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Snowflake Arctic Embed M V2.0
S
Snowflake Arctic Embed M V2.0
Snowflakeによって開発
Snowflake Arctic Embed M v2.0 は50以上の言語をサポートする多言語文埋め込みモデルで、文の類似性と特徴抽出タスクに特化しています。
ダウンロード数 92.30k
リリース時間 : 11/8/2024
モデル概要
このモデルは効率的な文トランスフォーマーで、テキストを高品質な埋め込みベクトルに変換でき、文類似度計算、情報検索、テキスト分類などの様々な自然言語処理タスクに適しています。
モデル特徴
多言語サポート
50以上の言語をサポートし、グローバルなアプリケーションシナリオに適しています。
高性能文埋め込み
高品質な文埋め込みベクトルを生成でき、様々な自然言語処理タスクに適用可能です。
広範な評価ベンチマーク
MTEBなどの複数のベンチマークテストで優れた性能を発揮し、分類、クラスタリング、検索などの多様なタスクをカバーしています。
モデル能力
文類似度計算
特徴抽出
テキスト分類
情報検索
テキストクラスタリング
使用事例
電子商取引
製品レビュー分類
AmazonなどのECプラットフォームの製品レビューを分類し、肯定的・否定的評価を識別します。
Amazon極性分類タスクで70.36%の精度を達成。
カスタマーサポート
重複問題検出
カスタマーサポートプラットフォームの重複問題を識別し、サポート効率を向上させます。
AskUbuntu重複問題タスクでMAP60.65%を達成。
学術研究
論文クラスタリング
学術論文をクラスタリングし、研究者が関連研究を発見するのを支援します。
論文クラスタリングP2PタスクでV-measure44.68%を達成。
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:
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- 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
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- type: mrr_at_10 value: 49.264599999999994
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- type: nauc_mrr_at_1000_std value: -16.095100000000002
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- 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:
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- 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:
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- type: ndcg_at_10 value: 56.31
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- type: recall_at_1 value: 37.732
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- 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
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revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
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name: MTEB STS17 (es-en)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
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revision: faeb762787bd10488a50c8b5be4a3b82e411949c
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type: mteb/sts17-crosslingual-sts
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name: MTEB STS17 (en-en)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
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type: mteb/sts17-crosslingual-sts
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name: MTEB STS17 (en-tr)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
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revision: faeb762787bd10488a50c8b5be4a3b82e411949c
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revision: faeb762787bd10488a50c8b5be4a3b82e411949c
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revision: faeb762787bd10488a50c8b5be4a3b82e411949c
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revision: faeb762787bd10488a50c8b5be4a3b82e411949c
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revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
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revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
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revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
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name: MTEB STSBenchmark (default)
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
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revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
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type: mteb/scidocs-reranking
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- type: nauc_recall_at_5_diff1 value: 8.3249
- type: nauc_recall_at_10_max value: -13.3968
- type: nauc_recall_at_10_std value: -19.2107
- type: nauc_recall_at_10_diff1 value: 0.1315
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- type: nauc_recall_at_20_std value: -10.005500000000001
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- type: nauc_recall_at_100_std value: 33.5358
- type: nauc_recall_at_100_diff1 value: -10.4876
- type: nauc_recall_at_1000_max value: 1.8395000000000001
- type: nauc_recall_at_1000_std value: 70.462
- type: nauc_recall_at_1000_diff1 value: -23.4072
- type: nauc_precision_at_1_max value: -23.0917
- type: nauc_precision_at_1_std value: -8.036999999999999
- type: nauc_precision_at_1_diff1 value: 19.354599999999998
- type: nauc_precision_at_3_max value: -11.3547
- type: nauc_precision_at_3_std value: -30.2495
- type: nauc_precision_at_3_diff1 value: 20.3126
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- type: nauc_precision_at_5_std value: -24.8896
- type: nauc_precision_at_5_diff1 value: 15.6276
- type: nauc_precision_at_10_max value: -11.5796
- type: nauc_precision_at_10_std value: -2.3662
- type: nauc_precision_at_10_diff1 value: 3.8091
- type: nauc_precision_at_20_max value: -11.9042
- type: nauc_precision_at_20_std value: 15.6577
- type: nauc_precision_at_20_diff1 value: -8.8878
- type: nauc_precision_at_100_max value: -0.5217
- type: nauc_precision_at_100_std value: 71.8387
- type: nauc_precision_at_100_diff1 value: -16.8714
- type: nauc_precision_at_1000_max value: 36.234300000000005
- type: nauc_precision_at_1000_std value: 37.5447
- type: nauc_precision_at_1000_diff1 value: -20.7229
- type: nauc_mrr_at_1_max value: -23.0917
- type: nauc_mrr_at_1_std value: -8.036999999999999
- type: nauc_mrr_at_1_diff1 value: 19.354599999999998
- type: nauc_mrr_at_3_max value: -27.9937
- type: nauc_mrr_at_3_std value: -26.519900000000003
- type: nauc_mrr_at_3_diff1 value: 20.288
- type: nauc_mrr_at_5_max value: -33.218599999999995
- type: nauc_mrr_at_5_std value: -23.857400000000002
- type: nauc_mrr_at_5_diff1 value: 15.978200000000001
- type: nauc_mrr_at_10_max value: -31.7904
- type: nauc_mrr_at_10_std value: -19.169900000000002
- type: nauc_mrr_at_10_diff1 value: 17.762700000000002
- type: nauc_mrr_at_20_max value: -30.44
- type: nauc_mrr_at_20_std value: -20.2867
- type: nauc_mrr_at_20_diff1 value: 18.895500000000002
- type: nauc_mrr_at_100_max value: -30.5404
- type: nauc_mrr_at_100_std value: -20.5699
- type: nauc_mrr_at_100_diff1 value: 18.7046
- type: nauc_mrr_at_1000_max value: -30.5404
- type: nauc_mrr_at_1000_std value: -20.5699
- type: nauc_mrr_at_1000_diff1 value: 18.7046
- type: main_score value: 29.767 task: type: 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
- dataset:
config: en-ext
name: MTEB AmazonCounterfactualClassification (en-ext)
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
split: test
type: mteb/amazon_counterfactual
metrics:
Snowflake's Arctic-embed-m-v2.0
News | Models | Usage | Evaluation | Contact | FAQ License | Acknowledgement
News
- 12/11/2024: Release of Technical Report
- 12/04/2024: Release of snowflake-arctic-embed-l-v2.0 and snowflake-arctic-embed-m-v2.0 our newest models with multilingual workloads in mind.
Models
Snowflake arctic-embed-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:
-
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.
-
Inference efficiency: Its 113m non-embedding parameters inference is fast and efficient for any scale.
-
Compression-friendly: Achieves high-quality retrieval with embeddings as small as 128 bytes/vector using Matryoshka Representation Learning (MRL) and quantization-aware embedding training. Please note that like our v1.5 model, the MRL for this model is 256 dimensions, and high-quality 128-byte compression is achieved via 4-bit quantization (e.g. using a
pq256x4fsfast-scan FAISS index or using the example code published alongside our 1.5 model). -
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)
Using Huggingface Transformers.js
If you haven't already, you can install the Transformers.js JavaScript library from NPM using:
npm i @huggingface/transformers
You can then use the model for retrieval, as follows:
import { pipeline, dot } from '@huggingface/transformers';
// Create feature extraction pipeline
const extractor = await pipeline('feature-extraction', 'Snowflake/snowflake-arctic-embed-m-v2.0');
// Generate sentence embeddings
const sentences = [
'query: what is snowflake?',
'The Data Cloud!',
'Mexico City of Course!',
]
const output = await extractor(sentences, { normalize: true, pooling: 'cls' });
// Compute similarity scores
const [source_embeddings, ...document_embeddings ] = output.tolist();
const similarities = document_embeddings.map(x => dot(source_embeddings, x));
console.log(similarities); // [0.32719788157046004, 0.06960141111667434]
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.
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