean_ap
      value: 64.7501
    - type: dot_accuracy
      value: 83.29260000000001
    - type: dot_accuracy_threshold
      value: 78.2731
    - type: dot_f1
      value: 60.656600000000005
    - type: dot_f1_threshold
      value: 73.4961
    - type: dot_precision
      value: 59.007
    - type: dot_recall
      value: 62.4011
    - type: dot_ap
      value: 64.7501
    - type: max_accuracy
      value: 83.2986
    - type: max_f1
      value: 60.7459
    - type: max_precision
      value: 59.0581
    - type: max_recall
      value: 62.53300000000001
    - type: max_ap
      value: 64.76859999999999
    - type: main_score
      value: 64.76859999999999
  - task:
      type: PairClassification
    dataset:
      name: MTEB TwitterURLCorpus (default)
      type: mteb/twitterurlcorpus-pairclassification
      config: default
      split: test
      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
    metrics:
    - type: similarity_accuracy
      value: 89.0247
    - type: similarity_accuracy_threshold
      value: 69.271
    - type: similarity_f1
      value: 78.24419999999999
    - type: similarity_f1_threshold
      value: 66.2183
    - type: similarity_precision
      value: 76.616
    - type: similarity_recall
      value: 79.943
    - type: similarity_ap
      value: 85.9494
    - type: cosine_accuracy
      value: 89.0247
    - type: cosine_accuracy_threshold
      value: 69.271
    - type: cosine_f1
      value: 78.24419999999999
    - type: cosine_f1_threshold
      value: 66.2183
    - type: cosine_precision
      value: 76.616
    - type: cosine_recall
      value: 79.943
    - type: cosine_ap
      value: 85.9494
    - type: manhattan_accuracy
      value: 89.0267
    - type: manhattan_accuracy_threshold
      value: 1750.3544000000002
    - type: manhattan_f1
      value: 78.2188
    - type: manhattan_f1_threshold
      value: 1837.7304
    - type: manhattan_precision
      value: 75.1472
    - type: manhattan_recall
      value: 81.5522
    - type: manhattan_ap
      value: 85.9496
    - type: euclidean_accuracy
      value: 89.0247
    - type: euclidean_accuracy_threshold
      value: 78.3951
    - type: euclidean_f1
      value: 78.24419999999999
    - type: euclidean_f1_threshold
      value: 82.197
    - type: euclidean_precision
      value: 76.616
    - type: euclidean_recall
      value: 79.943
    - type: euclidean_ap
      value: 85.9494
    - type: dot_accuracy
      value: 89.0247
    - type: dot_accuracy_threshold
      value: 69.271
    - type: dot_f1
      value: 78.24419999999999
    - type: dot_f1_threshold
      value: 66.2183
    - type: dot_precision
      value: 76.616
    - type: dot_recall
      value: 79.943
    - type: dot_ap
      value: 85.9494
    - type: max_accuracy
      value: 89.0267
    - type: max_f1
      value: 78.24419999999999
    - type: max_precision
      value: 76.616
    - type: max_recall
      value: 81.5522
    - type: max_ap
      value: 85.9496
    - type: main_score
      value: 85.9496
---

 
***See Disclaimer below***

----



# A Teradata Vantage compatible Embeddings Model

# Snowflake/snowflake-arctic-embed-m-v2.0

## Overview of this Model

An Embedding Model which maps text (sentence/ paragraphs) into a vector.  The [Snowflake/snowflake-arctic-embed-m-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v2.0) model is designed to generate text embeddings.

- 305.37M params (Sizes in ONNX format - "fp32": 1169.3MB, "int8": 296.51MB, "uint8": 296.51MB)
- 8192 maximum input tokens 
- 768 dimensions of output vector
- Licence: apache-2.0. The released models can be used for commercial purposes free of charge.
- Reference to Original Model: https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v2.0


## 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 = "snowflake-arctic-embed-m-v2.0"
number_dimensions_output = 768
model_file_name = "model.onnx"

# Step 1: Download Model from Teradata HuggingFace Page

hf_hub_download(repo_id=f"Teradata/{model_name}", filename=f"onnx/{model_file_name}", local_dir="./")
hf_hub_download(repo_id=f"Teradata/{model_name}", filename=f"tokenizer.json", local_dir="./")

# Step 2: Create Connection to Vantage

tdml.create_context(host = input('enter your hostname'), 
                    username=input('enter your username'), 
                    password = getpass.getpass("enter your password"))

# Step 3: Load Models into Vantage
# a) Embedding model
tdml.save_byom(model_id = model_name, # must be unique in the models table
               model_file = f"onnx/{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 [Snowflake/snowflake-arctic-embed-m-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v2.0) 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

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("Snowflake/snowflake-arctic-embed-m-v2.0")
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)



-----

DISCLAIMER: The content herein (“Content”) is provided “AS IS” and is not covered by any Teradata Operations, Inc. and its affiliates (“Teradata”) agreements. Its listing here does not constitute certification or endorsement by Teradata. 

To the extent any of the Content contains or is related to any artificial intelligence (“AI”) or other language learning models (“Models”) that interoperate with the products and services of Teradata, by accessing, bringing, deploying or using such Models, you acknowledge and agree that you are solely responsible for ensuring compliance with all applicable laws, regulations, and restrictions governing the use, deployment, and distribution of AI technologies. This includes, but is not limited to, AI Diffusion Rules, European Union AI Act, AI-related laws and regulations, privacy laws, export controls, and financial or sector-specific regulations.

While Teradata may provide support, guidance, or assistance in the deployment or implementation of Models to interoperate with Teradata’s products and/or services, you remain fully responsible for ensuring that your Models, data, and applications comply with all relevant legal and regulatory obligations. Our assistance does not constitute legal or regulatory approval, and Teradata disclaims any liability arising from non-compliance with applicable laws.

You must determine the suitability of the Models for any purpose. Given the probabilistic nature of machine learning and modeling, the use of the Models may in some situations result in incorrect output that does not accurately reflect the action generated. You should evaluate the accuracy of any output as appropriate for your use case, including by using human review of the output.
