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Snowflake Arctic Embed M V2.0 Cpu
S
Snowflake Arctic Embed M V2.0 Cpu
cnmoroによって開発
Snowflake Arctic Embed M v2.0 は多言語文埋め込みモデルで、文類似度タスクに特化しており、50以上の言語をサポートしています。
ダウンロード数 502
リリース時間 : 1/22/2025
モデル概要
このモデルは文変換器で、特徴抽出と文類似度計算に使用され、MTEBベンチマークテストで良好な性能を示します。
モデル特徴
多言語サポート
50以上の言語の文埋め込み計算をサポート
高性能
MTEBベンチマークテストで優れた性能を発揮
文類似度
文間の類似度計算に特化して最適化
モデル能力
文埋め込み
特徴抽出
文類似度計算
多言語処理
使用事例
テキスト検索
意味検索
キーワードではなく意味に基づく文書検索
分類タスク
感情分析
文埋め込みに基づく感情分類
Amazonレビュー分類タスクで33.766%の精度を達成
質問応答システム
質問回答マッチング
質問と候補回答間の類似度計算
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
- type: mrr_at_5 value: 47.131299999999996
- type: mrr_at_10 value: 49.264599999999994
- type: mrr_at_20 value: 49.8707
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- type: nauc_mrr_at_1000_std value: -16.095100000000002
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- 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:
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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_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
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revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
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type: mteb/sickr-sts
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revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
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revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
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name: MTEB STS17 (es-en)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
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name: MTEB STS17 (fr-en)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
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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type: mteb/sts17-crosslingual-sts
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name: MTEB STS17 (it-en)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
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type: mteb/sts17-crosslingual-sts
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revision: faeb762787bd10488a50c8b5be4a3b82e411949c
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name: MTEB STS17 (en-ar)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
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name: MTEB STS17 (en-de)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
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type: mteb/sts17-crosslingual-sts
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name: MTEB STS22 (zh-en)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
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revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
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revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
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config: default
name: MTEB STSBenchmark (default)
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
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name: MTEB SciDocsRR (default)
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
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type: mteb/scidocs-reranking
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- type: nauc_precision_at_3_std value: -30.2495
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- type: nauc_precision_at_5_std value: -24.8896
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- type: nauc_precision_at_10_std value: -2.3662
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- type: nauc_precision_at_20_std value: 15.6577
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- type: nauc_precision_at_100_std value: 71.8387
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- type: nauc_precision_at_1000_std value: 37.5447
- type: nauc_precision_at_1000_diff1 value: -20.7229
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- 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
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- type: nauc_mrr_at_5_std value: -23.857400000000002
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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
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- 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:
A modified version of Snowflake/snowflake-arctic-embed-m-v2.0, without xformers, so it works on CPU.
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