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metadata
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
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          - type: nauc_ndcg_at_1_std
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          - type: nauc_ndcg_at_1_diff1
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          - type: nauc_ndcg_at_3_max
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          - type: nauc_ndcg_at_100_max
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          - type: nauc_ndcg_at_100_diff1
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          - type: nauc_ndcg_at_1000_max
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          - type: nauc_ndcg_at_1000_std
            value: -15.223500000000001
          - type: nauc_ndcg_at_1000_diff1
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          - type: nauc_map_at_1_max
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          - type: nauc_map_at_1_std
            value: -17.1177
          - type: nauc_map_at_1_diff1
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          - type: nauc_map_at_3_max
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          - type: nauc_map_at_3_std
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          - type: nauc_map_at_5_max
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          - type: nauc_map_at_100_max
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          - type: nauc_map_at_100_std
            value: -15.848300000000002
          - type: nauc_map_at_100_diff1
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          - type: nauc_map_at_1000_max
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          - type: nauc_map_at_1000_std
            value: -15.8522
          - type: nauc_map_at_1000_diff1
            value: 10.9401
          - type: nauc_recall_at_1_max
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          - 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
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          - type: nauc_recall_at_20_max
            value: 15.754299999999999
          - type: nauc_recall_at_20_std
            value: 8.1974
          - type: nauc_recall_at_20_diff1
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          - type: nauc_recall_at_100_max
            value: 20.4574
          - type: nauc_recall_at_100_std
            value: 36.3741
          - type: nauc_recall_at_100_diff1
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          - type: nauc_recall_at_1000_max
            value: 21.6023
          - type: nauc_recall_at_1000_std
            value: 68.7296
          - type: nauc_recall_at_1000_diff1
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          - type: nauc_precision_at_1_max
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          - type: nauc_precision_at_1_std
            value: -17.1177
          - type: nauc_precision_at_1_diff1
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          - type: nauc_precision_at_3_max
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          - type: nauc_precision_at_3_std
            value: -16.730800000000002
          - type: nauc_precision_at_3_diff1
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          - type: nauc_precision_at_5_max
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          - type: nauc_precision_at_5_std
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          - type: nauc_precision_at_5_diff1
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          - type: nauc_precision_at_10_max
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          - type: nauc_precision_at_10_std
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          - type: nauc_precision_at_10_diff1
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          - type: nauc_precision_at_20_max
            value: 15.754299999999999
          - type: nauc_precision_at_20_std
            value: 8.1974
          - type: nauc_precision_at_20_diff1
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          - 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
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          - type: ndcg_at_100
            value: 61.307
          - type: ndcg_at_1000
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          - 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
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          - type: map_at_1000
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          - 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
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          - type: precision_at_3
            value: 24.368000000000002
          - type: precision_at_5
            value: 17.854
          - type: precision_at_10
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          - type: precision_at_20
            value: 6.23
          - type: precision_at_100
            value: 1.614
          - type: precision_at_1000
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          - type: mrr_at_1
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          - type: mrr_at_3
            value: 52.9804
          - type: mrr_at_5
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          - type: mrr_at_10
            value: 55.5713
          - type: mrr_at_20
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          - type: mrr_at_1000
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          revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
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          name: MTEB MassiveIntentClassification (en)
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          type: mteb/amazon_massive_intent
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      - dataset:
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          revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
          split: test
          type: mteb/sickr-sts
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          revision: a0d554a64d88156834ff5ae9920b964011b16384
          split: test
          type: mteb/sts12-sts
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          name: MTEB STS13 (default)
          revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
          split: test
          type: mteb/sts13-sts
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      - dataset:
          config: default
          name: MTEB STS14 (default)
          revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
          split: test
          type: mteb/sts14-sts
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      - dataset:
          config: default
          name: MTEB STS15 (default)
          revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
          split: test
          type: mteb/sts15-sts
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      - dataset:
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          name: MTEB STS16 (default)
          revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
          split: test
          type: mteb/sts16-sts
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        task:
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      - dataset:
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          name: MTEB STS17 (es-en)
          revision: faeb762787bd10488a50c8b5be4a3b82e411949c
          split: test
          type: mteb/sts17-crosslingual-sts
        metrics:
          - type: pearson
            value: 67.9751
          - type: spearman
            value: 68.92099999999999
          - type: cosine_pearson
            value: 67.9751
          - type: cosine_spearman
            value: 68.92099999999999
          - type: manhattan_pearson
            value: 68.9355
          - type: manhattan_spearman
            value: 68.777
          - type: euclidean_pearson
            value: 69.11410000000001
          - type: euclidean_spearman
            value: 68.92099999999999
          - type: main_score
            value: 68.92099999999999
        task:
          type: STS
      - dataset:
          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
          - type: ndcg_at_3
            value: 67.589
          - type: ndcg_at_5
            value: 68.948
          - type: ndcg_at_10
            value: 71.8
          - type: ndcg_at_20
            value: 72.595
          - type: ndcg_at_100
            value: 74.138
          - type: ndcg_at_1000
            value: 74.83800000000001
          - type: map_at_1
            value: 57.74399999999999
          - type: map_at_3
            value: 64.866
          - type: map_at_5
            value: 66.018
          - type: map_at_10
            value: 67.535
          - type: map_at_20
            value: 67.77
          - type: map_at_100
            value: 68.011
          - type: map_at_1000
            value: 68.042
          - type: recall_at_1
            value: 57.74399999999999
          - type: recall_at_3
            value: 71.906
          - type: recall_at_5
            value: 75.344
          - type: recall_at_10
            value: 83.2
          - type: recall_at_20
            value: 86.26700000000001
          - type: recall_at_100
            value: 94.333
          - type: recall_at_1000
            value: 99.667
          - type: precision_at_1
            value: 61
          - type: precision_at_3
            value: 26.111
          - type: precision_at_5
            value: 16.8
          - type: precision_at_10
            value: 9.5
          - type: precision_at_20
            value: 4.933
          - type: precision_at_100
            value: 1.073
          - type: precision_at_1000
            value: 0.11299999999999999
          - type: mrr_at_1
            value: 61
          - type: mrr_at_3
            value: 67.4444
          - type: mrr_at_5
            value: 68.0778
          - type: mrr_at_10
            value: 69.0483
          - type: mrr_at_20
            value: 69.2333
          - type: mrr_at_100
            value: 69.4403
          - type: mrr_at_1000
            value: 69.4708
          - type: nauc_ndcg_at_1_max
            value: 53.481500000000004
          - type: nauc_ndcg_at_1_std
            value: 8.227
          - type: nauc_ndcg_at_1_diff1
            value: 72.0771
          - type: nauc_ndcg_at_3_max
            value: 57.0147
          - type: nauc_ndcg_at_3_std
            value: 5.2435
          - type: nauc_ndcg_at_3_diff1
            value: 68.8841
          - type: nauc_ndcg_at_5_max
            value: 57.4675
          - type: nauc_ndcg_at_5_std
            value: 8.4709
          - type: nauc_ndcg_at_5_diff1
            value: 67.2977
          - type: nauc_ndcg_at_10_max
            value: 60.3957
          - type: nauc_ndcg_at_10_std
            value: 11.3174
          - type: nauc_ndcg_at_10_diff1
            value: 67.8332
          - type: nauc_ndcg_at_20_max
            value: 60.3607
          - type: nauc_ndcg_at_20_std
            value: 11.9948
          - type: nauc_ndcg_at_20_diff1
            value: 68.1122
          - type: nauc_ndcg_at_100_max
            value: 59.5293
          - type: nauc_ndcg_at_100_std
            value: 11.697799999999999
          - type: nauc_ndcg_at_100_diff1
            value: 68.453
          - type: nauc_ndcg_at_1000_max
            value: 58.8931
          - type: nauc_ndcg_at_1000_std
            value: 10.876199999999999
          - type: nauc_ndcg_at_1000_diff1
            value: 68.5746
          - type: nauc_map_at_1_max
            value: 49.762299999999996
          - type: nauc_map_at_1_std
            value: -0.2785
          - type: nauc_map_at_1_diff1
            value: 71.9072
          - type: nauc_map_at_3_max
            value: 54.108599999999996
          - type: nauc_map_at_3_std
            value: 2.0995
          - type: nauc_map_at_3_diff1
            value: 69.3459
          - type: nauc_map_at_5_max
            value: 55.257
          - type: nauc_map_at_5_std
            value: 5.5776
          - type: nauc_map_at_5_diff1
            value: 68.3314
          - type: nauc_map_at_10_max
            value: 57.1506
          - type: nauc_map_at_10_std
            value: 7.4561
          - type: nauc_map_at_10_diff1
            value: 68.8482
          - type: nauc_map_at_20_max
            value: 57.126200000000004
          - type: nauc_map_at_20_std
            value: 7.6833
          - type: nauc_map_at_20_diff1
            value: 68.9132
          - type: nauc_map_at_100_max
            value: 56.9874
          - type: nauc_map_at_100_std
            value: 7.7405
          - type: nauc_map_at_100_diff1
            value: 68.9371
          - type: nauc_map_at_1000_max
            value: 56.959199999999996
          - type: nauc_map_at_1000_std
            value: 7.709499999999999
          - type: nauc_map_at_1000_diff1
            value: 68.9444
          - type: nauc_recall_at_1_max
            value: 49.762299999999996
          - type: nauc_recall_at_1_std
            value: -0.2785
          - type: nauc_recall_at_1_diff1
            value: 71.9072
          - type: nauc_recall_at_3_max
            value: 58.22580000000001
          - type: nauc_recall_at_3_std
            value: 2.3135
          - type: nauc_recall_at_3_diff1
            value: 65.5868
          - type: nauc_recall_at_5_max
            value: 60.4096
          - type: nauc_recall_at_5_std
            value: 11.7662
          - type: nauc_recall_at_5_diff1
            value: 61.5815
          - type: nauc_recall_at_10_max
            value: 72.74629999999999
          - type: nauc_recall_at_10_std
            value: 22.148
          - type: nauc_recall_at_10_diff1
            value: 62.2401
          - type: nauc_recall_at_20_max
            value: 74.9625
          - type: nauc_recall_at_20_std
            value: 28.1358
          - type: nauc_recall_at_20_diff1
            value: 63.240700000000004
          - type: nauc_recall_at_100_max
            value: 79.15910000000001
          - type: nauc_recall_at_100_std
            value: 39.4162
          - type: nauc_recall_at_100_diff1
            value: 65.733
          - type: nauc_recall_at_1000_max
            value: 100
          - type: nauc_recall_at_1000_std
            value: 72.2222
          - type: nauc_recall_at_1000_diff1
            value: 72.2222
          - type: nauc_precision_at_1_max
            value: 53.481500000000004
          - type: nauc_precision_at_1_std
            value: 8.227
          - type: nauc_precision_at_1_diff1
            value: 72.0771
          - type: nauc_precision_at_3_max
            value: 55.675799999999995
          - type: nauc_precision_at_3_std
            value: 23.9615
          - type: nauc_precision_at_3_diff1
            value: 48.1199
          - type: nauc_precision_at_5_max
            value: 50.503299999999996
          - type: nauc_precision_at_5_std
            value: 36.9259
          - type: nauc_precision_at_5_diff1
            value: 31.769399999999997
          - type: nauc_precision_at_10_max
            value: 45.4878
          - type: nauc_precision_at_10_std
            value: 44.0469
          - type: nauc_precision_at_10_diff1
            value: 16.666900000000002
          - type: nauc_precision_at_20_max
            value: 40.2908
          - type: nauc_precision_at_20_std
            value: 47.330600000000004
          - type: nauc_precision_at_20_diff1
            value: 11.0043
          - type: nauc_precision_at_100_max
            value: 27.4643
          - type: nauc_precision_at_100_std
            value: 53.0014
          - type: nauc_precision_at_100_diff1
            value: -4.8238
          - type: nauc_precision_at_1000_max
            value: 15.755099999999999
          - type: nauc_precision_at_1000_std
            value: 56.634499999999996
          - type: nauc_precision_at_1000_diff1
            value: -21.124100000000002
          - type: nauc_mrr_at_1_max
            value: 53.481500000000004
          - type: nauc_mrr_at_1_std
            value: 8.227
          - type: nauc_mrr_at_1_diff1
            value: 72.0771
          - type: nauc_mrr_at_3_max
            value: 57.6662
          - type: nauc_mrr_at_3_std
            value: 9.2816
          - type: nauc_mrr_at_3_diff1
            value: 69.8276
          - type: nauc_mrr_at_5_max
            value: 57.6565
          - type: nauc_mrr_at_5_std
            value: 10.422099999999999
          - type: nauc_mrr_at_5_diff1
            value: 69.0964
          - type: nauc_mrr_at_10_max
            value: 58.000099999999996
          - type: nauc_mrr_at_10_std
            value: 10.957600000000001
          - type: nauc_mrr_at_10_diff1
            value: 69.0098
          - type: nauc_mrr_at_20_max
            value: 58.0066
          - type: nauc_mrr_at_20_std
            value: 11.0139
          - type: nauc_mrr_at_20_diff1
            value: 69.1278
          - type: nauc_mrr_at_100_max
            value: 57.9072
          - type: nauc_mrr_at_100_std
            value: 10.9621
          - type: nauc_mrr_at_100_diff1
            value: 69.1925
          - type: nauc_mrr_at_1000_max
            value: 57.87949999999999
          - type: nauc_mrr_at_1000_std
            value: 10.934199999999999
          - type: nauc_mrr_at_1000_diff1
            value: 69.2004
          - type: main_score
            value: 71.8
        task:
          type: 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
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            value: 99.8257
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          revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
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          type: mteb/stackexchange-clustering
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          name: MTEB StackExchangeClusteringP2P (default)
          revision: 815ca46b2622cec33ccafc3735d572c266efdb44
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          type: mteb/stackexchange-clustering-p2p
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      - dataset:
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          name: MTEB StackOverflowDupQuestions (default)
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          split: test
          type: mteb/stackoverflowdupquestions-reranking
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          - type: nauc_mrr_at_5_diff1
            value: 15.978200000000001
          - type: nauc_mrr_at_10_max
            value: -31.7904
          - type: nauc_mrr_at_10_std
            value: -19.169900000000002
          - type: nauc_mrr_at_10_diff1
            value: 17.762700000000002
          - type: nauc_mrr_at_20_max
            value: -30.44
          - type: nauc_mrr_at_20_std
            value: -20.2867
          - type: nauc_mrr_at_20_diff1
            value: 18.895500000000002
          - type: nauc_mrr_at_100_max
            value: -30.5404
          - type: nauc_mrr_at_100_std
            value: -20.5699
          - type: nauc_mrr_at_100_diff1
            value: 18.7046
          - type: nauc_mrr_at_1000_max
            value: -30.5404
          - type: nauc_mrr_at_1000_std
            value: -20.5699
          - type: nauc_mrr_at_1000_diff1
            value: 18.7046
          - type: main_score
            value: 29.767
        task:
          type: 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

Snowflake's Arctic-embed-m-v2.0

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

News

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-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 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

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).

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. The released models can be used for commercial purposes free of charge.