pipeline_tag: sentence-similarity tags:

• sentence-transformers
• feature-extraction
• sentence-similarity
• mteb
• arctic
• snowflake-arctic-embed
• transformers.js license: apache-2.0 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 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:
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    • 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
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    • 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
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    • 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
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    • type: precision_at_10 value: 8.812000000000001
    • type: precision_at_20 value: 4.74
    • type: precision_at_100 value: 0.992
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    • 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
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    • type: recall_at_1 value: 36.230000000000004
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  • dataset: config: default name: MTEB CQADupstackRetrieval (default) revision: CQADupstackRetrieval_is_a_combined_dataset split: test type: CQADupstackRetrieval_is_a_combined_dataset metrics:
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  • dataset: config: default name: MTEB CQADupstackTexRetrieval (default) revision: 46989137a86843e03a6195de44b09deda022eec7 split: test type: mteb/cqadupstack-tex metrics:
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  • dataset: config: default name: MTEB CQADupstackWordpressRetrieval (default) revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 split: test type: mteb/cqadupstack-wordpress metrics:
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  • dataset: config: en name: MTEB MTOPDomainClassification (en) revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf split: test type: mteb/mtop_domain metrics:
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  • dataset: config: en name: MTEB MTOPIntentClassification (en) revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba split: test type: mteb/mtop_intent metrics:
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  • dataset: config: en name: MTEB MassiveIntentClassification (en) revision: 4672e20407010da34463acc759c162ca9734bca6 split: test type: mteb/amazon_massive_intent metrics:
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  • dataset: config: en name: MTEB MassiveScenarioClassification (en) revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8 split: test type: mteb/amazon_massive_scenario metrics:
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  • dataset: config: default name: MTEB MedrxivClusteringP2P (default) revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 split: test type: mteb/medrxiv-clustering-p2p metrics:
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    • type: main_score value: 89.021 task: type: Retrieval
  • dataset: config: default name: MTEB RedditClustering (default) revision: 24640382cdbf8abc73003fb0fa6d111a705499eb split: test type: mteb/reddit-clustering metrics:
    • type: v_measure value: 51.208600000000004
    • type: v_measure_std value: 4.2761000000000005
    • type: main_score value: 51.208600000000004 task: type: Clustering
  • dataset: config: default name: MTEB RedditClusteringP2P (default) revision: 385e3cb46b4cfa89021f56c4380204149d0efe33 split: test type: mteb/reddit-clustering-p2p metrics:
    • type: v_measure value: 60.372899999999994
    • type: v_measure_std value: 12.0829
    • type: main_score value: 60.372899999999994 task: type: Clustering
  • dataset: config: default name: MTEB SCIDOCS (default) revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88 split: test type: mteb/scidocs metrics:
    • type: ndcg_at_1 value: 22.400000000000002
    • type: ndcg_at_3 value: 19.192
    • type: ndcg_at_5 value: 16.767000000000003
    • type: ndcg_at_10 value: 20.238999999999997
    • type: ndcg_at_20 value: 22.720000000000002
    • type: ndcg_at_100 value: 27.567999999999998
    • type: ndcg_at_1000 value: 32.535
    • type: map_at_1 value: 4.552
    • type: map_at_3 value: 8.495999999999999
    • type: map_at_5 value: 10.213999999999999
    • type: map_at_10 value: 11.985
    • type: map_at_20 value: 12.937000000000001
    • type: map_at_100 value: 13.885
    • type: map_at_1000 value: 14.155999999999999
    • type: recall_at_1 value: 4.552
    • type: recall_at_3 value: 11.067
    • type: recall_at_5 value: 15.052
    • type: recall_at_10 value: 21.422
    • type: recall_at_20 value: 27.279999999999998
    • type: recall_at_100 value: 42.968
    • type: recall_at_1000 value: 67.232
    • type: precision_at_1 value: 22.400000000000002
    • type: precision_at_3 value: 18.2
    • type: precision_at_5 value: 14.860000000000001
    • type: precision_at_10 value: 10.58
    • type: precision_at_20 value: 6.715
    • type: precision_at_100 value: 2.114
    • type: precision_at_1000 value: 0.331
    • type: mrr_at_1 value: 22.400000000000002
    • type: mrr_at_3 value: 31.0833
    • type: mrr_at_5 value: 32.853300000000004
    • type: mrr_at_10 value: 34.2814
    • type: mrr_at_20 value: 34.814
    • type: mrr_at_100 value: 35.2576
    • type: mrr_at_1000 value: 35.322199999999995
    • type: nauc_ndcg_at_1_max value: 23.7575
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    • type: main_score value: 20.238999999999997 task: type: Retrieval
  • dataset: config: default name: MTEB SICK-R (default) revision: 20a6d6f312dd54037fe07a32d58e5e168867909d split: test type: mteb/sickr-sts metrics:
    • type: pearson value: 79.5908
    • type: spearman value: 73.9888
    • type: cosine_pearson value: 79.5908
    • type: cosine_spearman value: 73.9888
    • type: manhattan_pearson value: 77.0623
    • type: manhattan_spearman value: 73.7724
    • type: euclidean_pearson value: 77.30890000000001
    • type: euclidean_spearman value: 73.9888
    • type: main_score value: 73.9888 task: type: STS
  • dataset: config: default name: MTEB STS12 (default) revision: a0d554a64d88156834ff5ae9920b964011b16384 split: test type: mteb/sts12-sts metrics:
    • type: pearson value: 74.0752
    • type: spearman value: 71.22699999999999
    • type: cosine_pearson value: 74.0752
    • type: cosine_spearman value: 71.22699999999999
    • type: manhattan_pearson value: 70.6037
    • type: manhattan_spearman value: 70.9916
    • type: euclidean_pearson value: 70.922
    • type: euclidean_spearman value: 71.22699999999999
    • type: main_score value: 71.22699999999999 task: type: STS
  • dataset: config: default name: MTEB STS13 (default) revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca split: test type: mteb/sts13-sts metrics:
    • type: pearson value: 77.8946
    • type: spearman value: 80.4405
    • type: cosine_pearson value: 77.8946
    • type: cosine_spearman value: 80.4405
    • type: manhattan_pearson value: 79.6856
    • type: manhattan_spearman value: 80.1236
    • type: euclidean_pearson value: 80.0315
    • type: euclidean_spearman value: 80.44059999999999
    • type: main_score value: 80.4405 task: type: STS
  • dataset: config: default name: MTEB STS14 (default) revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 split: test type: mteb/sts14-sts metrics:
    • type: pearson value: 76.2196
    • type: spearman value: 75.10419999999999
    • type: cosine_pearson value: 76.2196
    • type: cosine_spearman value: 75.10419999999999
    • type: manhattan_pearson value: 75.4647
    • type: manhattan_spearman value: 74.81179999999999
    • type: euclidean_pearson value: 75.8091
    • type: euclidean_spearman value: 75.10419999999999
    • type: main_score value: 75.10419999999999 task: type: STS
  • dataset: config: default name: MTEB STS15 (default) revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 split: test type: mteb/sts15-sts metrics:
    • type: pearson value: 81.2455
    • type: spearman value: 82.8681
    • type: cosine_pearson value: 81.2455
    • type: cosine_spearman value: 82.8681
    • type: manhattan_pearson value: 82.4327
    • type: manhattan_spearman value: 82.7513
    • type: euclidean_pearson value: 82.5635
    • type: euclidean_spearman value: 82.8681
    • type: main_score value: 82.8681 task: type: STS
  • dataset: config: default name: MTEB STS16 (default) revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 split: test type: mteb/sts16-sts metrics:
    • type: pearson value: 81.6322
    • type: spearman value: 83.487
    • type: cosine_pearson value: 81.6322
    • type: cosine_spearman value: 83.487
    • type: manhattan_pearson value: 83.0048
    • type: manhattan_spearman value: 83.4064
    • type: euclidean_pearson value: 83.0938
    • type: euclidean_spearman value: 83.487
    • type: main_score value: 83.487 task: type: STS
  • dataset: config: en-en name: MTEB STS17 (en-en) revision: faeb762787bd10488a50c8b5be4a3b82e411949c split: test type: mteb/sts17-crosslingual-sts metrics:
    • type: pearson value: 81.1124
    • type: spearman value: 84.5436
    • type: cosine_pearson value: 81.1124
    • type: cosine_spearman value: 84.5436
    • type: manhattan_pearson value: 83.5158
    • type: manhattan_spearman value: 84.596
    • type: euclidean_pearson value: 83.4429
    • type: euclidean_spearman value: 84.5436
    • type: main_score value: 84.5436 task: type: STS
  • 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
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    • 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:
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  • dataset: config: default name: MTEB SprintDuplicateQuestions (default) revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 split: test type: mteb/sprintduplicatequestions-pairclassification metrics:
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  • dataset: config: default name: MTEB StackExchangeClustering (default) revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 split: test type: mteb/stackexchange-clustering metrics:
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  • dataset: config: default name: MTEB StackExchangeClusteringP2P (default) revision: 815ca46b2622cec33ccafc3735d572c266efdb44 split: test type: mteb/stackexchange-clustering-p2p metrics:
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  • dataset: config: default name: MTEB StackOverflowDupQuestions (default) revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 split: test type: mteb/stackoverflowdupquestions-reranking metrics:
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  • dataset: config: default name: MTEB Touche2020 (default) revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f split: test type: mteb/touche2020 metrics:
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Snowflake's Arctic-embed-l-v2.0

News | Models | Usage | Evaluation | Contact | FAQ License | Acknowledgement

News

• 12/11/2024: Release of Technical Report
• 12/04/2024: Release of snowflake-arctic-embed-l-v2.0 and snowflake-arctic-embed-m-v2.0 our newest models with multilingual workloads in mind.

Models

Snowflake arctic-embed-l-v2.0 is the newest addition to the suite of embedding models Snowflake has released optimizing for retrieval performance and inference efficiency. Arctic Embed 2.0 introduces a new standard for multilingual embedding models, combining high-quality multilingual text retrieval without sacrificing performance in English. Released under the permissive Apache 2.0 license, Arctic Embed 2.0 is ideal for applications that demand reliable, enterprise-grade multilingual search and retrieval at scale.

Key Features:

1. Multilingual without compromise: Excels in English and non-English retrieval, outperforming leading open-source and proprietary models on benchmarks like MTEB Retrieval, CLEF, and MIRACL.

2. Inference efficiency: Its 303m non-embedding parameters inference is fast and efficient for any scale.

3. Compression-friendly: Achieves high-quality retrieval with embeddings as small as 128 bytes/vector using Matryoshka Representation Learning (MRL) and quantization-aware embedding training. Please note that like our v1.5 model, the MRL for this model is 256 dimensions, and high-quality 128-byte compression is achieved via 4-bit quantization (e.g. using a pq256x4fs fast-scan FAISS index or using the example code published alongside our 1.5 model).

4. Drop-In Replacement: arctic-embed-l-v2.0 builds on BAAI/bge-m3-retromae which allows direct drop-in inference replacement with any form of new libraries, kernels, inference engines etc.

5. Long Context Support: arctic-embed-l-v2.0 builds on BAAI/bge-m3-retromae which can support a context window of up to 8192 via the use of RoPE.

Quality Benchmarks

Unlike most other open-source models, Arctic-embed-l-v2.0 excels across English (via MTEB Retrieval) and multilingual (via MIRACL and CLEF). You no longer need to support models to empower high-quality English and multilingual retrieval. All numbers mentioned below are the average NDCG@10 across the dataset being discussed.

Aside from high-quality retrieval arctic delivers embeddings that are easily compressible. Leverage vector truncation via MRL to decrease vector size by 4x with less than 3% degredation in quality. Combine MRLed vectors with vector compression (Int4) to power retrieval in 128 bytes per doc.

Usage

Using Sentence Transformers

from sentence_transformers import SentenceTransformer

# Load the model
model_name = 'Snowflake/snowflake-arctic-embed-l-v2.0'
model = SentenceTransformer(model_name)

# Define the queries and documents
queries = ['what is snowflake?', 'Where can I get the best tacos?']
documents = ['The Data Cloud!', 'Mexico City of Course!']

# Compute embeddings: use `prompt_name="query"` to encode queries!
query_embeddings = model.encode(queries, prompt_name="query") 
document_embeddings = model.encode(documents)

# Compute cosine similarity scores
scores = model.similarity(query_embeddings, document_embeddings)

# Output the results
for query, query_scores in zip(queries, scores):
    doc_score_pairs = list(zip(documents, query_scores))
    doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
    print("Query:", query)
    for document, score in doc_score_pairs:
        print(score, document)

Using Huggingface Transformers

You can use the transformers package to use Snowflake's arctic-embed model, as shown below. For optimal retrieval quality, use the CLS token to embed each text portion and use the query prefix below (just on the query).

import torch
from transformers import AutoModel, AutoTokenizer

model_name = 'Snowflake/snowflake-arctic-embed-l-v2.0'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name, add_pooling_layer=False)
model.eval()

query_prefix = 'query: '
queries  = ['what is snowflake?', 'Where can I get the best tacos?']
queries_with_prefix = ["{}{}".format(query_prefix, i) for i in queries]
query_tokens = tokenizer(queries_with_prefix, padding=True, truncation=True, return_tensors='pt', max_length=8192)

documents = ['The Data Cloud!', 'Mexico City of Course!']
document_tokens =  tokenizer(documents, padding=True, truncation=True, return_tensors='pt', max_length=8192)

# Compute token embeddings
with torch.no_grad():
    query_embeddings = model(**query_tokens)[0][:, 0]
    document_embeddings = model(**document_tokens)[0][:, 0]


# normalize embeddings
query_embeddings = torch.nn.functional.normalize(query_embeddings, p=2, dim=1)
document_embeddings = torch.nn.functional.normalize(document_embeddings, p=2, dim=1)

scores = torch.mm(query_embeddings, document_embeddings.transpose(0, 1))
for query, query_scores in zip(queries, scores):
    doc_score_pairs = list(zip(documents, query_scores))
    doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
    #Output passages & scores
    print("Query:", query)
    for document, score in doc_score_pairs:
        print(score, document)

This should produce the following scores

Query: what is snowflake?
tensor(0.2715) The Data Cloud!
tensor(0.0661) Mexico City of Course!
Query: Where can I get the best tacos?
tensor(0.2797) Mexico City of Course!
tensor(0.1250) The Data Cloud!

Using Huggingface Transformers.js

If you haven't already, you can install the Transformers.js JavaScript library from NPM using:

npm i @huggingface/transformers

You can then use the model for retrieval, as follows:

import { pipeline, dot } from '@huggingface/transformers';

// Create feature extraction pipeline
const extractor = await pipeline('feature-extraction', 'Snowflake/snowflake-arctic-embed-m-v2.0', {
    dtype: 'q8',
});

// Generate sentence embeddings
const sentences = [
    'query: what is snowflake?',
    'The Data Cloud!',
    'Mexico City of Course!',
]
const output = await extractor(sentences, { normalize: true, pooling: 'cls' });

// Compute similarity scores
const [source_embeddings, ...document_embeddings ] = output.tolist();
const similarities = document_embeddings.map(x => dot(source_embeddings, x));
console.log(similarities); // [0.24783534471401417, 0.05313122704326892]

Contact

Feel free to open an issue or pull request if you have any questions or suggestions about this project. You also can email Daniel Campos(daniel.campos@snowflake.com).

License

Arctic is licensed under the Apache-2. The released models can be used for commercial purposes free of charge.