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JA
Snowflake Arctic Embed M
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Snowflake Arctic Embed M
Snowflakeによって開発
Snowflake Arctic Embed M は文類似度タスクに特化した文変換モデルで、効率的にテキスト特徴を抽出し文間の類似度を計算できます。
ダウンロード数 722.08k
リリース時間 : 4/11/2024
モデル概要
このモデルは主に文類似度計算と特徴抽出に使用され、複数の標準データセットで優れた性能を発揮し、様々な自然言語処理タスクをサポートします。
モデル特徴
効率的な文類似度計算
複数の標準データセットで優れた性能を発揮し、文間の意味的類似度を正確に計算可能
マルチタスクサポート
分類、クラスタリング、検索など様々な自然言語処理タスクをサポート
MTEBベンチマーク検証
MTEBの複数ベンチマークで優れた結果を達成し、モデルの広範な適用性を実証
モデル能力
文類似度計算
テキスト特徴抽出
テキスト分類
テキストクラスタリング
情報検索
使用事例
電子商取引
製品レビュー分類
Amazon製品レビューの感情分類
AmazonPolarityデータセットで82.83%の精度を達成
反事実的レビュー検出
Amazon上の反事実的レビューを識別
AmazonCounterfactualデータセットで76.81%の精度を達成
金融
銀行カスタマーサポート質問分類
銀行顧客の質問を自動分類
Banking77データセットで80.34%の精度を達成
学術研究
論文クラスタリング
arXivとbiorxiv論文の主題クラスタリング
ArxivClusteringP2PデータセットでV-measure47.24%を達成
pipeline_tag: sentence-similarity tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- mteb
- arctic
- snowflake-arctic-embed
- transformers.js model-index:
- name: snowflake-arctic-embed-m
results:
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
- type: accuracy value: 76.80597014925374
- type: ap value: 39.31198155789558
- type: f1 value: 70.48198448222148
- task:
type: Classification
dataset:
type: mteb/amazon_polarity
name: MTEB AmazonPolarityClassification
config: default
split: test
revision: e2d317d38cd51312af73b3d32a06d1a08b442046
metrics:
- type: accuracy value: 82.831525
- type: ap value: 77.4474050181638
- type: f1 value: 82.77204845110204
- task:
type: Classification
dataset:
type: mteb/amazon_reviews_multi
name: MTEB AmazonReviewsClassification (en)
config: en
split: test
revision: 1399c76144fd37290681b995c656ef9b2e06e26d
metrics:
- type: accuracy value: 38.93000000000001
- type: f1 value: 37.98013371053459
- task:
type: Retrieval
dataset:
type: mteb/arguana
name: MTEB ArguAna
config: default
split: test
revision: c22ab2a51041ffd869aaddef7af8d8215647e41a
metrics:
- type: map_at_1 value: 31.223
- type: map_at_10 value: 47.43
- type: map_at_100 value: 48.208
- type: map_at_1000 value: 48.211
- type: map_at_3 value: 42.579
- type: map_at_5 value: 45.263999999999996
- type: mrr_at_1 value: 31.65
- type: mrr_at_10 value: 47.573
- type: mrr_at_100 value: 48.359
- type: mrr_at_1000 value: 48.362
- type: mrr_at_3 value: 42.734
- type: mrr_at_5 value: 45.415
- type: ndcg_at_1 value: 31.223
- type: ndcg_at_10 value: 56.436
- type: ndcg_at_100 value: 59.657000000000004
- type: ndcg_at_1000 value: 59.731
- type: ndcg_at_3 value: 46.327
- type: ndcg_at_5 value: 51.178000000000004
- type: precision_at_1 value: 31.223
- type: precision_at_10 value: 8.527999999999999
- type: precision_at_100 value: 0.991
- type: precision_at_1000 value: 0.1
- type: precision_at_3 value: 19.061
- type: precision_at_5 value: 13.797999999999998
- type: recall_at_1 value: 31.223
- type: recall_at_10 value: 85.277
- type: recall_at_100 value: 99.075
- type: recall_at_1000 value: 99.644
- type: recall_at_3 value: 57.18299999999999
- type: recall_at_5 value: 68.99
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-p2p
name: MTEB ArxivClusteringP2P
config: default
split: test
revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d
metrics:
- type: v_measure value: 47.23625429411296
- task:
type: Clustering
dataset:
type: mteb/arxiv-clustering-s2s
name: MTEB ArxivClusteringS2S
config: default
split: test
revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53
metrics:
- type: v_measure value: 37.433880471403654
- task:
type: Reranking
dataset:
type: mteb/askubuntudupquestions-reranking
name: MTEB AskUbuntuDupQuestions
config: default
split: test
revision: 2000358ca161889fa9c082cb41daa8dcfb161a54
metrics:
- type: map value: 60.53175025582013
- type: mrr value: 74.51160796728664
- task:
type: STS
dataset:
type: mteb/biosses-sts
name: MTEB BIOSSES
config: default
split: test
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
metrics:
- type: cos_sim_pearson value: 88.93746103286769
- type: cos_sim_spearman value: 86.62245567912619
- type: euclidean_pearson value: 87.154173907501
- type: euclidean_spearman value: 86.62245567912619
- type: manhattan_pearson value: 87.17682026633462
- type: manhattan_spearman value: 86.74775973908348
- task:
type: Classification
dataset:
type: mteb/banking77
name: MTEB Banking77Classification
config: default
split: test
revision: 0fd18e25b25c072e09e0d92ab615fda904d66300
metrics:
- type: accuracy value: 80.33766233766232
- type: f1 value: 79.64931422442245
- task:
type: Clustering
dataset:
type: jinaai/big-patent-clustering
name: MTEB BigPatentClustering
config: default
split: test
revision: 62d5330920bca426ce9d3c76ea914f15fc83e891
metrics:
- type: v_measure value: 19.116028913890613
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-p2p
name: MTEB BiorxivClusteringP2P
config: default
split: test
revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40
metrics:
- type: v_measure value: 36.966921852810174
- task:
type: Clustering
dataset:
type: mteb/biorxiv-clustering-s2s
name: MTEB BiorxivClusteringS2S
config: default
split: test
revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908
metrics:
- type: v_measure value: 31.98019698537654
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-android
name: MTEB CQADupstackAndroidRetrieval
config: default
split: test
revision: f46a197baaae43b4f621051089b82a364682dfeb
metrics:
- type: map_at_1 value: 34.079
- type: map_at_10 value: 46.35
- type: map_at_100 value: 47.785
- type: map_at_1000 value: 47.903
- type: map_at_3 value: 42.620999999999995
- type: map_at_5 value: 44.765
- type: mrr_at_1 value: 41.345
- type: mrr_at_10 value: 52.032000000000004
- type: mrr_at_100 value: 52.690000000000005
- type: mrr_at_1000 value: 52.727999999999994
- type: mrr_at_3 value: 49.428
- type: mrr_at_5 value: 51.093999999999994
- type: ndcg_at_1 value: 41.345
- type: ndcg_at_10 value: 53.027
- type: ndcg_at_100 value: 57.962
- type: ndcg_at_1000 value: 59.611999999999995
- type: ndcg_at_3 value: 47.687000000000005
- type: ndcg_at_5 value: 50.367
- type: precision_at_1 value: 41.345
- type: precision_at_10 value: 10.157
- type: precision_at_100 value: 1.567
- type: precision_at_1000 value: 0.199
- type: precision_at_3 value: 23.081
- type: precision_at_5 value: 16.738
- type: recall_at_1 value: 34.079
- type: recall_at_10 value: 65.93900000000001
- type: recall_at_100 value: 86.42699999999999
- type: recall_at_1000 value: 96.61
- type: recall_at_3 value: 50.56699999999999
- type: recall_at_5 value: 57.82000000000001
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-english
name: MTEB CQADupstackEnglishRetrieval
config: default
split: test
revision: ad9991cb51e31e31e430383c75ffb2885547b5f0
metrics:
- type: map_at_1 value: 33.289
- type: map_at_10 value: 43.681
- type: map_at_100 value: 45.056000000000004
- type: map_at_1000 value: 45.171
- type: map_at_3 value: 40.702
- type: map_at_5 value: 42.292
- type: mrr_at_1 value: 41.146
- type: mrr_at_10 value: 49.604
- type: mrr_at_100 value: 50.28399999999999
- type: mrr_at_1000 value: 50.322
- type: mrr_at_3 value: 47.611
- type: mrr_at_5 value: 48.717
- type: ndcg_at_1 value: 41.146
- type: ndcg_at_10 value: 49.43
- type: ndcg_at_100 value: 54.01899999999999
- type: ndcg_at_1000 value: 55.803000000000004
- type: ndcg_at_3 value: 45.503
- type: ndcg_at_5 value: 47.198
- type: precision_at_1 value: 41.146
- type: precision_at_10 value: 9.268
- type: precision_at_100 value: 1.4749999999999999
- type: precision_at_1000 value: 0.19
- type: precision_at_3 value: 21.932
- type: precision_at_5 value: 15.389
- type: recall_at_1 value: 33.289
- type: recall_at_10 value: 59.209999999999994
- type: recall_at_100 value: 78.676
- type: recall_at_1000 value: 89.84100000000001
- type: recall_at_3 value: 47.351
- type: recall_at_5 value: 52.178999999999995
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-gaming
name: MTEB CQADupstackGamingRetrieval
config: default
split: test
revision: 4885aa143210c98657558c04aaf3dc47cfb54340
metrics:
- type: map_at_1 value: 44.483
- type: map_at_10 value: 56.862
- type: map_at_100 value: 57.901
- type: map_at_1000 value: 57.948
- type: map_at_3 value: 53.737
- type: map_at_5 value: 55.64
- type: mrr_at_1 value: 50.658
- type: mrr_at_10 value: 60.281
- type: mrr_at_100 value: 60.946
- type: mrr_at_1000 value: 60.967000000000006
- type: mrr_at_3 value: 58.192
- type: mrr_at_5 value: 59.531
- type: ndcg_at_1 value: 50.658
- type: ndcg_at_10 value: 62.339
- type: ndcg_at_100 value: 66.28399999999999
- type: ndcg_at_1000 value: 67.166
- type: ndcg_at_3 value: 57.458
- type: ndcg_at_5 value: 60.112
- type: precision_at_1 value: 50.658
- type: precision_at_10 value: 9.762
- type: precision_at_100 value: 1.26
- type: precision_at_1000 value: 0.13799999999999998
- type: precision_at_3 value: 25.329
- type: precision_at_5 value: 17.254
- type: recall_at_1 value: 44.483
- type: recall_at_10 value: 74.819
- type: recall_at_100 value: 91.702
- type: recall_at_1000 value: 97.84
- type: recall_at_3 value: 62.13999999999999
- type: recall_at_5 value: 68.569
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-gis
name: MTEB CQADupstackGisRetrieval
config: default
split: test
revision: 5003b3064772da1887988e05400cf3806fe491f2
metrics:
- type: map_at_1 value: 26.489
- type: map_at_10 value: 37.004999999999995
- type: map_at_100 value: 38.001000000000005
- type: map_at_1000 value: 38.085
- type: map_at_3 value: 34.239999999999995
- type: map_at_5 value: 35.934
- type: mrr_at_1 value: 28.362
- type: mrr_at_10 value: 38.807
- type: mrr_at_100 value: 39.671
- type: mrr_at_1000 value: 39.736
- type: mrr_at_3 value: 36.29
- type: mrr_at_5 value: 37.906
- type: ndcg_at_1 value: 28.362
- type: ndcg_at_10 value: 42.510999999999996
- type: ndcg_at_100 value: 47.226
- type: ndcg_at_1000 value: 49.226
- type: ndcg_at_3 value: 37.295
- type: ndcg_at_5 value: 40.165
- type: precision_at_1 value: 28.362
- type: precision_at_10 value: 6.633
- type: precision_at_100 value: 0.9490000000000001
- type: precision_at_1000 value: 0.11499999999999999
- type: precision_at_3 value: 16.234
- type: precision_at_5 value: 11.434999999999999
- type: recall_at_1 value: 26.489
- type: recall_at_10 value: 57.457
- type: recall_at_100 value: 78.712
- type: recall_at_1000 value: 93.565
- type: recall_at_3 value: 43.748
- type: recall_at_5 value: 50.589
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-mathematica
name: MTEB CQADupstackMathematicaRetrieval
config: default
split: test
revision: 90fceea13679c63fe563ded68f3b6f06e50061de
metrics:
- type: map_at_1 value: 12.418999999999999
- type: map_at_10 value: 22.866
- type: map_at_100 value: 24.365000000000002
- type: map_at_1000 value: 24.479
- type: map_at_3 value: 19.965
- type: map_at_5 value: 21.684
- type: mrr_at_1 value: 14.677000000000001
- type: mrr_at_10 value: 26.316
- type: mrr_at_100 value: 27.514
- type: mrr_at_1000 value: 27.57
- type: mrr_at_3 value: 23.3
- type: mrr_at_5 value: 25.191000000000003
- type: ndcg_at_1 value: 14.677000000000001
- type: ndcg_at_10 value: 28.875
- type: ndcg_at_100 value: 35.607
- type: ndcg_at_1000 value: 38.237
- type: ndcg_at_3 value: 23.284
- type: ndcg_at_5 value: 26.226
- type: precision_at_1 value: 14.677000000000001
- type: precision_at_10 value: 5.771
- type: precision_at_100 value: 1.058
- type: precision_at_1000 value: 0.14200000000000002
- type: precision_at_3 value: 11.940000000000001
- type: precision_at_5 value: 9.229
- type: recall_at_1 value: 12.418999999999999
- type: recall_at_10 value: 43.333
- type: recall_at_100 value: 71.942
- type: recall_at_1000 value: 90.67399999999999
- type: recall_at_3 value: 28.787000000000003
- type: recall_at_5 value: 35.638
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-physics
name: MTEB CQADupstackPhysicsRetrieval
config: default
split: test
revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4
metrics:
- type: map_at_1 value: 31.686999999999998
- type: map_at_10 value: 42.331
- type: map_at_100 value: 43.655
- type: map_at_1000 value: 43.771
- type: map_at_3 value: 38.944
- type: map_at_5 value: 40.991
- type: mrr_at_1 value: 37.921
- type: mrr_at_10 value: 47.534
- type: mrr_at_100 value: 48.362
- type: mrr_at_1000 value: 48.405
- type: mrr_at_3 value: 44.995000000000005
- type: mrr_at_5 value: 46.617
- type: ndcg_at_1 value: 37.921
- type: ndcg_at_10 value: 48.236000000000004
- type: ndcg_at_100 value: 53.705000000000005
- type: ndcg_at_1000 value: 55.596000000000004
- type: ndcg_at_3 value: 43.11
- type: ndcg_at_5 value: 45.862
- type: precision_at_1 value: 37.921
- type: precision_at_10 value: 8.643
- type: precision_at_100 value: 1.336
- type: precision_at_1000 value: 0.166
- type: precision_at_3 value: 20.308
- type: precision_at_5 value: 14.514
- type: recall_at_1 value: 31.686999999999998
- type: recall_at_10 value: 60.126999999999995
- type: recall_at_100 value: 83.10600000000001
- type: recall_at_1000 value: 95.15
- type: recall_at_3 value: 46.098
- type: recall_at_5 value: 53.179
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-programmers
name: MTEB CQADupstackProgrammersRetrieval
config: default
split: test
revision: 6184bc1440d2dbc7612be22b50686b8826d22b32
metrics:
- type: map_at_1 value: 28.686
- type: map_at_10 value: 39.146
- type: map_at_100 value: 40.543
- type: map_at_1000 value: 40.644999999999996
- type: map_at_3 value: 36.195
- type: map_at_5 value: 37.919000000000004
- type: mrr_at_1 value: 35.160000000000004
- type: mrr_at_10 value: 44.711
- type: mrr_at_100 value: 45.609
- type: mrr_at_1000 value: 45.655
- type: mrr_at_3 value: 42.409
- type: mrr_at_5 value: 43.779
- type: ndcg_at_1 value: 35.160000000000004
- type: ndcg_at_10 value: 44.977000000000004
- type: ndcg_at_100 value: 50.663000000000004
- type: ndcg_at_1000 value: 52.794
- type: ndcg_at_3 value: 40.532000000000004
- type: ndcg_at_5 value: 42.641
- type: precision_at_1 value: 35.160000000000004
- type: precision_at_10 value: 8.014000000000001
- type: precision_at_100 value: 1.269
- type: precision_at_1000 value: 0.163
- type: precision_at_3 value: 19.444
- type: precision_at_5 value: 13.653
- type: recall_at_1 value: 28.686
- type: recall_at_10 value: 56.801
- type: recall_at_100 value: 80.559
- type: recall_at_1000 value: 95.052
- type: recall_at_3 value: 43.675999999999995
- type: recall_at_5 value: 49.703
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack
name: MTEB CQADupstackRetrieval
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: map_at_1 value: 28.173833333333338
- type: map_at_10 value: 38.202083333333334
- type: map_at_100 value: 39.47475
- type: map_at_1000 value: 39.586499999999994
- type: map_at_3 value: 35.17308333333334
- type: map_at_5 value: 36.914
- type: mrr_at_1 value: 32.92958333333333
- type: mrr_at_10 value: 42.16758333333333
- type: mrr_at_100 value: 43.04108333333333
- type: mrr_at_1000 value: 43.092499999999994
- type: mrr_at_3 value: 39.69166666666666
- type: mrr_at_5 value: 41.19458333333333
- type: ndcg_at_1 value: 32.92958333333333
- type: ndcg_at_10 value: 43.80583333333333
- type: ndcg_at_100 value: 49.060916666666664
- type: ndcg_at_1000 value: 51.127250000000004
- type: ndcg_at_3 value: 38.80383333333333
- type: ndcg_at_5 value: 41.29658333333333
- type: precision_at_1 value: 32.92958333333333
- type: precision_at_10 value: 7.655666666666666
- type: precision_at_100 value: 1.2094166666666668
- type: precision_at_1000 value: 0.15750000000000003
- type: precision_at_3 value: 17.87975
- type: precision_at_5 value: 12.741833333333332
- type: recall_at_1 value: 28.173833333333338
- type: recall_at_10 value: 56.219249999999995
- type: recall_at_100 value: 79.01416666666665
- type: recall_at_1000 value: 93.13425000000001
- type: recall_at_3 value: 42.39241666666667
- type: recall_at_5 value: 48.764833333333335
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-stats
name: MTEB CQADupstackStatsRetrieval
config: default
split: test
revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a
metrics:
- type: map_at_1 value: 25.625999999999998
- type: map_at_10 value: 32.808
- type: map_at_100 value: 33.951
- type: map_at_1000 value: 34.052
- type: map_at_3 value: 30.536
- type: map_at_5 value: 31.77
- type: mrr_at_1 value: 28.374
- type: mrr_at_10 value: 35.527
- type: mrr_at_100 value: 36.451
- type: mrr_at_1000 value: 36.522
- type: mrr_at_3 value: 33.410000000000004
- type: mrr_at_5 value: 34.537
- type: ndcg_at_1 value: 28.374
- type: ndcg_at_10 value: 37.172
- type: ndcg_at_100 value: 42.474000000000004
- type: ndcg_at_1000 value: 44.853
- type: ndcg_at_3 value: 32.931
- type: ndcg_at_5 value: 34.882999999999996
- type: precision_at_1 value: 28.374
- type: precision_at_10 value: 5.813
- type: precision_at_100 value: 0.928
- type: precision_at_1000 value: 0.121
- type: precision_at_3 value: 14.008000000000001
- type: precision_at_5 value: 9.754999999999999
- type: recall_at_1 value: 25.625999999999998
- type: recall_at_10 value: 47.812
- type: recall_at_100 value: 71.61800000000001
- type: recall_at_1000 value: 88.881
- type: recall_at_3 value: 35.876999999999995
- type: recall_at_5 value: 40.839
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-tex
name: MTEB CQADupstackTexRetrieval
config: default
split: test
revision: 46989137a86843e03a6195de44b09deda022eec7
metrics:
- type: map_at_1 value: 18.233
- type: map_at_10 value: 26.375999999999998
- type: map_at_100 value: 27.575
- type: map_at_1000 value: 27.706999999999997
- type: map_at_3 value: 23.619
- type: map_at_5 value: 25.217
- type: mrr_at_1 value: 22.023
- type: mrr_at_10 value: 30.122
- type: mrr_at_100 value: 31.083
- type: mrr_at_1000 value: 31.163999999999998
- type: mrr_at_3 value: 27.541
- type: mrr_at_5 value: 29.061999999999998
- type: ndcg_at_1 value: 22.023
- type: ndcg_at_10 value: 31.476
- type: ndcg_at_100 value: 37.114000000000004
- type: ndcg_at_1000 value: 39.981
- type: ndcg_at_3 value: 26.538
- type: ndcg_at_5 value: 29.016
- type: precision_at_1 value: 22.023
- type: precision_at_10 value: 5.819
- type: precision_at_100 value: 1.018
- type: precision_at_1000 value: 0.14300000000000002
- type: precision_at_3 value: 12.583
- type: precision_at_5 value: 9.36
- type: recall_at_1 value: 18.233
- type: recall_at_10 value: 43.029
- type: recall_at_100 value: 68.253
- type: recall_at_1000 value: 88.319
- type: recall_at_3 value: 29.541
- type: recall_at_5 value: 35.783
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-unix
name: MTEB CQADupstackUnixRetrieval
config: default
split: test
revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53
metrics:
- type: map_at_1 value: 28.923
- type: map_at_10 value: 39.231
- type: map_at_100 value: 40.483000000000004
- type: map_at_1000 value: 40.575
- type: map_at_3 value: 35.94
- type: map_at_5 value: 37.683
- type: mrr_at_1 value: 33.955
- type: mrr_at_10 value: 43.163000000000004
- type: mrr_at_100 value: 44.054
- type: mrr_at_1000 value: 44.099
- type: mrr_at_3 value: 40.361000000000004
- type: mrr_at_5 value: 41.905
- type: ndcg_at_1 value: 33.955
- type: ndcg_at_10 value: 45.068000000000005
- type: ndcg_at_100 value: 50.470000000000006
- type: ndcg_at_1000 value: 52.349000000000004
- type: ndcg_at_3 value: 39.298
- type: ndcg_at_5 value: 41.821999999999996
- type: precision_at_1 value: 33.955
- type: precision_at_10 value: 7.649
- type: precision_at_100 value: 1.173
- type: precision_at_1000 value: 0.14200000000000002
- type: precision_at_3 value: 17.817
- type: precision_at_5 value: 12.537
- type: recall_at_1 value: 28.923
- type: recall_at_10 value: 58.934
- type: recall_at_100 value: 81.809
- type: recall_at_1000 value: 94.71300000000001
- type: recall_at_3 value: 42.975
- type: recall_at_5 value: 49.501
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-webmasters
name: MTEB CQADupstackWebmastersRetrieval
config: default
split: test
revision: 160c094312a0e1facb97e55eeddb698c0abe3571
metrics:
- type: map_at_1 value: 28.596
- type: map_at_10 value: 38.735
- type: map_at_100 value: 40.264
- type: map_at_1000 value: 40.48
- type: map_at_3 value: 35.394999999999996
- type: map_at_5 value: 37.099
- type: mrr_at_1 value: 33.992
- type: mrr_at_10 value: 43.076
- type: mrr_at_100 value: 44.005
- type: mrr_at_1000 value: 44.043
- type: mrr_at_3 value: 40.415
- type: mrr_at_5 value: 41.957
- type: ndcg_at_1 value: 33.992
- type: ndcg_at_10 value: 44.896
- type: ndcg_at_100 value: 50.44499999999999
- type: ndcg_at_1000 value: 52.675000000000004
- type: ndcg_at_3 value: 39.783
- type: ndcg_at_5 value: 41.997
- type: precision_at_1 value: 33.992
- type: precision_at_10 value: 8.498
- type: precision_at_100 value: 1.585
- type: precision_at_1000 value: 0.248
- type: precision_at_3 value: 18.511
- type: precision_at_5 value: 13.241
- type: recall_at_1 value: 28.596
- type: recall_at_10 value: 56.885
- type: recall_at_100 value: 82.306
- type: recall_at_1000 value: 95.813
- type: recall_at_3 value: 42.168
- type: recall_at_5 value: 48.32
- task:
type: Retrieval
dataset:
type: mteb/cqadupstack-wordpress
name: MTEB CQADupstackWordpressRetrieval
config: default
split: test
revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4
metrics:
- type: map_at_1 value: 25.576
- type: map_at_10 value: 33.034
- type: map_at_100 value: 34.117999999999995
- type: map_at_1000 value: 34.222
- type: map_at_3 value: 30.183
- type: map_at_5 value: 31.974000000000004
- type: mrr_at_1 value: 27.542
- type: mrr_at_10 value: 34.838
- type: mrr_at_100 value: 35.824
- type: mrr_at_1000 value: 35.899
- type: mrr_at_3 value: 32.348
- type: mrr_at_5 value: 34.039
- type: ndcg_at_1 value: 27.542
- type: ndcg_at_10 value: 37.663000000000004
- type: ndcg_at_100 value: 42.762
- type: ndcg_at_1000 value: 45.235
- type: ndcg_at_3 value: 32.227
- type: ndcg_at_5 value: 35.27
- type: precision_at_1 value: 27.542
- type: precision_at_10 value: 5.840999999999999
- type: precision_at_100 value: 0.895
- type: precision_at_1000 value: 0.123
- type: precision_at_3 value: 13.370000000000001
- type: precision_at_5 value: 9.797
- type: recall_at_1 value: 25.576
- type: recall_at_10 value: 50.285000000000004
- type: recall_at_100 value: 73.06
- type: recall_at_1000 value: 91.15299999999999
- type: recall_at_3 value: 35.781
- type: recall_at_5 value: 43.058
- task:
type: Retrieval
dataset:
type: mteb/climate-fever
name: MTEB ClimateFEVER
config: default
split: test
revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380
metrics:
- type: map_at_1 value: 17.061
- type: map_at_10 value: 29.464000000000002
- type: map_at_100 value: 31.552999999999997
- type: map_at_1000 value: 31.707
- type: map_at_3 value: 24.834999999999997
- type: map_at_5 value: 27.355
- type: mrr_at_1 value: 38.958
- type: mrr_at_10 value: 51.578
- type: mrr_at_100 value: 52.262
- type: mrr_at_1000 value: 52.283
- type: mrr_at_3 value: 48.599
- type: mrr_at_5 value: 50.404
- type: ndcg_at_1 value: 38.958
- type: ndcg_at_10 value: 39.367999999999995
- type: ndcg_at_100 value: 46.521
- type: ndcg_at_1000 value: 49.086999999999996
- type: ndcg_at_3 value: 33.442
- type: ndcg_at_5 value: 35.515
- type: precision_at_1 value: 38.958
- type: precision_at_10 value: 12.110999999999999
- type: precision_at_100 value: 1.982
- type: precision_at_1000 value: 0.247
- type: precision_at_3 value: 25.102999999999998
- type: precision_at_5 value: 18.971
- type: recall_at_1 value: 17.061
- type: recall_at_10 value: 45.198
- type: recall_at_100 value: 69.18900000000001
- type: recall_at_1000 value: 83.38499999999999
- type: recall_at_3 value: 30.241
- type: recall_at_5 value: 36.851
- task:
type: Retrieval
dataset:
type: mteb/dbpedia
name: MTEB DBPedia
config: default
split: test
revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659
metrics:
- type: map_at_1 value: 9.398
- type: map_at_10 value: 21.421
- type: map_at_100 value: 31.649
- type: map_at_1000 value: 33.469
- type: map_at_3 value: 15.310000000000002
- type: map_at_5 value: 17.946
- type: mrr_at_1 value: 71
- type: mrr_at_10 value: 78.92099999999999
- type: mrr_at_100 value: 79.225
- type: mrr_at_1000 value: 79.23
- type: mrr_at_3 value: 77.792
- type: mrr_at_5 value: 78.467
- type: ndcg_at_1 value: 57.99999999999999
- type: ndcg_at_10 value: 44.733000000000004
- type: ndcg_at_100 value: 50.646
- type: ndcg_at_1000 value: 57.903999999999996
- type: ndcg_at_3 value: 49.175999999999995
- type: ndcg_at_5 value: 46.800999999999995
- type: precision_at_1 value: 71
- type: precision_at_10 value: 36.25
- type: precision_at_100 value: 12.135
- type: precision_at_1000 value: 2.26
- type: precision_at_3 value: 52.75
- type: precision_at_5 value: 45.65
- type: recall_at_1 value: 9.398
- type: recall_at_10 value: 26.596999999999998
- type: recall_at_100 value: 57.943
- type: recall_at_1000 value: 81.147
- type: recall_at_3 value: 16.634
- type: recall_at_5 value: 20.7
- task:
type: Classification
dataset:
type: mteb/emotion
name: MTEB EmotionClassification
config: default
split: test
revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37
metrics:
- type: accuracy value: 46.535000000000004
- type: f1 value: 42.53702746452163
- task:
type: Retrieval
dataset:
type: mteb/fever
name: MTEB FEVER
config: default
split: test
revision: bea83ef9e8fb933d90a2f1d5515737465d613e12
metrics:
- type: map_at_1 value: 77.235
- type: map_at_10 value: 85.504
- type: map_at_100 value: 85.707
- type: map_at_1000 value: 85.718
- type: map_at_3 value: 84.425
- type: map_at_5 value: 85.13
- type: mrr_at_1 value: 83.363
- type: mrr_at_10 value: 89.916
- type: mrr_at_100 value: 89.955
- type: mrr_at_1000 value: 89.956
- type: mrr_at_3 value: 89.32600000000001
- type: mrr_at_5 value: 89.79
- type: ndcg_at_1 value: 83.363
- type: ndcg_at_10 value: 89.015
- type: ndcg_at_100 value: 89.649
- type: ndcg_at_1000 value: 89.825
- type: ndcg_at_3 value: 87.45100000000001
- type: ndcg_at_5 value: 88.39399999999999
- type: precision_at_1 value: 83.363
- type: precision_at_10 value: 10.659
- type: precision_at_100 value: 1.122
- type: precision_at_1000 value: 0.11499999999999999
- type: precision_at_3 value: 33.338
- type: precision_at_5 value: 20.671999999999997
- type: recall_at_1 value: 77.235
- type: recall_at_10 value: 95.389
- type: recall_at_100 value: 97.722
- type: recall_at_1000 value: 98.744
- type: recall_at_3 value: 91.19800000000001
- type: recall_at_5 value: 93.635
- task:
type: Retrieval
dataset:
type: mteb/fiqa
name: MTEB FiQA2018
config: default
split: test
revision: 27a168819829fe9bcd655c2df245fb19452e8e06
metrics:
- type: map_at_1 value: 20.835
- type: map_at_10 value: 34.459
- type: map_at_100 value: 36.335
- type: map_at_1000 value: 36.518
- type: map_at_3 value: 30.581000000000003
- type: map_at_5 value: 32.859
- type: mrr_at_1 value: 40.894999999999996
- type: mrr_at_10 value: 50.491
- type: mrr_at_100 value: 51.243
- type: mrr_at_1000 value: 51.286
- type: mrr_at_3 value: 47.994
- type: mrr_at_5 value: 49.429
- type: ndcg_at_1 value: 40.894999999999996
- type: ndcg_at_10 value: 42.403
- type: ndcg_at_100 value: 48.954
- type: ndcg_at_1000 value: 51.961
- type: ndcg_at_3 value: 39.11
- type: ndcg_at_5 value: 40.152
- type: precision_at_1 value: 40.894999999999996
- type: precision_at_10 value: 11.466
- type: precision_at_100 value: 1.833
- type: precision_at_1000 value: 0.23700000000000002
- type: precision_at_3 value: 25.874000000000002
- type: precision_at_5 value: 19.012
- type: recall_at_1 value: 20.835
- type: recall_at_10 value: 49.535000000000004
- type: recall_at_100 value: 73.39099999999999
- type: recall_at_1000 value: 91.01599999999999
- type: recall_at_3 value: 36.379
- type: recall_at_5 value: 42.059999999999995
- task:
type: Retrieval
dataset:
type: mteb/hotpotqa
name: MTEB HotpotQA
config: default
split: test
revision: ab518f4d6fcca38d87c25209f94beba119d02014
metrics:
- type: map_at_1 value: 40.945
- type: map_at_10 value: 65.376
- type: map_at_100 value: 66.278
- type: map_at_1000 value: 66.33
- type: map_at_3 value: 61.753
- type: map_at_5 value: 64.077
- type: mrr_at_1 value: 81.891
- type: mrr_at_10 value: 87.256
- type: mrr_at_100 value: 87.392
- type: mrr_at_1000 value: 87.395
- type: mrr_at_3 value: 86.442
- type: mrr_at_5 value: 86.991
- type: ndcg_at_1 value: 81.891
- type: ndcg_at_10 value: 73.654
- type: ndcg_at_100 value: 76.62299999999999
- type: ndcg_at_1000 value: 77.60000000000001
- type: ndcg_at_3 value: 68.71199999999999
- type: ndcg_at_5 value: 71.563
- type: precision_at_1 value: 81.891
- type: precision_at_10 value: 15.409
- type: precision_at_100 value: 1.77
- type: precision_at_1000 value: 0.19
- type: precision_at_3 value: 44.15
- type: precision_at_5 value: 28.732000000000003
- type: recall_at_1 value: 40.945
- type: recall_at_10 value: 77.04299999999999
- type: recall_at_100 value: 88.508
- type: recall_at_1000 value: 94.943
- type: recall_at_3 value: 66.226
- type: recall_at_5 value: 71.83
- task:
type: Classification
dataset:
type: mteb/imdb
name: MTEB ImdbClassification
config: default
split: test
revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7
metrics:
- type: accuracy value: 74.08200000000001
- type: ap value: 68.10929101713998
- type: f1 value: 73.98447117652009
- task:
type: Retrieval
dataset:
type: mteb/msmarco
name: MTEB MSMARCO
config: default
split: dev
revision: c5a29a104738b98a9e76336939199e264163d4a0
metrics:
- type: map_at_1 value: 21.729000000000003
- type: map_at_10 value: 34.602
- type: map_at_100 value: 35.756
- type: map_at_1000 value: 35.803000000000004
- type: map_at_3 value: 30.619000000000003
- type: map_at_5 value: 32.914
- type: mrr_at_1 value: 22.364
- type: mrr_at_10 value: 35.183
- type: mrr_at_100 value: 36.287000000000006
- type: mrr_at_1000 value: 36.327999999999996
- type: mrr_at_3 value: 31.258000000000003
- type: mrr_at_5 value: 33.542
- type: ndcg_at_1 value: 22.364
- type: ndcg_at_10 value: 41.765
- type: ndcg_at_100 value: 47.293
- type: ndcg_at_1000 value: 48.457
- type: ndcg_at_3 value: 33.676
- type: ndcg_at_5 value: 37.783
- type: precision_at_1 value: 22.364
- type: precision_at_10 value: 6.662
- type: precision_at_100 value: 0.943
- type: precision_at_1000 value: 0.104
- type: precision_at_3 value: 14.435999999999998
- type: precision_at_5 value: 10.764999999999999
- type: recall_at_1 value: 21.729000000000003
- type: recall_at_10 value: 63.815999999999995
- type: recall_at_100 value: 89.265
- type: recall_at_1000 value: 98.149
- type: recall_at_3 value: 41.898
- type: recall_at_5 value: 51.76500000000001
- task:
type: Classification
dataset:
type: mteb/mtop_domain
name: MTEB MTOPDomainClassification (en)
config: en
split: test
revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf
metrics:
- type: accuracy value: 92.73141814865483
- type: f1 value: 92.17518476408004
- task:
type: Classification
dataset:
type: mteb/mtop_intent
name: MTEB MTOPIntentClassification (en)
config: en
split: test
revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba
metrics:
- type: accuracy value: 65.18011855905152
- type: f1 value: 46.70999638311856
- task:
type: Classification
dataset:
type: masakhane/masakhanews
name: MTEB MasakhaNEWSClassification (eng)
config: eng
split: test
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
metrics:
- type: accuracy value: 75.24261603375525
- type: f1 value: 74.07895183913367
- task:
type: Clustering
dataset:
type: masakhane/masakhanews
name: MTEB MasakhaNEWSClusteringP2P (eng)
config: eng
split: test
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
metrics:
- type: v_measure value: 28.43855875387446
- task:
type: Clustering
dataset:
type: masakhane/masakhanews
name: MTEB MasakhaNEWSClusteringS2S (eng)
config: eng
split: test
revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60
metrics:
- type: v_measure value: 29.05331990256969
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (en)
config: en
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy value: 66.92333557498318
- type: f1 value: 64.29789389602692
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (en)
config: en
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy value: 72.74714189643578
- type: f1 value: 71.672585608315
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-p2p
name: MTEB MedrxivClusteringP2P
config: default
split: test
revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73
metrics:
- type: v_measure value: 31.503564225501613
- task:
type: Clustering
dataset:
type: mteb/medrxiv-clustering-s2s
name: MTEB MedrxivClusteringS2S
config: default
split: test
revision: 35191c8c0dca72d8ff3efcd72aa802307d469663
metrics:
- type: v_measure value: 28.410225127136457
- task:
type: Reranking
dataset:
type: mteb/mind_small
name: MTEB MindSmallReranking
config: default
split: test
revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69
metrics:
- type: map value: 29.170019896091908
- type: mrr value: 29.881276831500976
- task:
type: Retrieval
dataset:
type: mteb/nfcorpus
name: MTEB NFCorpus
config: default
split: test
revision: ec0fa4fe99da2ff19ca1214b7966684033a58814
metrics:
- type: map_at_1 value: 6.544
- type: map_at_10 value: 14.116999999999999
- type: map_at_100 value: 17.522
- type: map_at_1000 value: 19
- type: map_at_3 value: 10.369
- type: map_at_5 value: 12.189
- type: mrr_at_1 value: 47.988
- type: mrr_at_10 value: 56.84
- type: mrr_at_100 value: 57.367000000000004
- type: mrr_at_1000 value: 57.403000000000006
- type: mrr_at_3 value: 54.592
- type: mrr_at_5 value: 56.233
- type: ndcg_at_1 value: 45.82
- type: ndcg_at_10 value: 36.767
- type: ndcg_at_100 value: 33.356
- type: ndcg_at_1000 value: 42.062
- type: ndcg_at_3 value: 42.15
- type: ndcg_at_5 value: 40.355000000000004
- type: precision_at_1 value: 47.988
- type: precision_at_10 value: 27.121000000000002
- type: precision_at_100 value: 8.455
- type: precision_at_1000 value: 2.103
- type: precision_at_3 value: 39.628
- type: precision_at_5 value: 35.356
- type: recall_at_1 value: 6.544
- type: recall_at_10 value: 17.928
- type: recall_at_100 value: 32.843
- type: recall_at_1000 value: 65.752
- type: recall_at_3 value: 11.297
- type: recall_at_5 value: 14.357000000000001
- task:
type: Retrieval
dataset:
type: mteb/nq
name: MTEB NQ
config: default
split: test
revision: b774495ed302d8c44a3a7ea25c90dbce03968f31
metrics:
- type: map_at_1 value: 39.262
- type: map_at_10 value: 55.095000000000006
- type: map_at_100 value: 55.93900000000001
- type: map_at_1000 value: 55.955999999999996
- type: map_at_3 value: 50.93
- type: map_at_5 value: 53.491
- type: mrr_at_1 value: 43.598
- type: mrr_at_10 value: 57.379999999999995
- type: mrr_at_100 value: 57.940999999999995
- type: mrr_at_1000 value: 57.952000000000005
- type: mrr_at_3 value: 53.998000000000005
- type: mrr_at_5 value: 56.128
- type: ndcg_at_1 value: 43.598
- type: ndcg_at_10 value: 62.427
- type: ndcg_at_100 value: 65.759
- type: ndcg_at_1000 value: 66.133
- type: ndcg_at_3 value: 54.745999999999995
- type: ndcg_at_5 value: 58.975
- type: precision_at_1 value: 43.598
- type: precision_at_10 value: 9.789
- type: precision_at_100 value: 1.171
- type: precision_at_1000 value: 0.121
- type: precision_at_3 value: 24.295
- type: precision_at_5 value: 17.028
- type: recall_at_1 value: 39.262
- type: recall_at_10 value: 82.317
- type: recall_at_100 value: 96.391
- type: recall_at_1000 value: 99.116
- type: recall_at_3 value: 62.621
- type: recall_at_5 value: 72.357
- task:
type: Classification
dataset:
type: ag_news
name: MTEB NewsClassification
config: default
split: test
revision: eb185aade064a813bc0b7f42de02595523103ca4
metrics:
- type: accuracy value: 78.17500000000001
- type: f1 value: 78.01940892857273
- task:
type: PairClassification
dataset:
type: GEM/opusparcus
name: MTEB OpusparcusPC (en)
config: en
split: test
revision: 9e9b1f8ef51616073f47f306f7f47dd91663f86a
metrics:
- type: cos_sim_accuracy value: 99.89816700610999
- type: cos_sim_ap value: 100
- type: cos_sim_f1 value: 99.9490575649516
- type: cos_sim_precision value: 100
- type: cos_sim_recall value: 99.89816700610999
- type: dot_accuracy value: 99.89816700610999
- type: dot_ap value: 100
- type: dot_f1 value: 99.9490575649516
- type: dot_precision value: 100
- type: dot_recall value: 99.89816700610999
- type: euclidean_accuracy value: 99.89816700610999
- type: euclidean_ap value: 100
- type: euclidean_f1 value: 99.9490575649516
- type: euclidean_precision value: 100
- type: euclidean_recall value: 99.89816700610999
- type: manhattan_accuracy value: 99.89816700610999
- type: manhattan_ap value: 100
- type: manhattan_f1 value: 99.9490575649516
- type: manhattan_precision value: 100
- type: manhattan_recall value: 99.89816700610999
- type: max_accuracy value: 99.89816700610999
- type: max_ap value: 100
- type: max_f1 value: 99.9490575649516
- task:
type: PairClassification
dataset:
type: paws-x
name: MTEB PawsX (en)
config: en
split: test
revision: 8a04d940a42cd40658986fdd8e3da561533a3646
metrics:
- type: cos_sim_accuracy value: 61
- type: cos_sim_ap value: 59.630757252602464
- type: cos_sim_f1 value: 62.37521514629949
- type: cos_sim_precision value: 45.34534534534534
- type: cos_sim_recall value: 99.88974641675854
- type: dot_accuracy value: 61
- type: dot_ap value: 59.631527308059006
- type: dot_f1 value: 62.37521514629949
- type: dot_precision value: 45.34534534534534
- type: dot_recall value: 99.88974641675854
- type: euclidean_accuracy value: 61
- type: euclidean_ap value: 59.630757252602464
- type: euclidean_f1 value: 62.37521514629949
- type: euclidean_precision value: 45.34534534534534
- type: euclidean_recall value: 99.88974641675854
- type: manhattan_accuracy value: 60.9
- type: manhattan_ap value: 59.613947780462254
- type: manhattan_f1 value: 62.37521514629949
- type: manhattan_precision value: 45.34534534534534
- type: manhattan_recall value: 99.88974641675854
- type: max_accuracy value: 61
- type: max_ap value: 59.631527308059006
- type: max_f1 value: 62.37521514629949
- task:
type: Retrieval
dataset:
type: mteb/quora
name: MTEB QuoraRetrieval
config: default
split: test
revision: e4e08e0b7dbe3c8700f0daef558ff32256715259
metrics:
- type: map_at_1 value: 69.963
- type: map_at_10 value: 83.59400000000001
- type: map_at_100 value: 84.236
- type: map_at_1000 value: 84.255
- type: map_at_3 value: 80.69800000000001
- type: map_at_5 value: 82.568
- type: mrr_at_1 value: 80.58999999999999
- type: mrr_at_10 value: 86.78200000000001
- type: mrr_at_100 value: 86.89099999999999
- type: mrr_at_1000 value: 86.893
- type: mrr_at_3 value: 85.757
- type: mrr_at_5 value: 86.507
- type: ndcg_at_1 value: 80.60000000000001
- type: ndcg_at_10 value: 87.41799999999999
- type: ndcg_at_100 value: 88.723
- type: ndcg_at_1000 value: 88.875
- type: ndcg_at_3 value: 84.565
- type: ndcg_at_5 value: 86.236
- type: precision_at_1 value: 80.60000000000001
- type: precision_at_10 value: 13.239
- type: precision_at_100 value: 1.5150000000000001
- type: precision_at_1000 value: 0.156
- type: precision_at_3 value: 36.947
- type: precision_at_5 value: 24.354
- type: recall_at_1 value: 69.963
- type: recall_at_10 value: 94.553
- type: recall_at_100 value: 99.104
- type: recall_at_1000 value: 99.872
- type: recall_at_3 value: 86.317
- type: recall_at_5 value: 91.023
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering
name: MTEB RedditClustering
config: default
split: test
revision: 24640382cdbf8abc73003fb0fa6d111a705499eb
metrics:
- type: v_measure value: 47.52890410998761
- task:
type: Clustering
dataset:
type: mteb/reddit-clustering-p2p
name: MTEB RedditClusteringP2P
config: default
split: test
revision: 385e3cb46b4cfa89021f56c4380204149d0efe33
metrics:
- type: v_measure value: 62.760692287940486
- task:
type: Retrieval
dataset:
type: mteb/scidocs
name: MTEB SCIDOCS
config: default
split: test
revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88
metrics:
- type: map_at_1 value: 5.093
- type: map_at_10 value: 12.695
- type: map_at_100 value: 14.824000000000002
- type: map_at_1000 value: 15.123000000000001
- type: map_at_3 value: 8.968
- type: map_at_5 value: 10.828
- type: mrr_at_1 value: 25.1
- type: mrr_at_10 value: 35.894999999999996
- type: mrr_at_100 value: 36.966
- type: mrr_at_1000 value: 37.019999999999996
- type: mrr_at_3 value: 32.467
- type: mrr_at_5 value: 34.416999999999994
- type: ndcg_at_1 value: 25.1
- type: ndcg_at_10 value: 21.096999999999998
- type: ndcg_at_100 value: 29.202
- type: ndcg_at_1000 value: 34.541
- type: ndcg_at_3 value: 19.875
- type: ndcg_at_5 value: 17.497
- type: precision_at_1 value: 25.1
- type: precision_at_10 value: 10.9
- type: precision_at_100 value: 2.255
- type: precision_at_1000 value: 0.35400000000000004
- type: precision_at_3 value: 18.367
- type: precision_at_5 value: 15.299999999999999
- type: recall_at_1 value: 5.093
- type: recall_at_10 value: 22.092
- type: recall_at_100 value: 45.778
- type: recall_at_1000 value: 71.985
- type: recall_at_3 value: 11.167
- type: recall_at_5 value: 15.501999999999999
- task:
type: STS
dataset:
type: mteb/sickr-sts
name: MTEB SICK-R
config: default
split: test
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
metrics:
- type: cos_sim_pearson value: 74.04386981759481
- type: cos_sim_spearman value: 69.12484963763646
- type: euclidean_pearson value: 71.49384353291062
- type: euclidean_spearman value: 69.12484548317074
- type: manhattan_pearson value: 71.49828173987272
- type: manhattan_spearman value: 69.08350274367014
- task:
type: STS
dataset:
type: mteb/sts12-sts
name: MTEB STS12
config: default
split: test
revision: a0d554a64d88156834ff5ae9920b964011b16384
metrics:
- type: cos_sim_pearson value: 66.95372527615659
- type: cos_sim_spearman value: 66.96821894433991
- type: euclidean_pearson value: 64.675348002074
- type: euclidean_spearman value: 66.96821894433991
- type: manhattan_pearson value: 64.5965887073831
- type: manhattan_spearman value: 66.88569076794741
- task:
type: STS
dataset:
type: mteb/sts13-sts
name: MTEB STS13
config: default
split: test
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
metrics:
- type: cos_sim_pearson value: 77.34698437961983
- type: cos_sim_spearman value: 79.1153001117325
- type: euclidean_pearson value: 78.53562874696966
- type: euclidean_spearman value: 79.11530018205724
- type: manhattan_pearson value: 78.46484988944093
- type: manhattan_spearman value: 79.01416027493104
- task:
type: STS
dataset:
type: mteb/sts14-sts
name: MTEB STS14
config: default
split: test
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
metrics:
- type: cos_sim_pearson value: 68.81220371935373
- type: cos_sim_spearman value: 68.50538405089604
- type: euclidean_pearson value: 68.69204272683749
- type: euclidean_spearman value: 68.50534223912419
- type: manhattan_pearson value: 68.67300120149523
- type: manhattan_spearman value: 68.45404301623115
- task:
type: STS
dataset:
type: mteb/sts15-sts
name: MTEB STS15
config: default
split: test
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
metrics:
- type: cos_sim_pearson value: 78.2464678879813
- type: cos_sim_spearman value: 79.92003940566667
- type: euclidean_pearson value: 79.8080778793964
- type: euclidean_spearman value: 79.92003940566667
- type: manhattan_pearson value: 79.80153621444681
- type: manhattan_spearman value: 79.91293261418134
- task:
type: STS
dataset:
type: mteb/sts16-sts
name: MTEB STS16
config: default
split: test
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
metrics:
- type: cos_sim_pearson value: 76.31179207708662
- type: cos_sim_spearman value: 78.65597349856115
- type: euclidean_pearson value: 78.76937027472678
- type: euclidean_spearman value: 78.65597349856115
- type: manhattan_pearson value: 78.77129513300605
- type: manhattan_spearman value: 78.62640467680775
- task:
type: STS
dataset:
type: mteb/sts17-crosslingual-sts
name: MTEB STS17 (en-en)
config: en-en
split: test
revision: af5e6fb845001ecf41f4c1e033ce921939a2a68d
metrics:
- type: cos_sim_pearson value: 79.43158429552561
- type: cos_sim_spearman value: 81.46108646565362
- type: euclidean_pearson value: 81.47071791452292
- type: euclidean_spearman value: 81.46108646565362
- type: manhattan_pearson value: 81.56920643846031
- type: manhattan_spearman value: 81.42226241399516
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (en)
config: en
split: test
revision: eea2b4fe26a775864c896887d910b76a8098ad3f
metrics:
- type: cos_sim_pearson value: 66.89546474141514
- type: cos_sim_spearman value: 65.8393752170531
- type: euclidean_pearson value: 67.2580522762307
- type: euclidean_spearman value: 65.8393752170531
- type: manhattan_pearson value: 67.45157729300522
- type: manhattan_spearman value: 66.19470854403802
- task:
type: STS
dataset:
type: mteb/stsbenchmark-sts
name: MTEB STSBenchmark
config: default
split: test
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
metrics:
- type: cos_sim_pearson value: 71.39566306334434
- type: cos_sim_spearman value: 74.0981396086974
- type: euclidean_pearson value: 73.7834496259745
- type: euclidean_spearman value: 74.09803741302046
- type: manhattan_pearson value: 73.79958138780945
- type: manhattan_spearman value: 74.09894837555905
- task:
type: STS
dataset:
type: PhilipMay/stsb_multi_mt
name: MTEB STSBenchmarkMultilingualSTS (en)
config: en
split: test
revision: 93d57ef91790589e3ce9c365164337a8a78b7632
metrics:
- type: cos_sim_pearson value: 71.39566311006806
- type: cos_sim_spearman value: 74.0981396086974
- type: euclidean_pearson value: 73.78344970897099
- type: euclidean_spearman value: 74.09803741302046
- type: manhattan_pearson value: 73.79958147136705
- type: manhattan_spearman value: 74.09894837555905
- task:
type: Reranking
dataset:
type: mteb/scidocs-reranking
name: MTEB SciDocsRR
config: default
split: test
revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab
metrics:
- type: map value: 80.81059564334683
- type: mrr value: 94.62696617108381
- task:
type: Retrieval
dataset:
type: mteb/scifact
name: MTEB SciFact
config: default
split: test
revision: 0228b52cf27578f30900b9e5271d331663a030d7
metrics:
- type: map_at_1 value: 57.760999999999996
- type: map_at_10 value: 68.614
- type: map_at_100 value: 69.109
- type: map_at_1000 value: 69.134
- type: map_at_3 value: 65.735
- type: map_at_5 value: 67.42099999999999
- type: mrr_at_1 value: 60.667
- type: mrr_at_10 value: 69.94200000000001
- type: mrr_at_100 value: 70.254
- type: mrr_at_1000 value: 70.28
- type: mrr_at_3 value: 67.72200000000001
- type: mrr_at_5 value: 69.18900000000001
- type: ndcg_at_1 value: 60.667
- type: ndcg_at_10 value: 73.548
- type: ndcg_at_100 value: 75.381
- type: ndcg_at_1000 value: 75.991
- type: ndcg_at_3 value: 68.685
- type: ndcg_at_5 value: 71.26
- type: precision_at_1 value: 60.667
- type: precision_at_10 value: 9.833
- type: precision_at_100 value: 1.08
- type: precision_at_1000 value: 0.11299999999999999
- type: precision_at_3 value: 26.889000000000003
- type: precision_at_5 value: 17.8
- type: recall_at_1 value: 57.760999999999996
- type: recall_at_10 value: 87.13300000000001
- type: recall_at_100 value: 95
- type: recall_at_1000 value: 99.667
- type: recall_at_3 value: 74.211
- type: recall_at_5 value: 80.63900000000001
- task:
type: PairClassification
dataset:
type: mteb/sprintduplicatequestions-pairclassification
name: MTEB SprintDuplicateQuestions
config: default
split: test
revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46
metrics:
- type: cos_sim_accuracy value: 99.81881188118813
- type: cos_sim_ap value: 95.21196473745837
- type: cos_sim_f1 value: 90.69767441860465
- type: cos_sim_precision value: 91.71779141104295
- type: cos_sim_recall value: 89.7
- type: dot_accuracy value: 99.81881188118813
- type: dot_ap value: 95.21196473745837
- type: dot_f1 value: 90.69767441860465
- type: dot_precision value: 91.71779141104295
- type: dot_recall value: 89.7
- type: euclidean_accuracy value: 99.81881188118813
- type: euclidean_ap value: 95.21196473745839
- type: euclidean_f1 value: 90.69767441860465
- type: euclidean_precision value: 91.71779141104295
- type: euclidean_recall value: 89.7
- type: manhattan_accuracy value: 99.81287128712871
- type: manhattan_ap value: 95.16667174835017
- type: manhattan_f1 value: 90.41095890410959
- type: manhattan_precision value: 91.7610710607621
- type: manhattan_recall value: 89.1
- type: max_accuracy value: 99.81881188118813
- type: max_ap value: 95.21196473745839
- type: max_f1 value: 90.69767441860465
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering
name: MTEB StackExchangeClustering
config: default
split: test
revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259
metrics:
- type: v_measure value: 59.54942204515638
- task:
type: Clustering
dataset:
type: mteb/stackexchange-clustering-p2p
name: MTEB StackExchangeClusteringP2P
config: default
split: test
revision: 815ca46b2622cec33ccafc3735d572c266efdb44
metrics:
- type: v_measure value: 39.42892282672948
- task:
type: Reranking
dataset:
type: mteb/stackoverflowdupquestions-reranking
name: MTEB StackOverflowDupQuestions
config: default
split: test
revision: e185fbe320c72810689fc5848eb6114e1ef5ec69
metrics:
- type: map value: 51.189033075914324
- type: mrr value: 51.97014790764791
- task:
type: Summarization
dataset:
type: mteb/summeval
name: MTEB SummEval
config: default
split: test
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
metrics:
- type: cos_sim_pearson value: 30.09466569775977
- type: cos_sim_spearman value: 30.31058660775912
- type: dot_pearson value: 30.09466438861689
- type: dot_spearman value: 30.31058660775912
- task:
type: Retrieval
dataset:
type: mteb/trec-covid
name: MTEB TRECCOVID
config: default
split: test
revision: bb9466bac8153a0349341eb1b22e06409e78ef4e
metrics:
- type: map_at_1 value: 0.253
- type: map_at_10 value: 2.07
- type: map_at_100 value: 12.679000000000002
- type: map_at_1000 value: 30.412
- type: map_at_3 value: 0.688
- type: map_at_5 value: 1.079
- type: mrr_at_1 value: 96
- type: mrr_at_10 value: 98
- type: mrr_at_100 value: 98
- type: mrr_at_1000 value: 98
- type: mrr_at_3 value: 98
- type: mrr_at_5 value: 98
- type: ndcg_at_1 value: 89
- type: ndcg_at_10 value: 79.646
- type: ndcg_at_100 value: 62.217999999999996
- type: ndcg_at_1000 value: 55.13400000000001
- type: ndcg_at_3 value: 83.458
- type: ndcg_at_5 value: 80.982
- type: precision_at_1 value: 96
- type: precision_at_10 value: 84.6
- type: precision_at_100 value: 64.34
- type: precision_at_1000 value: 24.534
- type: precision_at_3 value: 88.667
- type: precision_at_5 value: 85.6
- type: recall_at_1 value: 0.253
- type: recall_at_10 value: 2.253
- type: recall_at_100 value: 15.606
- type: recall_at_1000 value: 51.595
- type: recall_at_3 value: 0.7100000000000001
- type: recall_at_5 value: 1.139
- task:
type: Retrieval
dataset:
type: mteb/touche2020
name: MTEB Touche2020
config: default
split: test
revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f
metrics:
- type: map_at_1 value: 3.0540000000000003
- type: map_at_10 value: 13.078999999999999
- type: map_at_100 value: 19.468
- type: map_at_1000 value: 21.006
- type: map_at_3 value: 6.8629999999999995
- type: map_at_5 value: 9.187
- type: mrr_at_1 value: 42.857
- type: mrr_at_10 value: 56.735
- type: mrr_at_100 value: 57.352000000000004
- type: mrr_at_1000 value: 57.352000000000004
- type: mrr_at_3 value: 52.721
- type: mrr_at_5 value: 54.66
- type: ndcg_at_1 value: 38.775999999999996
- type: ndcg_at_10 value: 31.469
- type: ndcg_at_100 value: 42.016999999999996
- type: ndcg_at_1000 value: 52.60399999999999
- type: ndcg_at_3 value: 35.894
- type: ndcg_at_5 value: 33.873
- type: precision_at_1 value: 42.857
- type: precision_at_10 value: 27.346999999999998
- type: precision_at_100 value: 8.327
- type: precision_at_1000 value: 1.551
- type: precision_at_3 value: 36.735
- type: precision_at_5 value: 33.469
- type: recall_at_1 value: 3.0540000000000003
- type: recall_at_10 value: 19.185
- type: recall_at_100 value: 51.056000000000004
- type: recall_at_1000 value: 82.814
- type: recall_at_3 value: 7.961
- type: recall_at_5 value: 11.829
- task:
type: Classification
dataset:
type: mteb/toxic_conversations_50k
name: MTEB ToxicConversationsClassification
config: default
split: test
revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de
metrics:
- type: accuracy value: 64.9346
- type: ap value: 12.121605736777527
- type: f1 value: 50.169902005887955
- task:
type: Classification
dataset:
type: mteb/tweet_sentiment_extraction
name: MTEB TweetSentimentExtractionClassification
config: default
split: test
revision: d604517c81ca91fe16a244d1248fc021f9ecee7a
metrics:
- type: accuracy value: 56.72608941709111
- type: f1 value: 57.0702928875253
- task:
type: Clustering
dataset:
type: mteb/twentynewsgroups-clustering
name: MTEB TwentyNewsgroupsClustering
config: default
split: test
revision: 6125ec4e24fa026cec8a478383ee943acfbd5449
metrics:
- type: v_measure value: 37.72671554400943
- task:
type: PairClassification
dataset:
type: mteb/twittersemeval2015-pairclassification
name: MTEB TwitterSemEval2015
config: default
split: test
revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1
metrics:
- type: cos_sim_accuracy value: 82.84556237706384
- type: cos_sim_ap value: 63.28364215788651
- type: cos_sim_f1 value: 60.00000000000001
- type: cos_sim_precision value: 54.45161290322581
- type: cos_sim_recall value: 66.80738786279683
- type: dot_accuracy value: 82.84556237706384
- type: dot_ap value: 63.28364302860433
- type: dot_f1 value: 60.00000000000001
- type: dot_precision value: 54.45161290322581
- type: dot_recall value: 66.80738786279683
- type: euclidean_accuracy value: 82.84556237706384
- type: euclidean_ap value: 63.28363625097978
- type: euclidean_f1 value: 60.00000000000001
- type: euclidean_precision value: 54.45161290322581
- type: euclidean_recall value: 66.80738786279683
- type: manhattan_accuracy value: 82.86940454193241
- type: manhattan_ap value: 63.244773709836764
- type: manhattan_f1 value: 60.12680942696495
- type: manhattan_precision value: 55.00109433136353
- type: manhattan_recall value: 66.3060686015831
- type: max_accuracy value: 82.86940454193241
- type: max_ap value: 63.28364302860433
- type: max_f1 value: 60.12680942696495
- task:
type: PairClassification
dataset:
type: mteb/twitterurlcorpus-pairclassification
name: MTEB TwitterURLCorpus
config: default
split: test
revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf
metrics:
- type: cos_sim_accuracy value: 88.32033220786278
- type: cos_sim_ap value: 84.71928176006863
- type: cos_sim_f1 value: 76.51483333969684
- type: cos_sim_precision value: 75.89184276300841
- type: cos_sim_recall value: 77.14813674160764
- type: dot_accuracy value: 88.32033220786278
- type: dot_ap value: 84.71928330149228
- type: dot_f1 value: 76.51483333969684
- type: dot_precision value: 75.89184276300841
- type: dot_recall value: 77.14813674160764
- type: euclidean_accuracy value: 88.32033220786278
- type: euclidean_ap value: 84.71928045384345
- type: euclidean_f1 value: 76.51483333969684
- type: euclidean_precision value: 75.89184276300841
- type: euclidean_recall value: 77.14813674160764
- type: manhattan_accuracy value: 88.27570147863545
- type: manhattan_ap value: 84.68523541579755
- type: manhattan_f1 value: 76.51512269355146
- type: manhattan_precision value: 75.62608107091825
- type: manhattan_recall value: 77.42531567600862
- type: max_accuracy value: 88.32033220786278
- type: max_ap value: 84.71928330149228
- type: max_f1 value: 76.51512269355146
- task:
type: Clustering
dataset:
type: jinaai/cities_wiki_clustering
name: MTEB WikiCitiesClustering
config: default
split: test
revision: ddc9ee9242fa65332597f70e967ecc38b9d734fa
metrics:
- type: v_measure value: 85.30624598674467 license: apache-2.0 new_version: Snowflake/snowflake-arctic-embed-m-v2.0
- task:
type: Classification
dataset:
type: mteb/amazon_counterfactual
name: MTEB AmazonCounterfactualClassification (en)
config: en
split: test
revision: e8379541af4e31359cca9fbcf4b00f2671dba205
metrics:
Snowflake's Arctic-embed-m
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. These models outperform prior versions of Arctic Embed and we suggest these replace prior versions!
07/26/2024: Release preprint [2407.18887] Embedding And Clustering Your Data Can Improve Contrastive Pretraining on arXiv.
07/18/2024: Release of snowflake-arctic-embed-m-v1.5, capable of producing highly compressible embedding vectors that preserve quality even when squished as small as 128 bytes per vector. Details about the development of this model are available in the launch post on the Snowflake engineering blog.
05/10/2024: Release the technical report on Arctic Embed
04/16/2024: Release the ** snowflake-arctic-embed ** family of text embedding models. The releases are state-of-the-art for Retrieval quality at each of their representative size profiles. Technical Report is coming shortly. For more details, please refer to our Github: Arctic-Text-Embed.
Models
snowflake-arctic-embed is a suite of text embedding models that focuses on creating high-quality retrieval models optimized for performance.
The snowflake-arctic-embedding models achieve state-of-the-art performance on the MTEB/BEIR leaderboard for each of their size variants. Evaluation is performed using these scripts. As shown below, each class of model size achieves SOTA retrieval accuracy compared to other top models.
The models are trained by leveraging existing open-source text representation models, such as bert-base-uncased, and are trained in a multi-stage pipeline to optimize their retrieval performance. First, the models are trained with large batches of query-document pairs where negatives are derived in-batch—pretraining leverages about 400m samples of a mix of public datasets and proprietary web search data. Following pretraining models are further optimized with long training on a smaller dataset (about 1m samples) of triplets of query, positive document, and negative document derived from hard harmful mining. Mining of the negatives and data curation is crucial to retrieval accuracy. A detailed technical report can be found here.
| Name | MTEB Retrieval Score (NDCG @ 10) | Parameters (Millions) | Embedding Dimension |
|---|---|---|---|
| snowflake-arctic-embed-xs | 50.15 | 22 | 384 |
| snowflake-arctic-embed-s | 51.98 | 33 | 384 |
| snowflake-arctic-embed-m | 54.90 | 110 | 768 |
| snowflake-arctic-embed-m-long | 54.83 | 137 | 768 |
| snowflake-arctic-embed-l | 55.98 | 335 | 1024 |
Aside from being great open-source models, the largest model, snowflake-arctic-embed-l, can serve as a natural replacement for closed-source embedding, as shown below.
| Model Name | MTEB Retrieval Score (NDCG @ 10) |
|---|---|
| snowflake-arctic-embed-l | 55.98 |
| Google-gecko-text-embedding | 55.7 |
| text-embedding-3-large | 55.44 |
| Cohere-embed-english-v3.0 | 55.00 |
| bge-large-en-v1.5 | 54.29 |
snowflake-arctic-embed-xs
This tiny model packs quite the punch. Based on the all-MiniLM-L6-v2 model with only 22m parameters and 384 dimensions, this model should meet even the strictest latency/TCO budgets. Despite its size, its retrieval accuracy is closer to that of models with 100m paramers.
| Model Name | MTEB Retrieval Score (NDCG @ 10) |
|---|---|
| snowflake-arctic-embed-xs | 50.15 |
| GIST-all-MiniLM-L6-v2 | 45.12 |
| gte-tiny | 44.92 |
| all-MiniLM-L6-v2 | 41.95 |
| bge-micro-v2 | 42.56 |
snowflake-arctic-embed-s
Based on the intfloat/e5-small-unsupervised model, this small model does not trade off retrieval accuracy for its small size. With only 33m parameters and 384 dimensions, this model should easily allow scaling to large datasets.
| Model Name | MTEB Retrieval Score (NDCG @ 10) |
|---|---|
| snowflake-arctic-embed-s | 51.98 |
| bge-small-en-v1.5 | 51.68 |
| Cohere-embed-english-light-v3.0 | 51.34 |
| text-embedding-3-small | 51.08 |
| e5-small-v2 | 49.04 |
snowflake-arctic-embed-m
Based on the intfloat/e5-base-unsupervised model, this medium model is the workhorse that provides the best retrieval performance without slowing down inference.
| Model Name | MTEB Retrieval Score (NDCG @ 10) |
|---|---|
| snowflake-arctic-embed-m | 54.90 |
| bge-base-en-v1.5 | 53.25 |
| nomic-embed-text-v1.5 | 53.25 |
| GIST-Embedding-v0 | 52.31 |
| gte-base | 52.31 |
snowflake-arctic-embed-m-long
Based on the nomic-ai/nomic-embed-text-v1-unsupervised model, this long-context variant of our medium-sized model is perfect for workloads that can be constrained by the regular 512 token context of our other models. Without the use of RPE, this model supports up to 2048 tokens. With RPE, it can scale to 8192!
| Model Name | MTEB Retrieval Score (NDCG @ 10) |
|---|---|
| snowflake-arctic-embed-m-long | 54.83 |
| nomic-embed-text-v1.5 | 53.01 |
| nomic-embed-text-v1 | 52.81 |
snowflake-arctic-embed-l
Based on the intfloat/e5-large-unsupervised model, this large model is a direct drop-in for closed APIs and delivers the most accurate retrieval experience.
| Model Name | MTEB Retrieval Score (NDCG @ 10) |
|---|---|
| snowflake-arctic-embed-l | 55.98 |
| UAE-Large-V1 | 54.66 |
| bge-large-en-v1.5 | 54.29 |
| mxbai-embed-large-v1 | 54.39 |
| e5-Large-v2 | 50.56 |
Usage
Using Sentence Transformers
You can use the sentence-transformers package to use an snowflake-arctic-embed model, as shown below.
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("Snowflake/snowflake-arctic-embed-m")
queries = ['what is snowflake?', 'Where can I get the best tacos?']
documents = ['The Data Cloud!', 'Mexico City of Course!']
query_embeddings = model.encode(queries, prompt_name="query")
document_embeddings = model.encode(documents)
scores = query_embeddings @ document_embeddings.T
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)
Produces:
Query: what is snowflake?
0.2747492 The Data Cloud!
0.19998045 Mexico City of Course!
Query: Where can I get the best tacos?
0.29974818 Mexico City of Course!
0.2344071 The Data Cloud!
Using Huggingface transformers
You can use the transformers package to use an snowflake-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
tokenizer = AutoTokenizer.from_pretrained('Snowflake/snowflake-arctic-embed-m')
model = AutoModel.from_pretrained('Snowflake/snowflake-arctic-embed-m', add_pooling_layer=False)
model.eval()
query_prefix = 'Represent this sentence for searching relevant passages: '
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=512)
documents = ['The Data Cloud!', 'Mexico City of Course!']
document_tokens = tokenizer(documents, padding=True, truncation=True, return_tensors='pt', max_length=512)
# 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 Transformers.js
If you haven't already, you can install the Transformers.js JavaScript library from NPM by running:
npm i @xenova/transformers
You can then use the model to compute embeddings as follows:
import { pipeline, dot } from '@xenova/transformers';
// Create feature extraction pipeline
const extractor = await pipeline('feature-extraction', 'Snowflake/snowflake-arctic-embed-m', {
quantized: false, // Comment out this line to use the quantized version
});
// Generate sentence embeddings
const sentences = [
'Represent this sentence for searching relevant passages: Where can I get the best tacos?',
'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); // [0.15664823859882132, 0.24481869975470627]
Using Infinity
OpenAI compatible API deployment with Infinity and Docker.
docker run --gpus all -v $PWD/data:/app/.cache -p "7997":"7997" \
michaelf34/infinity:0.0.70 \
v2 --model-id Snowflake/snowflake-arctic-embed-m --dtype float16 --batch-size 32 --engine torch --port 7997
FAQ
TBD
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.
Acknowledgement
We want to thank the open-source community, which has provided the great building blocks upon which we could make our models. We thank our modeling engineers, Danmei Xu, Luke Merrick, Gaurav Nuti, and Daniel Campos, for making these great models possible. We thank our leadership, Himabindu Pucha, Kelvin So, Vivek Raghunathan, and Sridhar Ramaswamy, for supporting this work. We also thank the open-source community for producing the great models we could build on top of and making these releases possible. Finally, we thank the researchers who created BEIR and MTEB benchmarks. It is largely thanks to their tireless work to define what better looks like that we could improve model performance.
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