README.md

metadata

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
model-index:
  - name: snowflake-arctic-embed-l-v2.0
    results:
      - dataset:
          config: en-ext
          name: MTEB AmazonCounterfactualClassification (en-ext)
          revision: e8379541af4e31359cca9fbcf4b00f2671dba205
          split: test
          type: mteb/amazon_counterfactual
        metrics:
          - type: accuracy
            value: 67.039
          - type: f1
            value: 55.1806
          - type: f1_weighted
            value: 73.41149999999999
          - type: ap
            value: 17.9914
          - type: ap_weighted
            value: 17.9914
          - type: main_score
            value: 67.039
        task:
          type: Classification
      - dataset:
          config: en
          name: MTEB AmazonCounterfactualClassification (en)
          revision: e8379541af4e31359cca9fbcf4b00f2671dba205
          split: test
          type: mteb/amazon_counterfactual
        metrics:
          - type: accuracy
            value: 65.59700000000001
          - type: f1
            value: 60.244299999999996
          - type: f1_weighted
            value: 68.9975
          - type: ap
            value: 29.762100000000004
          - type: ap_weighted
            value: 29.762100000000004
          - type: main_score
            value: 65.59700000000001
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB AmazonPolarityClassification (default)
          revision: e2d317d38cd51312af73b3d32a06d1a08b442046
          split: test
          type: mteb/amazon_polarity
        metrics:
          - type: accuracy
            value: 74.2565
          - type: f1
            value: 74.0291
          - type: f1_weighted
            value: 74.0291
          - type: ap
            value: 68.7595
          - type: ap_weighted
            value: 68.7595
          - type: main_score
            value: 74.2565
        task:
          type: Classification
      - dataset:
          config: en
          name: MTEB AmazonReviewsClassification (en)
          revision: 1399c76144fd37290681b995c656ef9b2e06e26d
          split: test
          type: mteb/amazon_reviews_multi
        metrics:
          - type: accuracy
            value: 34.946
          - type: f1
            value: 34.2853
          - type: f1_weighted
            value: 34.2853
          - type: main_score
            value: 34.946
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB ArguAna (default)
          revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
          split: test
          type: mteb/arguana
        metrics:
          - type: ndcg_at_1
            value: 33.286
          - type: ndcg_at_3
            value: 49.051
          - type: ndcg_at_5
            value: 54.107000000000006
          - type: ndcg_at_10
            value: 59.146
          - type: ndcg_at_20
            value: 60.897999999999996
          - type: ndcg_at_100
            value: 61.78399999999999
          - type: ndcg_at_1000
            value: 61.845000000000006
          - type: map_at_1
            value: 33.286
          - type: map_at_3
            value: 45.14
          - type: map_at_5
            value: 47.939
          - type: map_at_10
            value: 50.046
          - type: map_at_20
            value: 50.56
          - type: map_at_100
            value: 50.708
          - type: map_at_1000
            value: 50.712
          - type: recall_at_1
            value: 33.286
          - type: recall_at_3
            value: 60.38400000000001
          - type: recall_at_5
            value: 72.688
          - type: recall_at_10
            value: 88.122
          - type: recall_at_20
            value: 94.808
          - type: recall_at_100
            value: 99.21799999999999
          - type: recall_at_1000
            value: 99.644
          - type: precision_at_1
            value: 33.286
          - type: precision_at_3
            value: 20.128
          - type: precision_at_5
            value: 14.538
          - type: precision_at_10
            value: 8.812000000000001
          - type: precision_at_20
            value: 4.74
          - type: precision_at_100
            value: 0.992
          - type: precision_at_1000
            value: 0.1
          - type: mrr_at_1
            value: 33.926
          - type: mrr_at_3
            value: 45.3414
          - type: mrr_at_5
            value: 48.1828
          - type: mrr_at_10
            value: 50.270700000000005
          - type: mrr_at_20
            value: 50.7844
          - type: mrr_at_100
            value: 50.9259
          - type: mrr_at_1000
            value: 50.9294
          - type: nauc_ndcg_at_1_max
            value: -10.305
          - type: nauc_ndcg_at_1_std
            value: -15.674199999999999
          - type: nauc_ndcg_at_1_diff1
            value: 18.6355
          - type: nauc_ndcg_at_3_max
            value: -7.744
          - type: nauc_ndcg_at_3_std
            value: -16.894000000000002
          - type: nauc_ndcg_at_3_diff1
            value: 15.4469
          - type: nauc_ndcg_at_5_max
            value: -6.4887
          - type: nauc_ndcg_at_5_std
            value: -16.1382
          - type: nauc_ndcg_at_5_diff1
            value: 13.8214
          - type: nauc_ndcg_at_10_max
            value: -7.616499999999999
          - type: nauc_ndcg_at_10_std
            value: -15.8073
          - type: nauc_ndcg_at_10_diff1
            value: 13.7678
          - type: nauc_ndcg_at_20_max
            value: -6.9801
          - type: nauc_ndcg_at_20_std
            value: -15.068699999999998
          - type: nauc_ndcg_at_20_diff1
            value: 14.2013
          - type: nauc_ndcg_at_100_max
            value: -7.5221
          - type: nauc_ndcg_at_100_std
            value: -15.417200000000001
          - type: nauc_ndcg_at_100_diff1
            value: 15.1072
          - type: nauc_ndcg_at_1000_max
            value: -7.6931
          - type: nauc_ndcg_at_1000_std
            value: -15.5367
          - type: nauc_ndcg_at_1000_diff1
            value: 15.001700000000001
          - type: nauc_map_at_1_max
            value: -10.305
          - type: nauc_map_at_1_std
            value: -15.674199999999999
          - type: nauc_map_at_1_diff1
            value: 18.6355
          - type: nauc_map_at_3_max
            value: -8.4505
          - type: nauc_map_at_3_std
            value: -16.5487
          - type: nauc_map_at_3_diff1
            value: 15.965599999999998
          - type: nauc_map_at_5_max
            value: -7.8429
          - type: nauc_map_at_5_std
            value: -16.1332
          - type: nauc_map_at_5_diff1
            value: 15.0893
          - type: nauc_map_at_10_max
            value: -8.3186
          - type: nauc_map_at_10_std
            value: -15.979399999999998
          - type: nauc_map_at_10_diff1
            value: 15.136199999999999
          - type: nauc_map_at_20_max
            value: -8.1697
          - type: nauc_map_at_20_std
            value: -15.8241
          - type: nauc_map_at_20_diff1
            value: 15.260599999999998
          - type: nauc_map_at_100_max
            value: -8.2285
          - type: nauc_map_at_100_std
            value: -15.8624
          - type: nauc_map_at_100_diff1
            value: 15.412600000000001
          - type: nauc_map_at_1000_max
            value: -8.2359
          - type: nauc_map_at_1000_std
            value: -15.867
          - type: nauc_map_at_1000_diff1
            value: 15.408
          - type: nauc_recall_at_1_max
            value: -10.305
          - type: nauc_recall_at_1_std
            value: -15.674199999999999
          - type: nauc_recall_at_1_diff1
            value: 18.6355
          - type: nauc_recall_at_3_max
            value: -5.5097
          - type: nauc_recall_at_3_std
            value: -17.9896
          - type: nauc_recall_at_3_diff1
            value: 13.9525
          - type: nauc_recall_at_5_max
            value: -0.9383
          - type: nauc_recall_at_5_std
            value: -16.035
          - type: nauc_recall_at_5_diff1
            value: 8.8431
          - type: nauc_recall_at_10_max
            value: -2.8548
          - type: nauc_recall_at_10_std
            value: -14.1203
          - type: nauc_recall_at_10_diff1
            value: 3.2265
          - type: nauc_recall_at_20_max
            value: 14.2043
          - type: nauc_recall_at_20_std
            value: 2.1298999999999997
          - type: nauc_recall_at_20_diff1
            value: -1.9900000000000002
          - type: nauc_recall_at_100_max
            value: 44.0173
          - type: nauc_recall_at_100_std
            value: 42.131800000000005
          - type: nauc_recall_at_100_diff1
            value: 29.9983
          - type: nauc_recall_at_1000_max
            value: 25.9434
          - type: nauc_recall_at_1000_std
            value: 53.9252
          - type: nauc_recall_at_1000_diff1
            value: -0.9778
          - type: nauc_precision_at_1_max
            value: -10.305
          - type: nauc_precision_at_1_std
            value: -15.674199999999999
          - type: nauc_precision_at_1_diff1
            value: 18.6355
          - type: nauc_precision_at_3_max
            value: -5.5097
          - type: nauc_precision_at_3_std
            value: -17.9896
          - type: nauc_precision_at_3_diff1
            value: 13.9525
          - type: nauc_precision_at_5_max
            value: -0.9383
          - type: nauc_precision_at_5_std
            value: -16.035
          - type: nauc_precision_at_5_diff1
            value: 8.8431
          - type: nauc_precision_at_10_max
            value: -2.8548
          - type: nauc_precision_at_10_std
            value: -14.1203
          - type: nauc_precision_at_10_diff1
            value: 3.2265
          - type: nauc_precision_at_20_max
            value: 14.2043
          - type: nauc_precision_at_20_std
            value: 2.1298999999999997
          - type: nauc_precision_at_20_diff1
            value: -1.9900000000000002
          - type: nauc_precision_at_100_max
            value: 44.0173
          - type: nauc_precision_at_100_std
            value: 42.131800000000005
          - type: nauc_precision_at_100_diff1
            value: 29.9983
          - type: nauc_precision_at_1000_max
            value: 25.9434
          - type: nauc_precision_at_1000_std
            value: 53.9252
          - type: nauc_precision_at_1000_diff1
            value: -0.9778
          - type: nauc_mrr_at_1_max
            value: -9.833
          - type: nauc_mrr_at_1_std
            value: -14.8351
          - type: nauc_mrr_at_1_diff1
            value: 16.7604
          - type: nauc_mrr_at_3_max
            value: -9.0116
          - type: nauc_mrr_at_3_std
            value: -16.296
          - type: nauc_mrr_at_3_diff1
            value: 14.178199999999999
          - type: nauc_mrr_at_5_max
            value: -8.308300000000001
          - type: nauc_mrr_at_5_std
            value: -15.751999999999999
          - type: nauc_mrr_at_5_diff1
            value: 13.306299999999998
          - type: nauc_mrr_at_10_max
            value: -8.7962
          - type: nauc_mrr_at_10_std
            value: -15.688099999999999
          - type: nauc_mrr_at_10_diff1
            value: 13.2589
          - type: nauc_mrr_at_20_max
            value: -8.6773
          - type: nauc_mrr_at_20_std
            value: -15.479499999999998
          - type: nauc_mrr_at_20_diff1
            value: 13.354
          - type: nauc_mrr_at_100_max
            value: -8.7533
          - type: nauc_mrr_at_100_std
            value: -15.553600000000001
          - type: nauc_mrr_at_100_diff1
            value: 13.4796
          - type: nauc_mrr_at_1000_max
            value: -8.7608
          - type: nauc_mrr_at_1000_std
            value: -15.5582
          - type: nauc_mrr_at_1000_diff1
            value: 13.4748
          - type: main_score
            value: 59.146
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB ArxivClusteringP2P (default)
          revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
          split: test
          type: mteb/arxiv-clustering-p2p
        metrics:
          - type: v_measure
            value: 43.9715
          - type: v_measure_std
            value: 13.4325
          - type: main_score
            value: 43.9715
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB ArxivClusteringS2S (default)
          revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
          split: test
          type: mteb/arxiv-clustering-s2s
        metrics:
          - type: v_measure
            value: 34.775800000000004
          - type: v_measure_std
            value: 13.922799999999999
          - type: main_score
            value: 34.775800000000004
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB AskUbuntuDupQuestions (default)
          revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
          split: test
          type: mteb/askubuntudupquestions-reranking
        metrics:
          - type: map
            value: 63.3521
          - type: mrr
            value: 77.5965
          - type: nAUC_map_max
            value: 21.2353
          - type: nAUC_map_std
            value: 17.002100000000002
          - type: nAUC_map_diff1
            value: 3.8135000000000003
          - type: nAUC_mrr_max
            value: 35.058299999999996
          - type: nAUC_mrr_std
            value: 20.432
          - type: nAUC_mrr_diff1
            value: 9.2584
          - type: main_score
            value: 63.3521
        task:
          type: Reranking
      - dataset:
          config: default
          name: MTEB BIOSSES (default)
          revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
          split: test
          type: mteb/biosses-sts
        metrics:
          - type: pearson
            value: 89.8072
          - type: spearman
            value: 87.2875
          - type: cosine_pearson
            value: 89.8072
          - type: cosine_spearman
            value: 87.2875
          - type: manhattan_pearson
            value: 87.9173
          - type: manhattan_spearman
            value: 86.7327
          - type: euclidean_pearson
            value: 88.21600000000001
          - type: euclidean_spearman
            value: 87.2875
          - type: main_score
            value: 87.2875
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB Banking77Classification (default)
          revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
          split: test
          type: mteb/banking77
        metrics:
          - type: accuracy
            value: 81.8149
          - type: f1
            value: 81.2226
          - type: f1_weighted
            value: 81.2226
          - type: main_score
            value: 81.8149
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB BiorxivClusteringP2P (default)
          revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
          split: test
          type: mteb/biorxiv-clustering-p2p
        metrics:
          - type: v_measure
            value: 35.0927
          - type: v_measure_std
            value: 0.7048
          - type: main_score
            value: 35.0927
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB BiorxivClusteringS2S (default)
          revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
          split: test
          type: mteb/biorxiv-clustering-s2s
        metrics:
          - type: v_measure
            value: 30.220999999999997
          - type: v_measure_std
            value: 1.107
          - type: main_score
            value: 30.220999999999997
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB CQADupstackAndroidRetrieval (default)
          revision: f46a197baaae43b4f621051089b82a364682dfeb
          split: test
          type: mteb/cqadupstack-android
        metrics:
          - type: ndcg_at_1
            value: 44.349
          - type: ndcg_at_3
            value: 50.109
          - type: ndcg_at_5
            value: 52.88699999999999
          - type: ndcg_at_10
            value: 55.799
          - type: ndcg_at_20
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          - type: ndcg_at_100
            value: 60.539
          - type: ndcg_at_1000
            value: 61.897000000000006
          - type: map_at_1
            value: 36.230000000000004
          - type: map_at_3
            value: 44.929
          - type: map_at_5
            value: 47.191
          - type: map_at_10
            value: 48.88
          - type: map_at_20
            value: 49.685
          - type: map_at_100
            value: 50.327
          - type: map_at_1000
            value: 50.431000000000004
          - type: recall_at_1
            value: 36.230000000000004
          - type: recall_at_3
            value: 53.173
          - type: recall_at_5
            value: 60.35
          - type: recall_at_10
            value: 69.07
          - type: recall_at_20
            value: 75.371
          - type: recall_at_100
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          - type: recall_at_1000
            value: 96.75399999999999
          - type: precision_at_1
            value: 44.349
          - type: precision_at_3
            value: 23.748
          - type: precision_at_5
            value: 17.368
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            value: 10.629
          - type: precision_at_20
            value: 6.152
          - type: precision_at_100
            value: 1.6150000000000002
          - type: precision_at_1000
            value: 0.201
          - type: mrr_at_1
            value: 44.3491
          - type: mrr_at_3
            value: 52.0744
          - type: mrr_at_5
            value: 53.9628
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            value: 54.9072
          - type: mrr_at_20
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          - type: mrr_at_1000
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          - type: nauc_ndcg_at_1_max
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          name: MTEB MTOPDomainClassification (en)
          revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
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          type: mteb/mtop_domain
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          name: MTEB MTOPIntentClassification (en)
          revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
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          type: mteb/mtop_intent
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          name: MTEB MassiveIntentClassification (en)
          revision: 4672e20407010da34463acc759c162ca9734bca6
          split: test
          type: mteb/amazon_massive_intent
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      - dataset:
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          revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
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          type: mteb/amazon_massive_scenario
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          revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
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        task:
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      - dataset:
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          name: MTEB MedrxivClusteringS2S (default)
          revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
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          revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7
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          name: MTEB NFCorpus (default)
          revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
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          type: mteb/nfcorpus
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          type: mteb/reddit-clustering
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        task:
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      - dataset:
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          revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
          split: test
          type: mteb/sickr-sts
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      - dataset:
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          revision: a0d554a64d88156834ff5ae9920b964011b16384
          split: test
          type: mteb/sts12-sts
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      - dataset:
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          name: MTEB STS13 (default)
          revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
          split: test
          type: mteb/sts13-sts
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        task:
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      - dataset:
          config: default
          name: MTEB STS14 (default)
          revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
          split: test
          type: mteb/sts14-sts
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      - dataset:
          config: default
          name: MTEB STS15 (default)
          revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
          split: test
          type: mteb/sts15-sts
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      - dataset:
          config: default
          name: MTEB STS16 (default)
          revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
          split: test
          type: mteb/sts16-sts
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        task:
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      - dataset:
          config: en-en
          name: MTEB STS17 (en-en)
          revision: faeb762787bd10488a50c8b5be4a3b82e411949c
          split: test
          type: mteb/sts17-crosslingual-sts
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        task:
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      - dataset:
          config: en-tr
          name: MTEB STS17 (en-tr)
          revision: faeb762787bd10488a50c8b5be4a3b82e411949c
          split: test
          type: mteb/sts17-crosslingual-sts
        metrics:
          - type: pearson
            value: 62.0001
          - type: spearman
            value: 63.631099999999996
          - type: cosine_pearson
            value: 62.0001
          - type: cosine_spearman
            value: 63.631099999999996
          - type: manhattan_pearson
            value: 62.239599999999996
          - type: manhattan_spearman
            value: 62.892199999999995
          - type: euclidean_pearson
            value: 62.9809
          - type: euclidean_spearman
            value: 63.631099999999996
          - type: main_score
            value: 63.631099999999996
        task:
          type: STS
      - dataset:
          config: it-en
          name: MTEB STS17 (it-en)
          revision: faeb762787bd10488a50c8b5be4a3b82e411949c
          split: test
          type: mteb/sts17-crosslingual-sts
        metrics:
          - type: pearson
            value: 75.1556
          - type: spearman
            value: 76.8807
          - type: cosine_pearson
            value: 75.1556
          - type: cosine_spearman
            value: 76.8807
          - type: manhattan_pearson
            value: 76.2428
          - type: manhattan_spearman
            value: 76.8101
          - type: euclidean_pearson
            value: 76.107
          - type: euclidean_spearman
            value: 76.8807
          - type: main_score
            value: 76.8807
        task:
          type: STS
      - dataset:
          config: es-en
          name: MTEB STS17 (es-en)
          revision: faeb762787bd10488a50c8b5be4a3b82e411949c
          split: test
          type: mteb/sts17-crosslingual-sts
        metrics:
          - type: pearson
            value: 69.85719999999999
          - type: spearman
            value: 71.0489
          - type: cosine_pearson
            value: 69.85719999999999
          - type: cosine_spearman
            value: 71.0489
          - type: manhattan_pearson
            value: 71.08449999999999
          - type: manhattan_spearman
            value: 71.0051
          - type: euclidean_pearson
            value: 71.19760000000001
          - type: euclidean_spearman
            value: 71.0489
          - type: main_score
            value: 71.0489
        task:
          type: STS
      - dataset:
          config: nl-en
          name: MTEB STS17 (nl-en)
          revision: faeb762787bd10488a50c8b5be4a3b82e411949c
          split: test
          type: mteb/sts17-crosslingual-sts
        metrics:
          - type: pearson
            value: 76.1131
          - type: spearman
            value: 78.2714
          - type: cosine_pearson
            value: 76.1131
          - type: cosine_spearman
            value: 78.2714
          - type: manhattan_pearson
            value: 76.70270000000001
          - type: manhattan_spearman
            value: 77.7803
          - type: euclidean_pearson
            value: 77.14269999999999
          - type: euclidean_spearman
            value: 78.2714
          - type: main_score
            value: 78.2714
        task:
          type: STS
      - dataset:
          config: fr-en
          name: MTEB STS17 (fr-en)
          revision: faeb762787bd10488a50c8b5be4a3b82e411949c
          split: test
          type: mteb/sts17-crosslingual-sts
        metrics:
          - type: pearson
            value: 74.49719999999999
          - type: spearman
            value: 76.2747
          - type: cosine_pearson
            value: 74.49719999999999
          - type: cosine_spearman
            value: 76.2747
          - type: manhattan_pearson
            value: 75.071
          - type: manhattan_spearman
            value: 75.8969
          - type: euclidean_pearson
            value: 75.289
          - type: euclidean_spearman
            value: 76.2747
          - type: main_score
            value: 76.2747
        task:
          type: STS
      - dataset:
          config: en-de
          name: MTEB STS17 (en-de)
          revision: faeb762787bd10488a50c8b5be4a3b82e411949c
          split: test
          type: mteb/sts17-crosslingual-sts
        metrics:
          - type: pearson
            value: 76.7073
          - type: spearman
            value: 79.3107
          - type: cosine_pearson
            value: 76.7073
          - type: cosine_spearman
            value: 79.3107
          - type: manhattan_pearson
            value: 77.9578
          - type: manhattan_spearman
            value: 79.3195
          - type: euclidean_pearson
            value: 77.7386
          - type: euclidean_spearman
            value: 79.3107
          - type: main_score
            value: 79.3107
        task:
          type: STS
      - dataset:
          config: en-ar
          name: MTEB STS17 (en-ar)
          revision: faeb762787bd10488a50c8b5be4a3b82e411949c
          split: test
          type: mteb/sts17-crosslingual-sts
        metrics:
          - type: pearson
            value: 60.5826
          - type: spearman
            value: 61.0502
          - type: cosine_pearson
            value: 60.5826
          - type: cosine_spearman
            value: 61.0502
          - type: manhattan_pearson
            value: 61.202
          - type: manhattan_spearman
            value: 61.2039
          - type: euclidean_pearson
            value: 61.1915
          - type: euclidean_spearman
            value: 61.0502
          - type: main_score
            value: 61.0502
        task:
          type: STS
      - dataset:
          config: en
          name: MTEB STS22 (en)
          revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
          split: test
          type: mteb/sts22-crosslingual-sts
        metrics:
          - type: pearson
            value: 69.2521
          - type: spearman
            value: 68.06219999999999
          - type: cosine_pearson
            value: 69.2521
          - type: cosine_spearman
            value: 68.06219999999999
          - type: manhattan_pearson
            value: 70.5115
          - type: manhattan_spearman
            value: 67.8705
          - type: euclidean_pearson
            value: 70.68480000000001
          - type: euclidean_spearman
            value: 68.06219999999999
          - type: main_score
            value: 68.06219999999999
        task:
          type: STS
      - dataset:
          config: pl-en
          name: MTEB STS22 (pl-en)
          revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
          split: test
          type: mteb/sts22-crosslingual-sts
        metrics:
          - type: pearson
            value: 77.97500000000001
          - type: spearman
            value: 76.848
          - type: cosine_pearson
            value: 77.97500000000001
          - type: cosine_spearman
            value: 76.848
          - type: manhattan_pearson
            value: 76.4098
          - type: manhattan_spearman
            value: 76.6188
          - type: euclidean_pearson
            value: 77.17500000000001
          - type: euclidean_spearman
            value: 76.848
          - type: main_score
            value: 76.848
        task:
          type: STS
      - dataset:
          config: zh-en
          name: MTEB STS22 (zh-en)
          revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
          split: test
          type: mteb/sts22-crosslingual-sts
        metrics:
          - type: pearson
            value: 71.3604
          - type: spearman
            value: 70.7891
          - type: cosine_pearson
            value: 71.3604
          - type: cosine_spearman
            value: 70.7891
          - type: manhattan_pearson
            value: 73.0185
          - type: manhattan_spearman
            value: 70.79299999999999
          - type: euclidean_pearson
            value: 73.17620000000001
          - type: euclidean_spearman
            value: 70.7891
          - type: main_score
            value: 70.7891
        task:
          type: STS
      - dataset:
          config: es-en
          name: MTEB STS22 (es-en)
          revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
          split: test
          type: mteb/sts22-crosslingual-sts
        metrics:
          - type: pearson
            value: 77.58239999999999
          - type: spearman
            value: 78.5907
          - type: cosine_pearson
            value: 77.58239999999999
          - type: cosine_spearman
            value: 78.5907
          - type: manhattan_pearson
            value: 79.25720000000001
          - type: manhattan_spearman
            value: 78.6249
          - type: euclidean_pearson
            value: 79.3724
          - type: euclidean_spearman
            value: 78.5907
          - type: main_score
            value: 78.5907
        task:
          type: STS
      - dataset:
          config: de-en
          name: MTEB STS22 (de-en)
          revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
          split: test
          type: mteb/sts22-crosslingual-sts
        metrics:
          - type: pearson
            value: 63.324000000000005
          - type: spearman
            value: 55.1099
          - type: cosine_pearson
            value: 63.324000000000005
          - type: cosine_spearman
            value: 55.1099
          - type: manhattan_pearson
            value: 67.3128
          - type: manhattan_spearman
            value: 56.340199999999996
          - type: euclidean_pearson
            value: 67.12089999999999
          - type: euclidean_spearman
            value: 55.1099
          - type: main_score
            value: 55.1099
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB STSBenchmark (default)
          revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
          split: test
          type: mteb/stsbenchmark-sts
        metrics:
          - type: pearson
            value: 78.02329999999999
          - type: spearman
            value: 79.1887
          - type: cosine_pearson
            value: 78.02329999999999
          - type: cosine_spearman
            value: 79.1887
          - type: manhattan_pearson
            value: 78.8951
          - type: manhattan_spearman
            value: 78.9444
          - type: euclidean_pearson
            value: 79.1499
          - type: euclidean_spearman
            value: 79.1888
          - type: main_score
            value: 79.1887
        task:
          type: STS
      - dataset:
          config: default
          name: MTEB SciDocsRR (default)
          revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
          split: test
          type: mteb/scidocs-reranking
        metrics:
          - type: map
            value: 78.7501
          - type: mrr
            value: 93.9748
          - type: nAUC_map_max
            value: 54.495599999999996
          - type: nAUC_map_std
            value: 70.0377
          - type: nAUC_map_diff1
            value: 6.0146999999999995
          - type: nAUC_mrr_max
            value: 81.1486
          - type: nAUC_mrr_std
            value: 78.3478
          - type: nAUC_mrr_diff1
            value: 50.7613
          - type: main_score
            value: 78.7501
        task:
          type: Reranking
      - dataset:
          config: default
          name: MTEB SciFact (default)
          revision: 0228b52cf27578f30900b9e5271d331663a030d7
          split: test
          type: mteb/scifact
        metrics:
          - type: ndcg_at_1
            value: 58.667
          - type: ndcg_at_3
            value: 66.022
          - type: ndcg_at_5
            value: 68.508
          - type: ndcg_at_10
            value: 70.586
          - type: ndcg_at_20
            value: 71.714
          - type: ndcg_at_100
            value: 72.81
          - type: ndcg_at_1000
            value: 73.482
          - type: map_at_1
            value: 55.594
          - type: map_at_3
            value: 63.2
          - type: map_at_5
            value: 64.996
          - type: map_at_10
            value: 65.988
          - type: map_at_20
            value: 66.347
          - type: map_at_100
            value: 66.526
          - type: map_at_1000
            value: 66.547
          - type: recall_at_1
            value: 55.594
          - type: recall_at_3
            value: 71.22800000000001
          - type: recall_at_5
            value: 77.078
          - type: recall_at_10
            value: 83.172
          - type: recall_at_20
            value: 87.422
          - type: recall_at_100
            value: 93.167
          - type: recall_at_1000
            value: 98.667
          - type: precision_at_1
            value: 58.667
          - type: precision_at_3
            value: 25.778000000000002
          - type: precision_at_5
            value: 17.333000000000002
          - type: precision_at_10
            value: 9.433
          - type: precision_at_20
            value: 4.967
          - type: precision_at_100
            value: 1.06
          - type: precision_at_1000
            value: 0.11199999999999999
          - type: mrr_at_1
            value: 58.666700000000006
          - type: mrr_at_3
            value: 65.3889
          - type: mrr_at_5
            value: 66.62219999999999
          - type: mrr_at_10
            value: 67.3364
          - type: mrr_at_20
            value: 67.6046
          - type: mrr_at_100
            value: 67.73320000000001
          - type: mrr_at_1000
            value: 67.7526
          - type: nauc_ndcg_at_1_max
            value: 60.2511
          - type: nauc_ndcg_at_1_std
            value: 12.422
          - type: nauc_ndcg_at_1_diff1
            value: 74.4289
          - type: nauc_ndcg_at_3_max
            value: 60.2109
          - type: nauc_ndcg_at_3_std
            value: 11.0152
          - type: nauc_ndcg_at_3_diff1
            value: 71.0436
          - type: nauc_ndcg_at_5_max
            value: 62.690999999999995
          - type: nauc_ndcg_at_5_std
            value: 13.585700000000001
          - type: nauc_ndcg_at_5_diff1
            value: 70.4007
          - type: nauc_ndcg_at_10_max
            value: 62.740899999999996
          - type: nauc_ndcg_at_10_std
            value: 13.980400000000001
          - type: nauc_ndcg_at_10_diff1
            value: 70.0506
          - type: nauc_ndcg_at_20_max
            value: 62.271699999999996
          - type: nauc_ndcg_at_20_std
            value: 15.9756
          - type: nauc_ndcg_at_20_diff1
            value: 70.3237
          - type: nauc_ndcg_at_100_max
            value: 62.125
          - type: nauc_ndcg_at_100_std
            value: 15.5809
          - type: nauc_ndcg_at_100_diff1
            value: 70.4151
          - type: nauc_ndcg_at_1000_max
            value: 61.9259
          - type: nauc_ndcg_at_1000_std
            value: 15.3462
          - type: nauc_ndcg_at_1000_diff1
            value: 70.7346
          - type: nauc_map_at_1_max
            value: 53.6767
          - type: nauc_map_at_1_std
            value: 3.7751
          - type: nauc_map_at_1_diff1
            value: 74.60329999999999
          - type: nauc_map_at_3_max
            value: 57.0403
          - type: nauc_map_at_3_std
            value: 8.2272
          - type: nauc_map_at_3_diff1
            value: 71.7906
          - type: nauc_map_at_5_max
            value: 59.6713
          - type: nauc_map_at_5_std
            value: 10.8346
          - type: nauc_map_at_5_diff1
            value: 71.3356
          - type: nauc_map_at_10_max
            value: 60.0086
          - type: nauc_map_at_10_std
            value: 11.4394
          - type: nauc_map_at_10_diff1
            value: 71.14869999999999
          - type: nauc_map_at_20_max
            value: 59.940599999999996
          - type: nauc_map_at_20_std
            value: 12.0728
          - type: nauc_map_at_20_diff1
            value: 71.31
          - type: nauc_map_at_100_max
            value: 59.95589999999999
          - type: nauc_map_at_100_std
            value: 12.148299999999999
          - type: nauc_map_at_100_diff1
            value: 71.2142
          - type: nauc_map_at_1000_max
            value: 59.9486
          - type: nauc_map_at_1000_std
            value: 12.139
          - type: nauc_map_at_1000_diff1
            value: 71.2225
          - type: nauc_recall_at_1_max
            value: 53.6767
          - type: nauc_recall_at_1_std
            value: 3.7751
          - type: nauc_recall_at_1_diff1
            value: 74.60329999999999
          - type: nauc_recall_at_3_max
            value: 60.4078
          - type: nauc_recall_at_3_std
            value: 9.038300000000001
          - type: nauc_recall_at_3_diff1
            value: 67.60119999999999
          - type: nauc_recall_at_5_max
            value: 68.0179
          - type: nauc_recall_at_5_std
            value: 16.061600000000002
          - type: nauc_recall_at_5_diff1
            value: 65.54759999999999
          - type: nauc_recall_at_10_max
            value: 68.7372
          - type: nauc_recall_at_10_std
            value: 16.8637
          - type: nauc_recall_at_10_diff1
            value: 62.7613
          - type: nauc_recall_at_20_max
            value: 67.1403
          - type: nauc_recall_at_20_std
            value: 31.3919
          - type: nauc_recall_at_20_diff1
            value: 62.66929999999999
          - type: nauc_recall_at_100_max
            value: 68.6366
          - type: nauc_recall_at_100_std
            value: 32.4577
          - type: nauc_recall_at_100_diff1
            value: 64.52029999999999
          - type: nauc_recall_at_1000_max
            value: 70.7166
          - type: nauc_recall_at_1000_std
            value: 70.47149999999999
          - type: nauc_recall_at_1000_diff1
            value: 85.58590000000001
          - type: nauc_precision_at_1_max
            value: 60.2511
          - type: nauc_precision_at_1_std
            value: 12.422
          - type: nauc_precision_at_1_diff1
            value: 74.4289
          - type: nauc_precision_at_3_max
            value: 58.75280000000001
          - type: nauc_precision_at_3_std
            value: 27.605400000000003
          - type: nauc_precision_at_3_diff1
            value: 49.1523
          - type: nauc_precision_at_5_max
            value: 56.4694
          - type: nauc_precision_at_5_std
            value: 39.080799999999996
          - type: nauc_precision_at_5_diff1
            value: 28.8162
          - type: nauc_precision_at_10_max
            value: 48.8894
          - type: nauc_precision_at_10_std
            value: 43.8149
          - type: nauc_precision_at_10_diff1
            value: 15.0093
          - type: nauc_precision_at_20_max
            value: 41.4059
          - type: nauc_precision_at_20_std
            value: 50.7143
          - type: nauc_precision_at_20_diff1
            value: 8.3552
          - type: nauc_precision_at_100_max
            value: 33.5064
          - type: nauc_precision_at_100_std
            value: 52.8775
          - type: nauc_precision_at_100_diff1
            value: -5.0870999999999995
          - type: nauc_precision_at_1000_max
            value: 23.9064
          - type: nauc_precision_at_1000_std
            value: 57.784800000000004
          - type: nauc_precision_at_1000_diff1
            value: -20.1246
          - type: nauc_mrr_at_1_max
            value: 60.2511
          - type: nauc_mrr_at_1_std
            value: 12.422
          - type: nauc_mrr_at_1_diff1
            value: 74.4289
          - type: nauc_mrr_at_3_max
            value: 62.663199999999996
          - type: nauc_mrr_at_3_std
            value: 14.7348
          - type: nauc_mrr_at_3_diff1
            value: 72.1185
          - type: nauc_mrr_at_5_max
            value: 63.3871
          - type: nauc_mrr_at_5_std
            value: 15.773000000000001
          - type: nauc_mrr_at_5_diff1
            value: 71.6722
          - type: nauc_mrr_at_10_max
            value: 62.8474
          - type: nauc_mrr_at_10_std
            value: 15.1896
          - type: nauc_mrr_at_10_diff1
            value: 71.64110000000001
          - type: nauc_mrr_at_20_max
            value: 62.699400000000004
          - type: nauc_mrr_at_20_std
            value: 15.554499999999999
          - type: nauc_mrr_at_20_diff1
            value: 71.6049
          - type: nauc_mrr_at_100_max
            value: 62.6665
          - type: nauc_mrr_at_100_std
            value: 15.4586
          - type: nauc_mrr_at_100_diff1
            value: 71.6217
          - type: nauc_mrr_at_1000_max
            value: 62.6641
          - type: nauc_mrr_at_1000_std
            value: 15.4535
          - type: nauc_mrr_at_1000_diff1
            value: 71.6307
          - type: main_score
            value: 70.586
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB SprintDuplicateQuestions (default)
          revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
          split: test
          type: mteb/sprintduplicatequestions-pairclassification
        metrics:
          - type: similarity_accuracy
            value: 99.8416
          - type: similarity_accuracy_threshold
            value: 74.52069999999999
          - type: similarity_f1
            value: 92.008
          - type: similarity_f1_threshold
            value: 74.4529
          - type: similarity_precision
            value: 91.9162
          - type: similarity_recall
            value: 92.10000000000001
          - type: similarity_ap
            value: 96.54209999999999
          - type: cosine_accuracy
            value: 99.8416
          - type: cosine_accuracy_threshold
            value: 74.52069999999999
          - type: cosine_f1
            value: 92.008
          - type: cosine_f1_threshold
            value: 74.4529
          - type: cosine_precision
            value: 91.9162
          - type: cosine_recall
            value: 92.10000000000001
          - type: cosine_ap
            value: 96.54209999999999
          - type: manhattan_accuracy
            value: 99.8446
          - type: manhattan_accuracy_threshold
            value: 1784.866
          - type: manhattan_f1
            value: 92.1539
          - type: manhattan_f1_threshold
            value: 1787.6774
          - type: manhattan_precision
            value: 92.1079
          - type: manhattan_recall
            value: 92.2
          - type: manhattan_ap
            value: 96.5207
          - type: euclidean_accuracy
            value: 99.8416
          - type: euclidean_accuracy_threshold
            value: 71.3853
          - type: euclidean_f1
            value: 92.008
          - type: euclidean_f1_threshold
            value: 71.4803
          - type: euclidean_precision
            value: 91.9162
          - type: euclidean_recall
            value: 92.10000000000001
          - type: euclidean_ap
            value: 96.54209999999999
          - type: dot_accuracy
            value: 99.8416
          - type: dot_accuracy_threshold
            value: 74.52069999999999
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            value: -5.7068
          - type: nauc_mrr_at_100_max
            value: -23.0016
          - type: nauc_mrr_at_100_std
            value: -22.488
          - type: nauc_mrr_at_100_diff1
            value: -5.3738
          - type: nauc_mrr_at_1000_max
            value: -23.0016
          - type: nauc_mrr_at_1000_std
            value: -22.488
          - type: nauc_mrr_at_1000_diff1
            value: -5.3738
          - type: main_score
            value: 29.499
        task:
          type: Retrieval
      - dataset:
          config: default
          name: MTEB ToxicConversationsClassification (default)
          revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
          split: test
          type: mteb/toxic_conversations_50k
        metrics:
          - type: accuracy
            value: 65.8643
          - type: f1
            value: 50.6764
          - type: f1_weighted
            value: 73.2472
          - type: ap
            value: 12.2658
          - type: ap_weighted
            value: 12.2658
          - type: main_score
            value: 65.8643
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB TweetSentimentExtractionClassification (default)
          revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
          split: test
          type: mteb/tweet_sentiment_extraction
        metrics:
          - type: accuracy
            value: 59.6633
          - type: f1
            value: 59.935700000000004
          - type: f1_weighted
            value: 59.0249
          - type: main_score
            value: 59.6633
        task:
          type: Classification
      - dataset:
          config: default
          name: MTEB TwentyNewsgroupsClustering (default)
          revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
          split: test
          type: mteb/twentynewsgroups-clustering
        metrics:
          - type: v_measure
            value: 43.2311
          - type: v_measure_std
            value: 2.3994999999999997
          - type: main_score
            value: 43.2311
        task:
          type: Clustering
      - dataset:
          config: default
          name: MTEB TwitterSemEval2015 (default)
          revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
          split: test
          type: mteb/twittersemeval2015-pairclassification
        metrics:
          - type: similarity_accuracy
            value: 83.8469
          - type: similarity_accuracy_threshold
            value: 77.6695
          - type: similarity_f1
            value: 62.3159
          - type: similarity_f1_threshold
            value: 71.6554
          - type: similarity_precision
            value: 59.114599999999996
          - type: similarity_recall
            value: 65.8839
          - type: similarity_ap
            value: 67.00930000000001
          - type: cosine_accuracy
            value: 83.8469
          - type: cosine_accuracy_threshold
            value: 77.6695
          - type: cosine_f1
            value: 62.3159
          - type: cosine_f1_threshold
            value: 71.6554
          - type: cosine_precision
            value: 59.114599999999996
          - type: cosine_recall
            value: 65.8839
          - type: cosine_ap
            value: 67.00930000000001
          - type: manhattan_accuracy
            value: 83.7694
          - type: manhattan_accuracy_threshold
            value: 1677.8293999999999
          - type: manhattan_f1
            value: 62.1324
          - type: manhattan_f1_threshold
            value: 1848.6641
          - type: manhattan_precision
            value: 61.839999999999996
          - type: manhattan_recall
            value: 62.4274
          - type: manhattan_ap
            value: 66.8849
          - type: euclidean_accuracy
            value: 83.8469
          - type: euclidean_accuracy_threshold
            value: 66.8288
          - type: euclidean_f1
            value: 62.3159
          - type: euclidean_f1_threshold
            value: 75.2922
          - type: euclidean_precision
            value: 59.114599999999996
          - type: euclidean_recall
            value: 65.8839
          - type: euclidean_ap
            value: 67.00930000000001
          - type: dot_accuracy
            value: 83.8469
          - type: dot_accuracy_threshold
            value: 77.6695
          - type: dot_f1
            value: 62.3159
          - type: dot_f1_threshold
            value: 71.6554
          - type: dot_precision
            value: 59.114599999999996
          - type: dot_recall
            value: 65.8839
          - type: dot_ap
            value: 67.00930000000001
          - type: max_accuracy
            value: 83.8469
          - type: max_f1
            value: 62.3159
          - type: max_precision
            value: 61.839999999999996
          - type: max_recall
            value: 65.8839
          - type: max_ap
            value: 67.00930000000001
          - type: main_score
            value: 67.00930000000001
        task:
          type: PairClassification
      - dataset:
          config: default
          name: MTEB TwitterURLCorpus (default)
          revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
          split: test
          type: mteb/twitterurlcorpus-pairclassification
        metrics:
          - type: similarity_accuracy
            value: 88.8811
          - type: similarity_accuracy_threshold
            value: 71.1053
          - type: similarity_f1
            value: 77.9005
          - type: similarity_f1_threshold
            value: 67.5068
          - type: similarity_precision
            value: 75.5609
          - type: similarity_recall
            value: 80.3896
          - type: similarity_ap
            value: 85.459
          - type: cosine_accuracy
            value: 88.8811
          - type: cosine_accuracy_threshold
            value: 71.1053
          - type: cosine_f1
            value: 77.9005
          - type: cosine_f1_threshold
            value: 67.5068
          - type: cosine_precision
            value: 75.5609
          - type: cosine_recall
            value: 80.3896
          - type: cosine_ap
            value: 85.459
          - type: manhattan_accuracy
            value: 88.8598
          - type: manhattan_accuracy_threshold
            value: 1928.9173
          - type: manhattan_f1
            value: 77.9172
          - type: manhattan_f1_threshold
            value: 2007.8883999999998
          - type: manhattan_precision
            value: 76.29310000000001
          - type: manhattan_recall
            value: 79.6119
          - type: manhattan_ap
            value: 85.4464
          - type: euclidean_accuracy
            value: 88.8811
          - type: euclidean_accuracy_threshold
            value: 76.0193
          - type: euclidean_f1
            value: 77.9005
          - type: euclidean_f1_threshold
            value: 80.6141
          - type: euclidean_precision
            value: 75.5609
          - type: euclidean_recall
            value: 80.3896
          - type: euclidean_ap
            value: 85.459
          - type: dot_accuracy
            value: 88.8811
          - type: dot_accuracy_threshold
            value: 71.1053
          - type: dot_f1
            value: 77.9005
          - type: dot_f1_threshold
            value: 67.5068
          - type: dot_precision
            value: 75.5609
          - type: dot_recall
            value: 80.3896
          - type: dot_ap
            value: 85.459
          - type: max_accuracy
            value: 88.8811
          - type: max_f1
            value: 77.9172
          - type: max_precision
            value: 76.29310000000001
          - type: max_recall
            value: 80.3896
          - type: max_ap
            value: 85.459
          - type: main_score
            value: 85.459
        task:
          type: PairClassification
pipeline_tag: sentence-similarity
tags:
  - sentence-transformers
  - feature-extraction
  - sentence-similarity
  - mteb
  - arctic
  - snowflake-arctic-embed
  - transformers.js
  - onnx
  - teradata

A Teradata Vantage compatible Embeddings Model

Snowflake/snowflake-arctic-embed-l-v2.0

Overview of this Model

An Embedding Model which maps text (sentence/ paragraphs) into a vector. The Snowflake/snowflake-arctic-embed-l-v2.0 model is designed to generate text embeddings.

• 567.75M params (Sizes in ONNX format - "int8": 543.33MB, "uint8": 543.33MB)
• 8194 maximum input tokens
• 1024 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-l-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.

import teradataml as tdml
import getpass
from huggingface_hub import hf_hub_download

model_name = "snowflake-arctic-embed-l-v2.0"
number_dimensions_output = 1024
model_file_name = "model_int8.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
• Semantic Distance with TD_VectorDistance: Semantic Similarity Python Notebook
• RAG-Based Application with TD_VectorDistance: RAG and Bedrock Query PDF Notebook

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-l-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: int8, uint8

You can find the detailed conversion steps in the file 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.

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-l-v2.0")
predef_sess = rt.InferenceSession("onnx/model_int8.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