--- 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 - 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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 - 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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 ---

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](https://huggingface.co/Snowflake/snowflake-arctic-embed-l-v2.0) and [snowflake-arctic-embed-m-v2.0](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-v2.0) our newest models with multilingual workloads in mind. 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](https://arxiv.org/abs/2407.18887) 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](https://www.snowflake.com/engineering-blog/arctic-embed-m-v1-5-enterprise-retrieval/). 05/10/2024: Release the [technical report on Arctic Embed](https://arxiv.org/abs/2405.05374) 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](https://github.com/Snowflake-Labs/arctic-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](https://github.com/Snowflake-Labs/snowflake-arctic-embed/tree/main/src). 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](https://arxiv.org/abs/2405.05374). | Name | MTEB Retrieval Score (NDCG @ 10) | Parameters (Millions) | Embedding Dimension | | ----------------------------------------------------------------------- | -------------------------------- | --------------------- | ------------------- | | [snowflake-arctic-embed-xs](https://huggingface.co/Snowflake/snowflake-arctic-embed-xs/) | 50.15 | 22 | 384 | | [snowflake-arctic-embed-s](https://huggingface.co/Snowflake/snowflake-arctic-embed-s/) | 51.98 | 33 | 384 | | [snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m/) | 54.90 | 110 | 768 | | [snowflake-arctic-embed-m-long](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-long/) | 54.83 | 137 | 768 | | [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/) | 55.98 | 335 | 1024 | Aside from being great open-source models, the largest model, [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/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](https://huggingface.co/Snowflake/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](https://huggingface.co/Snowflake/snowflake-arctic-embed-xs) This tiny model packs quite the punch. Based on the [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/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](https://huggingface.co/Snowflake/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](https://huggingface.co/Snowflake/snowflake-arctic-embed-s) Based on the [intfloat/e5-small-unsupervised](https://huggingface.co/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](https://huggingface.co/Snowflake/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](https://huggingface.co/Snowflake/snowflake-arctic-embed-m/) Based on the [intfloat/e5-base-unsupervised](https://huggingface.co/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](https://huggingface.co/Snowflake/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](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-long/) Based on the [nomic-ai/nomic-embed-text-v1-unsupervised](https://huggingface.co/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](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-long/) | 54.83 | | nomic-embed-text-v1.5 | 53.01 | | nomic-embed-text-v1 | 52.81 | ### [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/) Based on the [intfloat/e5-large-unsupervised](https://huggingface.co/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](https://huggingface.co/Snowflake/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. ```python 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). ```python 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](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@xenova/transformers) by running: ```bash npm i @xenova/transformers ``` You can then use the model to compute embeddings as follows: ```js 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](https://github.com/michaelfeil/infinity) and Docker. ```bash 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](https://www.apache.org/licenses/LICENSE-2.0). 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.