README.md

---
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-m-v2.0
  results:
  - dataset:
      config: en-ext
      name: MTEB AmazonCounterfactualClassification (en-ext)
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
      split: test
      type: mteb/amazon_counterfactual
    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:
      config: en
      name: MTEB AmazonCounterfactualClassification (en)
      revision: e8379541af4e31359cca9fbcf4b00f2671dba205
      split: test
      type: mteb/amazon_counterfactual
    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:
      config: default
      name: MTEB AmazonPolarityClassification (default)
      revision: e2d317d38cd51312af73b3d32a06d1a08b442046
      split: test
      type: mteb/amazon_polarity
    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:
      config: en
      name: MTEB AmazonReviewsClassification (en)
      revision: 1399c76144fd37290681b995c656ef9b2e06e26d
      split: test
      type: mteb/amazon_reviews_multi
    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: Classification
  - dataset:
      config: default
      name: MTEB ArguAna (default)
      revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
      split: test
      type: mteb/arguana
    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: Retrieval
  - dataset:
      config: default
      name: MTEB ArxivClusteringP2P (default)
      revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
      split: test
      type: mteb/arxiv-clustering-p2p
    metrics:
    - type: v_measure
      value: 44.684400000000004
    - type: v_measure_std
      value: 13.5064
    - type: main_score
      value: 44.684400000000004
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB ArxivClusteringS2S (default)
      revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
      split: test
      type: mteb/arxiv-clustering-s2s
    metrics:
    - type: v_measure
      value: 35.0503
    - type: v_measure_std
      value: 13.9543
    - type: main_score
      value: 35.0503
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB AskUbuntuDupQuestions (default)
      revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
      split: test
      type: mteb/askubuntudupquestions-reranking
    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: Reranking
  - dataset:
      config: default
      name: MTEB BIOSSES (default)
      revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
      split: test
      type: mteb/biosses-sts
    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: STS
  - dataset:
      config: default
      name: MTEB Banking77Classification (default)
      revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
      split: test
      type: mteb/banking77
    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: Classification
  - dataset:
      config: default
      name: MTEB BiorxivClusteringP2P (default)
      revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
      split: test
      type: mteb/biorxiv-clustering-p2p
    metrics:
    - type: v_measure
      value: 36.0126
    - type: v_measure_std
      value: 0.47019999999999995
    - type: main_score
      value: 36.0126
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB BiorxivClusteringS2S (default)
      revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
      split: test
      type: mteb/biorxiv-clustering-s2s
    metrics:
    - type: v_measure
      value: 28.6331
    - type: v_measure_std
      value: 0.8607999999999999
    - type: main_score
      value: 28.6331
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB CQADupstackAndroidRetrieval (default)
      revision: f46a197baaae43b4f621051089b82a364682dfeb
      split: test
      type: mteb/cqadupstack-android
    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
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      value: -3.5654
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    - type: nauc_map_at_1_max
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      value: -5.8014
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      value: 59.0186
    - type: nauc_map_at_3_max
      value: 40.8666
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      value: -3.9361
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      value: -3.8526
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      value: -3.4568000000000003
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      value: -3.4715999999999996
    - type: nauc_map_at_1000_diff1
      value: 55.0117
    - type: nauc_recall_at_1_max
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  - dataset:
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      revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
      split: test
      type: mteb/imdb
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    task:
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  - dataset:
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      revision: c5a29a104738b98a9e76336939199e264163d4a0
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      type: mteb/msmarco
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    task:
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  - dataset:
      config: en
      name: MTEB MTOPDomainClassification (en)
      revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
      split: test
      type: mteb/mtop_domain
    metrics:
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    task:
      type: Classification
  - dataset:
      config: en
      name: MTEB MTOPIntentClassification (en)
      revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
      split: test
      type: mteb/mtop_intent
    metrics:
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    task:
      type: Classification
  - dataset:
      config: en
      name: MTEB MassiveIntentClassification (en)
      revision: 4672e20407010da34463acc759c162ca9734bca6
      split: test
      type: mteb/amazon_massive_intent
    metrics:
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    - type: f1_weighted
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    task:
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  - dataset:
      config: en
      name: MTEB MassiveScenarioClassification (en)
      revision: fad2c6e8459f9e1c45d9315f4953d921437d70f8
      split: test
      type: mteb/amazon_massive_scenario
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    task:
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  - dataset:
      config: default
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      revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
      split: test
      type: mteb/medrxiv-clustering-p2p
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      value: 1.3743999999999998
    - type: main_score
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    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB MedrxivClusteringS2S (default)
      revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
      split: test
      type: mteb/medrxiv-clustering-s2s
    metrics:
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      value: 1.4235
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    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB MindSmallReranking (default)
      revision: 59042f120c80e8afa9cdbb224f67076cec0fc9a7
      split: test
      type: mteb/mind_small
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      value: 12.437199999999999
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      value: -19.5255
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      value: -6.1112
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      value: 12.1585
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      value: 30.3039
    task:
      type: Reranking
  - dataset:
      config: default
      name: MTEB NFCorpus (default)
      revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
      split: test
      type: mteb/nfcorpus
    metrics:
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      value: 41.975
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      value: 39.421
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    task:
      type: Retrieval
  - dataset:
      config: default
      name: MTEB RedditClustering (default)
      revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
      split: test
      type: mteb/reddit-clustering
    metrics:
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    - type: v_measure_std
      value: 4.5517
    - type: main_score
      value: 49.1271
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB RedditClusteringP2P (default)
      revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
      split: test
      type: mteb/reddit-clustering-p2p
    metrics:
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    - type: v_measure_std
      value: 12.6364
    - type: main_score
      value: 61.0626
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB SCIDOCS (default)
      revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
      split: test
      type: mteb/scidocs
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    - type: recall_at_1000
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    task:
      type: Retrieval
  - dataset:
      config: default
      name: MTEB SICK-R (default)
      revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
      split: test
      type: mteb/sickr-sts
    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:
      config: default
      name: MTEB STS12 (default)
      revision: a0d554a64d88156834ff5ae9920b964011b16384
      split: test
      type: mteb/sts12-sts
    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
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      value: 70.4521
    - type: euclidean_pearson
      value: 69.41720000000001
    - type: euclidean_spearman
      value: 70.6581
    - type: main_score
      value: 70.6581
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STS13 (default)
      revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
      split: test
      type: mteb/sts13-sts
    metrics:
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    - 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:
      config: default
      name: MTEB STS14 (default)
      revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
      split: test
      type: mteb/sts14-sts
    metrics:
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      value: 74.46340000000001
    - type: spearman
      value: 72.9162
    - 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
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      value: 72.9162
    - type: main_score
      value: 72.9162
    task:
      type: STS
  - dataset:
      config: default
      name: MTEB STS15 (default)
      revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
      split: test
      type: mteb/sts15-sts
    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:
      config: default
      name: MTEB STS16 (default)
      revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
      split: test
      type: mteb/sts16-sts
    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
    - type: euclidean_spearman
      value: 81.2262
    - type: main_score
      value: 81.2262
    task:
      type: STS
  - dataset:
      config: es-en
      name: MTEB STS17 (es-en)
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
      split: test
      type: mteb/sts17-crosslingual-sts
    metrics:
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    - 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:
      config: fr-en
      name: MTEB STS17 (fr-en)
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
      split: test
      type: mteb/sts17-crosslingual-sts
    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:
      config: en-en
      name: MTEB STS17 (en-en)
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
      split: test
      type: mteb/sts17-crosslingual-sts
    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:
      config: en-tr
      name: MTEB STS17 (en-tr)
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
      split: test
      type: mteb/sts17-crosslingual-sts
    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:
      config: it-en
      name: MTEB STS17 (it-en)
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
      split: test
      type: mteb/sts17-crosslingual-sts
    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:
      config: nl-en
      name: MTEB STS17 (nl-en)
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
      split: test
      type: mteb/sts17-crosslingual-sts
    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:
      config: en-ar
      name: MTEB STS17 (en-ar)
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
      split: test
      type: mteb/sts17-crosslingual-sts
    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:
      config: en-de
      name: MTEB STS17 (en-de)
      revision: faeb762787bd10488a50c8b5be4a3b82e411949c
      split: test
      type: mteb/sts17-crosslingual-sts
    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:
      config: zh-en
      name: MTEB STS22 (zh-en)
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
      split: test
      type: mteb/sts22-crosslingual-sts
    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:
      config: es-en
      name: MTEB STS22 (es-en)
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
      split: test
      type: mteb/sts22-crosslingual-sts
    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:
      config: de-en
      name: MTEB STS22 (de-en)
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
      split: test
      type: mteb/sts22-crosslingual-sts
    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:
      config: pl-en
      name: MTEB STS22 (pl-en)
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
      split: test
      type: mteb/sts22-crosslingual-sts
    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:
      config: en
      name: MTEB STS22 (en)
      revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
      split: test
      type: mteb/sts22-crosslingual-sts
    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:
      config: default
      name: MTEB STSBenchmark (default)
      revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
      split: test
      type: mteb/stsbenchmark-sts
    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: STS
  - dataset:
      config: default
      name: MTEB SciDocsRR (default)
      revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
      split: test
      type: mteb/scidocs-reranking
    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: Reranking
  - dataset:
      config: default
      name: MTEB SciFact (default)
      revision: 0228b52cf27578f30900b9e5271d331663a030d7
      split: test
      type: mteb/scifact
    metrics:
    - type: ndcg_at_1
      value: 61.0
    - 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: 61.0
    - 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: 61.0
    - 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: 100.0
    - 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: Retrieval
  - dataset:
      config: default
      name: MTEB SprintDuplicateQuestions (default)
      revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
      split: test
      type: mteb/sprintduplicatequestions-pairclassification
    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
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      value: 91.3568
    - type: euclidean_recall
      value: 90.9
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      value: 96.00319999999999
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      value: 99.8248
    - type: dot_accuracy_threshold
      value: 74.6155
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      value: 91.12780000000001
    - type: dot_f1_threshold
      value: 74.2422
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      value: 91.3568
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      value: 90.9
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      value: 96.00319999999999
    - type: max_accuracy
      value: 99.8257
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      value: 91.1531
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      value: 91.3568
    - type: max_recall
      value: 91.7
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      value: 96.00319999999999
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      value: 96.00319999999999
    task:
      type: PairClassification
  - dataset:
      config: default
      name: MTEB StackExchangeClustering (default)
      revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
      split: test
      type: mteb/stackexchange-clustering
    metrics:
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      value: 5.2151000000000005
    - type: main_score
      value: 61.3985
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB StackExchangeClusteringP2P (default)
      revision: 815ca46b2622cec33ccafc3735d572c266efdb44
      split: test
      type: mteb/stackexchange-clustering-p2p
    metrics:
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      value: 1.5853
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      value: 36.1433
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB StackOverflowDupQuestions (default)
      revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
      split: test
      type: mteb/stackoverflowdupquestions-reranking
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      value: 6.239999999999999
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      value: 36.3486
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      value: 10.9306
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      value: 6.7909
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      value: 36.5536
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      value: 50.47580000000001
    task:
      type: Reranking
  - dataset:
      config: default
      name: MTEB SummEval (default)
      revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
      split: test
      type: mteb/summeval
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    task:
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  - dataset:
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      revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
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      type: mteb/trec-covid
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    task:
      type: Retrieval
  - dataset:
      config: default
      name: MTEB Touche2020 (default)
      revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
      split: test
      type: mteb/touche2020
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      value: 41.094
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      value: 2.64
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      value: 36.735
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    - 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: Retrieval
  - dataset:
      config: default
      name: MTEB ToxicConversationsClassification (default)
      revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
      split: test
      type: mteb/toxic_conversations_50k
    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:
      config: default
      name: MTEB TweetSentimentExtractionClassification (default)
      revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
      split: test
      type: mteb/tweet_sentiment_extraction
    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: Classification
  - dataset:
      config: default
      name: MTEB TwentyNewsgroupsClustering (default)
      revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
      split: test
      type: mteb/twentynewsgroups-clustering
    metrics:
    - type: v_measure
      value: 39.293
    - type: v_measure_std
      value: 1.5684
    - type: main_score
      value: 39.293
    task:
      type: Clustering
  - dataset:
      config: default
      name: MTEB TwitterSemEval2015 (default)
      revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
      split: test
      type: mteb/twittersemeval2015-pairclassification
    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:
      config: default
      name: MTEB TwitterURLCorpus (default)
      revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
      split: test
      type: mteb/twitterurlcorpus-pairclassification
    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
    task:
      type: PairClassification
---
<h1 align="center">Snowflake's Arctic-embed-m-v2.0</h1>
<h4 align="center">
   <p>
       <a href=#news>News</a> |
       <a href=#models>Models</a> |
       <a href=#usage>Usage</a>  |
       <a href="#evaluation">Evaluation</a> |
       <a href="#contact">Contact</a> |
       <a href="#faq">FAQ</a>
       <a href="#license">License</a> |
       <a href="#acknowledgement">Acknowledgement</a>
   <p>
</h4>


## News
12/04/2024: Release of [snowflake-arctic-embed-l-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-l-v2.0) and [snowflake-arctic-embed-m-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v2.0) our newest models with multilingual workloads in mind.


## Models
Snowflake arctic-embed-m-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 113m 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.

4. Long Context Support: arctic-embed-m-v2.0 builds on [GTE-multilingual-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-base) 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-m-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. 

| Model Name | # params | # non-emb params | # dimensions | BEIR (15) | MIRACL (4) | CLEF (Focused) | CLEF (Full) |
|---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|
| **snowflake-arctic-m-v2.0** | 305M | 113M | 768 | **55.4** | 55.2 | **51.7** | **53.9** |
| snowflake-arctic-m | 109M | 86M | 768 | 54.9 | 24.9 | 34.4 | 29.1 |
| me5 base | 560M | 303M | 1024 | 51.4 | 54.0 | 43.0 | 34.6 |
| bge-m3 (BAAI) | 568M | 303M | 1024 | 48.8 | **56.8** | 40.8 | 41.3 |
| gte (Alibaba) | 305M | 113M | 768 | 51.1 | 52.3 | 47.7 | 53.1 |

Aside from high-quality retrieval, arctic delivers embeddings that are easily compressible. By leveraging vector truncation via MRL to decrease vector size by 3x with about 3% degradation in quality. 
Combine MRLed vectors with vector compression (Int4) to power retrieval in 128 bytes per doc. 

| Model |  | BEIR (15) | Relative Performance | MIRACL (4) | Relative Performance | CLEF (5) | Relative Performance | CLEF (Full) | Relative Performance |
|---|---|:---:|:---:|:---:|:---:|:---:|---|---|---|
| snowflake-arctic-m-v2.0 | 768 | 55.4 | N/A | 55.2 | N/A | 51.7 | N/A | 53.9 | N/A |
| snowflake-arctic-m-v2.0 | 256 | 54.4 | -1.81% | 54.0 | -2.17% | 50.6 | -2.13% | 52.3 | -3.06% |

## Usage

### Using Sentence Transformers

```python
from sentence_transformers import SentenceTransformer

# Load the model
model_name = 'Snowflake/snowflake-arctic-embed-m-v2.0'
model = SentenceTransformer(model_name, trust_remote_code=True)

# 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).

```python
import torch
from transformers import AutoModel, AutoTokenizer

model_name = 'Snowflake/snowflake-arctic-embed-m-v2.0'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name, add_pooling_layer=False, trust_remote_code=True)
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)
```


## 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](https://www.apache.org/licenses/LICENSE-2.0). The released models can be used for commercial purposes free of charge.