| --- |
| pipeline_tag: sentence-similarity |
| tags: |
| - sentence-transformers |
| - feature-extraction |
| - sentence-similarity |
| - mteb |
| - arctic |
| - snowflake-arctic-embed |
| - transformers.js |
| model-index: |
| - name: snowflake-snowflake-arctic-embed-s |
| results: |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_counterfactual |
| name: MTEB AmazonCounterfactualClassification (en) |
| config: en |
| split: test |
| revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
| metrics: |
| - type: accuracy |
| value: 71.17910447761193 |
| - type: ap |
| value: 33.15833652904991 |
| - type: f1 |
| value: 64.86214791591543 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_polarity |
| name: MTEB AmazonPolarityClassification |
| config: default |
| split: test |
| revision: e2d317d38cd51312af73b3d32a06d1a08b442046 |
| metrics: |
| - type: accuracy |
| value: 78.750325 |
| - type: ap |
| value: 72.83242788470943 |
| - type: f1 |
| value: 78.63968044029453 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_reviews_multi |
| name: MTEB AmazonReviewsClassification (en) |
| config: en |
| split: test |
| revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
| metrics: |
| - type: accuracy |
| value: 38.264 |
| - type: f1 |
| value: 37.140269688532825 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/arguana |
| name: MTEB ArguAna |
| config: default |
| split: test |
| revision: c22ab2a51041ffd869aaddef7af8d8215647e41a |
| metrics: |
| - type: map_at_1 |
| value: 32.646 |
| - type: map_at_10 |
| value: 48.372 |
| - type: map_at_100 |
| value: 49.207 |
| - type: map_at_1000 |
| value: 49.214 |
| - type: map_at_3 |
| value: 43.611 |
| - type: map_at_5 |
| value: 46.601 |
| - type: mrr_at_1 |
| value: 33.144 |
| - type: mrr_at_10 |
| value: 48.557 |
| - type: mrr_at_100 |
| value: 49.385 |
| - type: mrr_at_1000 |
| value: 49.392 |
| - type: mrr_at_3 |
| value: 43.777 |
| - type: mrr_at_5 |
| value: 46.792 |
| - type: ndcg_at_1 |
| value: 32.646 |
| - type: ndcg_at_10 |
| value: 56.874 |
| - type: ndcg_at_100 |
| value: 60.307 |
| - type: ndcg_at_1000 |
| value: 60.465999999999994 |
| - type: ndcg_at_3 |
| value: 47.339999999999996 |
| - type: ndcg_at_5 |
| value: 52.685 |
| - type: precision_at_1 |
| value: 32.646 |
| - type: precision_at_10 |
| value: 8.378 |
| - type: precision_at_100 |
| value: 0.984 |
| - type: precision_at_1000 |
| value: 0.1 |
| - type: precision_at_3 |
| value: 19.393 |
| - type: precision_at_5 |
| value: 14.210999999999999 |
| - type: recall_at_1 |
| value: 32.646 |
| - type: recall_at_10 |
| value: 83.784 |
| - type: recall_at_100 |
| value: 98.43499999999999 |
| - type: recall_at_1000 |
| value: 99.644 |
| - type: recall_at_3 |
| value: 58.179 |
| - type: recall_at_5 |
| value: 71.053 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/arxiv-clustering-p2p |
| name: MTEB ArxivClusteringP2P |
| config: default |
| split: test |
| revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d |
| metrics: |
| - type: v_measure |
| value: 44.94353025039141 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/arxiv-clustering-s2s |
| name: MTEB ArxivClusteringS2S |
| config: default |
| split: test |
| revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 |
| metrics: |
| - type: v_measure |
| value: 35.870836103029156 |
| - task: |
| type: Reranking |
| dataset: |
| type: mteb/askubuntudupquestions-reranking |
| name: MTEB AskUbuntuDupQuestions |
| config: default |
| split: test |
| revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 |
| metrics: |
| - type: map |
| value: 61.149290266979236 |
| - type: mrr |
| value: 73.8448093919008 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/biosses-sts |
| name: MTEB BIOSSES |
| config: default |
| split: test |
| revision: d3fb88f8f02e40887cd149695127462bbcf29b4a |
| metrics: |
| - type: cos_sim_pearson |
| value: 87.055571064151 |
| - type: cos_sim_spearman |
| value: 86.2652186235749 |
| - type: euclidean_pearson |
| value: 85.82039272282503 |
| - type: euclidean_spearman |
| value: 86.2652186235749 |
| - type: manhattan_pearson |
| value: 85.95825392094812 |
| - type: manhattan_spearman |
| value: 86.6742640885316 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/banking77 |
| name: MTEB Banking77Classification |
| config: default |
| split: test |
| revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 |
| metrics: |
| - type: accuracy |
| value: 79.11688311688312 |
| - type: f1 |
| value: 78.28328901613885 |
| - task: |
| type: Clustering |
| dataset: |
| type: jinaai/big-patent-clustering |
| name: MTEB BigPatentClustering |
| config: default |
| split: test |
| revision: 62d5330920bca426ce9d3c76ea914f15fc83e891 |
| metrics: |
| - type: v_measure |
| value: 19.147523589859325 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/biorxiv-clustering-p2p |
| name: MTEB BiorxivClusteringP2P |
| config: default |
| split: test |
| revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 |
| metrics: |
| - type: v_measure |
| value: 35.68369864124274 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/biorxiv-clustering-s2s |
| name: MTEB BiorxivClusteringS2S |
| config: default |
| split: test |
| revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 |
| metrics: |
| - type: v_measure |
| value: 30.474958792950872 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/cqadupstack-android |
| name: MTEB CQADupstackAndroidRetrieval |
| config: default |
| split: test |
| revision: f46a197baaae43b4f621051089b82a364682dfeb |
| metrics: |
| - type: map_at_1 |
| value: 33.183 |
| - type: map_at_10 |
| value: 43.989 |
| - type: map_at_100 |
| value: 45.389 |
| - type: map_at_1000 |
| value: 45.517 |
| - type: map_at_3 |
| value: 40.275 |
| - type: map_at_5 |
| value: 42.306 |
| - type: mrr_at_1 |
| value: 40.486 |
| - type: mrr_at_10 |
| value: 49.62 |
| - type: mrr_at_100 |
| value: 50.351 |
| - type: mrr_at_1000 |
| value: 50.393 |
| - type: mrr_at_3 |
| value: 46.805 |
| - type: mrr_at_5 |
| value: 48.429 |
| - type: ndcg_at_1 |
| value: 40.486 |
| - type: ndcg_at_10 |
| value: 50.249 |
| - type: ndcg_at_100 |
| value: 55.206 |
| - type: ndcg_at_1000 |
| value: 57.145 |
| - type: ndcg_at_3 |
| value: 44.852 |
| - type: ndcg_at_5 |
| value: 47.355000000000004 |
| - type: precision_at_1 |
| value: 40.486 |
| - type: precision_at_10 |
| value: 9.571 |
| - type: precision_at_100 |
| value: 1.4949999999999999 |
| - type: precision_at_1000 |
| value: 0.196 |
| - type: precision_at_3 |
| value: 21.173000000000002 |
| - type: precision_at_5 |
| value: 15.622 |
| - type: recall_at_1 |
| value: 33.183 |
| - type: recall_at_10 |
| value: 62.134 |
| - type: recall_at_100 |
| value: 82.73 |
| - type: recall_at_1000 |
| value: 94.93599999999999 |
| - type: recall_at_3 |
| value: 46.497 |
| - type: recall_at_5 |
| value: 53.199 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/cqadupstack-english |
| name: MTEB CQADupstackEnglishRetrieval |
| config: default |
| split: test |
| revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 |
| metrics: |
| - type: map_at_1 |
| value: 32.862 |
| - type: map_at_10 |
| value: 42.439 |
| - type: map_at_100 |
| value: 43.736999999999995 |
| - type: map_at_1000 |
| value: 43.864 |
| - type: map_at_3 |
| value: 39.67 |
| - type: map_at_5 |
| value: 41.202 |
| - type: mrr_at_1 |
| value: 40.892 |
| - type: mrr_at_10 |
| value: 48.61 |
| - type: mrr_at_100 |
| value: 49.29 |
| - type: mrr_at_1000 |
| value: 49.332 |
| - type: mrr_at_3 |
| value: 46.688 |
| - type: mrr_at_5 |
| value: 47.803000000000004 |
| - type: ndcg_at_1 |
| value: 40.892 |
| - type: ndcg_at_10 |
| value: 47.797 |
| - type: ndcg_at_100 |
| value: 52.17699999999999 |
| - type: ndcg_at_1000 |
| value: 54.127 |
| - type: ndcg_at_3 |
| value: 44.189 |
| - type: ndcg_at_5 |
| value: 45.821 |
| - type: precision_at_1 |
| value: 40.892 |
| - type: precision_at_10 |
| value: 8.841000000000001 |
| - type: precision_at_100 |
| value: 1.419 |
| - type: precision_at_1000 |
| value: 0.188 |
| - type: precision_at_3 |
| value: 21.104 |
| - type: precision_at_5 |
| value: 14.777000000000001 |
| - type: recall_at_1 |
| value: 32.862 |
| - type: recall_at_10 |
| value: 56.352999999999994 |
| - type: recall_at_100 |
| value: 74.795 |
| - type: recall_at_1000 |
| value: 86.957 |
| - type: recall_at_3 |
| value: 45.269999999999996 |
| - type: recall_at_5 |
| value: 50.053000000000004 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/cqadupstack-gaming |
| name: MTEB CQADupstackGamingRetrieval |
| config: default |
| split: test |
| revision: 4885aa143210c98657558c04aaf3dc47cfb54340 |
| metrics: |
| - type: map_at_1 |
| value: 42.998999999999995 |
| - type: map_at_10 |
| value: 54.745 |
| - type: map_at_100 |
| value: 55.650999999999996 |
| - type: map_at_1000 |
| value: 55.703 |
| - type: map_at_3 |
| value: 51.67 |
| - type: map_at_5 |
| value: 53.503 |
| - type: mrr_at_1 |
| value: 49.028 |
| - type: mrr_at_10 |
| value: 58.172000000000004 |
| - type: mrr_at_100 |
| value: 58.744 |
| - type: mrr_at_1000 |
| value: 58.769000000000005 |
| - type: mrr_at_3 |
| value: 55.977 |
| - type: mrr_at_5 |
| value: 57.38799999999999 |
| - type: ndcg_at_1 |
| value: 49.028 |
| - type: ndcg_at_10 |
| value: 60.161 |
| - type: ndcg_at_100 |
| value: 63.806 |
| - type: ndcg_at_1000 |
| value: 64.821 |
| - type: ndcg_at_3 |
| value: 55.199 |
| - type: ndcg_at_5 |
| value: 57.830999999999996 |
| - type: precision_at_1 |
| value: 49.028 |
| - type: precision_at_10 |
| value: 9.455 |
| - type: precision_at_100 |
| value: 1.216 |
| - type: precision_at_1000 |
| value: 0.135 |
| - type: precision_at_3 |
| value: 24.242 |
| - type: precision_at_5 |
| value: 16.614 |
| - type: recall_at_1 |
| value: 42.998999999999995 |
| - type: recall_at_10 |
| value: 72.542 |
| - type: recall_at_100 |
| value: 88.605 |
| - type: recall_at_1000 |
| value: 95.676 |
| - type: recall_at_3 |
| value: 59.480999999999995 |
| - type: recall_at_5 |
| value: 65.886 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/cqadupstack-gis |
| name: MTEB CQADupstackGisRetrieval |
| config: default |
| split: test |
| revision: 5003b3064772da1887988e05400cf3806fe491f2 |
| metrics: |
| - type: map_at_1 |
| value: 27.907 |
| - type: map_at_10 |
| value: 35.975 |
| - type: map_at_100 |
| value: 36.985 |
| - type: map_at_1000 |
| value: 37.063 |
| - type: map_at_3 |
| value: 33.467999999999996 |
| - type: map_at_5 |
| value: 34.749 |
| - type: mrr_at_1 |
| value: 30.056 |
| - type: mrr_at_10 |
| value: 38.047 |
| - type: mrr_at_100 |
| value: 38.932 |
| - type: mrr_at_1000 |
| value: 38.991 |
| - type: mrr_at_3 |
| value: 35.705999999999996 |
| - type: mrr_at_5 |
| value: 36.966 |
| - type: ndcg_at_1 |
| value: 30.056 |
| - type: ndcg_at_10 |
| value: 40.631 |
| - type: ndcg_at_100 |
| value: 45.564 |
| - type: ndcg_at_1000 |
| value: 47.685 |
| - type: ndcg_at_3 |
| value: 35.748000000000005 |
| - type: ndcg_at_5 |
| value: 37.921 |
| - type: precision_at_1 |
| value: 30.056 |
| - type: precision_at_10 |
| value: 6.079 |
| - type: precision_at_100 |
| value: 0.898 |
| - type: precision_at_1000 |
| value: 0.11199999999999999 |
| - type: precision_at_3 |
| value: 14.727 |
| - type: precision_at_5 |
| value: 10.056 |
| - type: recall_at_1 |
| value: 27.907 |
| - type: recall_at_10 |
| value: 52.981 |
| - type: recall_at_100 |
| value: 75.53999999999999 |
| - type: recall_at_1000 |
| value: 91.759 |
| - type: recall_at_3 |
| value: 39.878 |
| - type: recall_at_5 |
| value: 45.077 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/cqadupstack-mathematica |
| name: MTEB CQADupstackMathematicaRetrieval |
| config: default |
| split: test |
| revision: 90fceea13679c63fe563ded68f3b6f06e50061de |
| metrics: |
| - type: map_at_1 |
| value: 16.764000000000003 |
| - type: map_at_10 |
| value: 24.294 |
| - type: map_at_100 |
| value: 25.507999999999996 |
| - type: map_at_1000 |
| value: 25.64 |
| - type: map_at_3 |
| value: 21.807000000000002 |
| - type: map_at_5 |
| value: 23.21 |
| - type: mrr_at_1 |
| value: 20.771 |
| - type: mrr_at_10 |
| value: 28.677000000000003 |
| - type: mrr_at_100 |
| value: 29.742 |
| - type: mrr_at_1000 |
| value: 29.816 |
| - type: mrr_at_3 |
| value: 26.327 |
| - type: mrr_at_5 |
| value: 27.639000000000003 |
| - type: ndcg_at_1 |
| value: 20.771 |
| - type: ndcg_at_10 |
| value: 29.21 |
| - type: ndcg_at_100 |
| value: 34.788000000000004 |
| - type: ndcg_at_1000 |
| value: 37.813 |
| - type: ndcg_at_3 |
| value: 24.632 |
| - type: ndcg_at_5 |
| value: 26.801000000000002 |
| - type: precision_at_1 |
| value: 20.771 |
| - type: precision_at_10 |
| value: 5.373 |
| - type: precision_at_100 |
| value: 0.923 |
| - type: precision_at_1000 |
| value: 0.133 |
| - type: precision_at_3 |
| value: 12.065 |
| - type: precision_at_5 |
| value: 8.706 |
| - type: recall_at_1 |
| value: 16.764000000000003 |
| - type: recall_at_10 |
| value: 40.072 |
| - type: recall_at_100 |
| value: 63.856 |
| - type: recall_at_1000 |
| value: 85.141 |
| - type: recall_at_3 |
| value: 27.308 |
| - type: recall_at_5 |
| value: 32.876 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/cqadupstack-physics |
| name: MTEB CQADupstackPhysicsRetrieval |
| config: default |
| split: test |
| revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 |
| metrics: |
| - type: map_at_1 |
| value: 31.194 |
| - type: map_at_10 |
| value: 40.731 |
| - type: map_at_100 |
| value: 42.073 |
| - type: map_at_1000 |
| value: 42.178 |
| - type: map_at_3 |
| value: 37.726 |
| - type: map_at_5 |
| value: 39.474 |
| - type: mrr_at_1 |
| value: 37.729 |
| - type: mrr_at_10 |
| value: 46.494 |
| - type: mrr_at_100 |
| value: 47.368 |
| - type: mrr_at_1000 |
| value: 47.407 |
| - type: mrr_at_3 |
| value: 44.224999999999994 |
| - type: mrr_at_5 |
| value: 45.582 |
| - type: ndcg_at_1 |
| value: 37.729 |
| - type: ndcg_at_10 |
| value: 46.312999999999995 |
| - type: ndcg_at_100 |
| value: 51.915 |
| - type: ndcg_at_1000 |
| value: 53.788000000000004 |
| - type: ndcg_at_3 |
| value: 41.695 |
| - type: ndcg_at_5 |
| value: 43.956 |
| - type: precision_at_1 |
| value: 37.729 |
| - type: precision_at_10 |
| value: 8.181 |
| - type: precision_at_100 |
| value: 1.275 |
| - type: precision_at_1000 |
| value: 0.16199999999999998 |
| - type: precision_at_3 |
| value: 19.41 |
| - type: precision_at_5 |
| value: 13.648 |
| - type: recall_at_1 |
| value: 31.194 |
| - type: recall_at_10 |
| value: 57.118 |
| - type: recall_at_100 |
| value: 80.759 |
| - type: recall_at_1000 |
| value: 92.779 |
| - type: recall_at_3 |
| value: 44.083 |
| - type: recall_at_5 |
| value: 50.044999999999995 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/cqadupstack-programmers |
| name: MTEB CQADupstackProgrammersRetrieval |
| config: default |
| split: test |
| revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 |
| metrics: |
| - type: map_at_1 |
| value: 28.047 |
| - type: map_at_10 |
| value: 37.79 |
| - type: map_at_100 |
| value: 39.145 |
| - type: map_at_1000 |
| value: 39.254 |
| - type: map_at_3 |
| value: 34.857 |
| - type: map_at_5 |
| value: 36.545 |
| - type: mrr_at_1 |
| value: 35.388 |
| - type: mrr_at_10 |
| value: 43.475 |
| - type: mrr_at_100 |
| value: 44.440000000000005 |
| - type: mrr_at_1000 |
| value: 44.494 |
| - type: mrr_at_3 |
| value: 41.286 |
| - type: mrr_at_5 |
| value: 42.673 |
| - type: ndcg_at_1 |
| value: 35.388 |
| - type: ndcg_at_10 |
| value: 43.169000000000004 |
| - type: ndcg_at_100 |
| value: 48.785000000000004 |
| - type: ndcg_at_1000 |
| value: 51.029 |
| - type: ndcg_at_3 |
| value: 38.801 |
| - type: ndcg_at_5 |
| value: 40.9 |
| - type: precision_at_1 |
| value: 35.388 |
| - type: precision_at_10 |
| value: 7.7509999999999994 |
| - type: precision_at_100 |
| value: 1.212 |
| - type: precision_at_1000 |
| value: 0.157 |
| - type: precision_at_3 |
| value: 18.455 |
| - type: precision_at_5 |
| value: 13.014000000000001 |
| - type: recall_at_1 |
| value: 28.047 |
| - type: recall_at_10 |
| value: 53.53099999999999 |
| - type: recall_at_100 |
| value: 77.285 |
| - type: recall_at_1000 |
| value: 92.575 |
| - type: recall_at_3 |
| value: 40.949000000000005 |
| - type: recall_at_5 |
| value: 46.742 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/cqadupstack |
| name: MTEB CQADupstackRetrieval |
| config: default |
| split: test |
| revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 |
| metrics: |
| - type: map_at_1 |
| value: 28.131999999999994 |
| - type: map_at_10 |
| value: 36.93333333333334 |
| - type: map_at_100 |
| value: 38.117250000000006 |
| - type: map_at_1000 |
| value: 38.23275 |
| - type: map_at_3 |
| value: 34.19708333333333 |
| - type: map_at_5 |
| value: 35.725166666666674 |
| - type: mrr_at_1 |
| value: 33.16116666666667 |
| - type: mrr_at_10 |
| value: 41.057833333333335 |
| - type: mrr_at_100 |
| value: 41.90033333333333 |
| - type: mrr_at_1000 |
| value: 41.95625 |
| - type: mrr_at_3 |
| value: 38.757333333333335 |
| - type: mrr_at_5 |
| value: 40.097333333333324 |
| - type: ndcg_at_1 |
| value: 33.16116666666667 |
| - type: ndcg_at_10 |
| value: 42.01983333333333 |
| - type: ndcg_at_100 |
| value: 46.99916666666667 |
| - type: ndcg_at_1000 |
| value: 49.21783333333334 |
| - type: ndcg_at_3 |
| value: 37.479916666666654 |
| - type: ndcg_at_5 |
| value: 39.6355 |
| - type: precision_at_1 |
| value: 33.16116666666667 |
| - type: precision_at_10 |
| value: 7.230249999999999 |
| - type: precision_at_100 |
| value: 1.1411666666666667 |
| - type: precision_at_1000 |
| value: 0.1520833333333333 |
| - type: precision_at_3 |
| value: 17.028166666666667 |
| - type: precision_at_5 |
| value: 12.046999999999999 |
| - type: recall_at_1 |
| value: 28.131999999999994 |
| - type: recall_at_10 |
| value: 52.825500000000005 |
| - type: recall_at_100 |
| value: 74.59608333333333 |
| - type: recall_at_1000 |
| value: 89.87916666666668 |
| - type: recall_at_3 |
| value: 40.13625 |
| - type: recall_at_5 |
| value: 45.699999999999996 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/cqadupstack-stats |
| name: MTEB CQADupstackStatsRetrieval |
| config: default |
| split: test |
| revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a |
| metrics: |
| - type: map_at_1 |
| value: 24.773999999999997 |
| - type: map_at_10 |
| value: 31.997999999999998 |
| - type: map_at_100 |
| value: 32.857 |
| - type: map_at_1000 |
| value: 32.957 |
| - type: map_at_3 |
| value: 30.041 |
| - type: map_at_5 |
| value: 31.119000000000003 |
| - type: mrr_at_1 |
| value: 27.607 |
| - type: mrr_at_10 |
| value: 34.538000000000004 |
| - type: mrr_at_100 |
| value: 35.308 |
| - type: mrr_at_1000 |
| value: 35.375 |
| - type: mrr_at_3 |
| value: 32.643 |
| - type: mrr_at_5 |
| value: 33.755 |
| - type: ndcg_at_1 |
| value: 27.607 |
| - type: ndcg_at_10 |
| value: 36.035000000000004 |
| - type: ndcg_at_100 |
| value: 40.351 |
| - type: ndcg_at_1000 |
| value: 42.684 |
| - type: ndcg_at_3 |
| value: 32.414 |
| - type: ndcg_at_5 |
| value: 34.11 |
| - type: precision_at_1 |
| value: 27.607 |
| - type: precision_at_10 |
| value: 5.6129999999999995 |
| - type: precision_at_100 |
| value: 0.8370000000000001 |
| - type: precision_at_1000 |
| value: 0.11199999999999999 |
| - type: precision_at_3 |
| value: 13.957 |
| - type: precision_at_5 |
| value: 9.571 |
| - type: recall_at_1 |
| value: 24.773999999999997 |
| - type: recall_at_10 |
| value: 45.717 |
| - type: recall_at_100 |
| value: 65.499 |
| - type: recall_at_1000 |
| value: 82.311 |
| - type: recall_at_3 |
| value: 35.716 |
| - type: recall_at_5 |
| value: 40.007999999999996 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/cqadupstack-tex |
| name: MTEB CQADupstackTexRetrieval |
| config: default |
| split: test |
| revision: 46989137a86843e03a6195de44b09deda022eec7 |
| metrics: |
| - type: map_at_1 |
| value: 19.227 |
| - type: map_at_10 |
| value: 26.649 |
| - type: map_at_100 |
| value: 27.711999999999996 |
| - type: map_at_1000 |
| value: 27.837 |
| - type: map_at_3 |
| value: 24.454 |
| - type: map_at_5 |
| value: 25.772000000000002 |
| - type: mrr_at_1 |
| value: 23.433999999999997 |
| - type: mrr_at_10 |
| value: 30.564999999999998 |
| - type: mrr_at_100 |
| value: 31.44 |
| - type: mrr_at_1000 |
| value: 31.513999999999996 |
| - type: mrr_at_3 |
| value: 28.435 |
| - type: mrr_at_5 |
| value: 29.744999999999997 |
| - type: ndcg_at_1 |
| value: 23.433999999999997 |
| - type: ndcg_at_10 |
| value: 31.104 |
| - type: ndcg_at_100 |
| value: 36.172 |
| - type: ndcg_at_1000 |
| value: 39.006 |
| - type: ndcg_at_3 |
| value: 27.248 |
| - type: ndcg_at_5 |
| value: 29.249000000000002 |
| - type: precision_at_1 |
| value: 23.433999999999997 |
| - type: precision_at_10 |
| value: 5.496 |
| - type: precision_at_100 |
| value: 0.9490000000000001 |
| - type: precision_at_1000 |
| value: 0.13699999999999998 |
| - type: precision_at_3 |
| value: 12.709000000000001 |
| - type: precision_at_5 |
| value: 9.209 |
| - type: recall_at_1 |
| value: 19.227 |
| - type: recall_at_10 |
| value: 40.492 |
| - type: recall_at_100 |
| value: 63.304 |
| - type: recall_at_1000 |
| value: 83.45 |
| - type: recall_at_3 |
| value: 29.713 |
| - type: recall_at_5 |
| value: 34.82 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/cqadupstack-unix |
| name: MTEB CQADupstackUnixRetrieval |
| config: default |
| split: test |
| revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 |
| metrics: |
| - type: map_at_1 |
| value: 29.199 |
| - type: map_at_10 |
| value: 37.617 |
| - type: map_at_100 |
| value: 38.746 |
| - type: map_at_1000 |
| value: 38.851 |
| - type: map_at_3 |
| value: 34.882000000000005 |
| - type: map_at_5 |
| value: 36.571999999999996 |
| - type: mrr_at_1 |
| value: 33.489000000000004 |
| - type: mrr_at_10 |
| value: 41.089999999999996 |
| - type: mrr_at_100 |
| value: 41.965 |
| - type: mrr_at_1000 |
| value: 42.028 |
| - type: mrr_at_3 |
| value: 38.666 |
| - type: mrr_at_5 |
| value: 40.159 |
| - type: ndcg_at_1 |
| value: 33.489000000000004 |
| - type: ndcg_at_10 |
| value: 42.487 |
| - type: ndcg_at_100 |
| value: 47.552 |
| - type: ndcg_at_1000 |
| value: 49.774 |
| - type: ndcg_at_3 |
| value: 37.623 |
| - type: ndcg_at_5 |
| value: 40.184999999999995 |
| - type: precision_at_1 |
| value: 33.489000000000004 |
| - type: precision_at_10 |
| value: 6.94 |
| - type: precision_at_100 |
| value: 1.0699999999999998 |
| - type: precision_at_1000 |
| value: 0.136 |
| - type: precision_at_3 |
| value: 16.667 |
| - type: precision_at_5 |
| value: 11.922 |
| - type: recall_at_1 |
| value: 29.199 |
| - type: recall_at_10 |
| value: 53.689 |
| - type: recall_at_100 |
| value: 75.374 |
| - type: recall_at_1000 |
| value: 90.64999999999999 |
| - type: recall_at_3 |
| value: 40.577999999999996 |
| - type: recall_at_5 |
| value: 46.909 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/cqadupstack-webmasters |
| name: MTEB CQADupstackWebmastersRetrieval |
| config: default |
| split: test |
| revision: 160c094312a0e1facb97e55eeddb698c0abe3571 |
| metrics: |
| - type: map_at_1 |
| value: 27.206999999999997 |
| - type: map_at_10 |
| value: 36.146 |
| - type: map_at_100 |
| value: 37.759 |
| - type: map_at_1000 |
| value: 37.979 |
| - type: map_at_3 |
| value: 32.967999999999996 |
| - type: map_at_5 |
| value: 34.809 |
| - type: mrr_at_1 |
| value: 32.806000000000004 |
| - type: mrr_at_10 |
| value: 40.449 |
| - type: mrr_at_100 |
| value: 41.404999999999994 |
| - type: mrr_at_1000 |
| value: 41.457 |
| - type: mrr_at_3 |
| value: 37.614999999999995 |
| - type: mrr_at_5 |
| value: 39.324999999999996 |
| - type: ndcg_at_1 |
| value: 32.806000000000004 |
| - type: ndcg_at_10 |
| value: 41.911 |
| - type: ndcg_at_100 |
| value: 47.576 |
| - type: ndcg_at_1000 |
| value: 50.072 |
| - type: ndcg_at_3 |
| value: 36.849 |
| - type: ndcg_at_5 |
| value: 39.475 |
| - type: precision_at_1 |
| value: 32.806000000000004 |
| - type: precision_at_10 |
| value: 8.103 |
| - type: precision_at_100 |
| value: 1.557 |
| - type: precision_at_1000 |
| value: 0.242 |
| - type: precision_at_3 |
| value: 17.26 |
| - type: precision_at_5 |
| value: 12.885 |
| - type: recall_at_1 |
| value: 27.206999999999997 |
| - type: recall_at_10 |
| value: 52.56999999999999 |
| - type: recall_at_100 |
| value: 78.302 |
| - type: recall_at_1000 |
| value: 94.121 |
| - type: recall_at_3 |
| value: 38.317 |
| - type: recall_at_5 |
| value: 45.410000000000004 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/cqadupstack-wordpress |
| name: MTEB CQADupstackWordpressRetrieval |
| config: default |
| split: test |
| revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 |
| metrics: |
| - type: map_at_1 |
| value: 24.221 |
| - type: map_at_10 |
| value: 30.826999999999998 |
| - type: map_at_100 |
| value: 31.845000000000002 |
| - type: map_at_1000 |
| value: 31.95 |
| - type: map_at_3 |
| value: 28.547 |
| - type: map_at_5 |
| value: 29.441 |
| - type: mrr_at_1 |
| value: 26.247999999999998 |
| - type: mrr_at_10 |
| value: 32.957 |
| - type: mrr_at_100 |
| value: 33.819 |
| - type: mrr_at_1000 |
| value: 33.899 |
| - type: mrr_at_3 |
| value: 30.714999999999996 |
| - type: mrr_at_5 |
| value: 31.704 |
| - type: ndcg_at_1 |
| value: 26.247999999999998 |
| - type: ndcg_at_10 |
| value: 35.171 |
| - type: ndcg_at_100 |
| value: 40.098 |
| - type: ndcg_at_1000 |
| value: 42.67 |
| - type: ndcg_at_3 |
| value: 30.508999999999997 |
| - type: ndcg_at_5 |
| value: 32.022 |
| - type: precision_at_1 |
| value: 26.247999999999998 |
| - type: precision_at_10 |
| value: 5.36 |
| - type: precision_at_100 |
| value: 0.843 |
| - type: precision_at_1000 |
| value: 0.11499999999999999 |
| - type: precision_at_3 |
| value: 12.568999999999999 |
| - type: precision_at_5 |
| value: 8.540000000000001 |
| - type: recall_at_1 |
| value: 24.221 |
| - type: recall_at_10 |
| value: 46.707 |
| - type: recall_at_100 |
| value: 69.104 |
| - type: recall_at_1000 |
| value: 88.19500000000001 |
| - type: recall_at_3 |
| value: 33.845 |
| - type: recall_at_5 |
| value: 37.375 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/climate-fever |
| name: MTEB ClimateFEVER |
| config: default |
| split: test |
| revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 |
| metrics: |
| - type: map_at_1 |
| value: 13.624 |
| - type: map_at_10 |
| value: 22.557 |
| - type: map_at_100 |
| value: 24.367 |
| - type: map_at_1000 |
| value: 24.54 |
| - type: map_at_3 |
| value: 18.988 |
| - type: map_at_5 |
| value: 20.785999999999998 |
| - type: mrr_at_1 |
| value: 30.619000000000003 |
| - type: mrr_at_10 |
| value: 42.019 |
| - type: mrr_at_100 |
| value: 42.818 |
| - type: mrr_at_1000 |
| value: 42.856 |
| - type: mrr_at_3 |
| value: 38.578 |
| - type: mrr_at_5 |
| value: 40.669 |
| - type: ndcg_at_1 |
| value: 30.619000000000003 |
| - type: ndcg_at_10 |
| value: 31.252999999999997 |
| - type: ndcg_at_100 |
| value: 38.238 |
| - type: ndcg_at_1000 |
| value: 41.368 |
| - type: ndcg_at_3 |
| value: 25.843 |
| - type: ndcg_at_5 |
| value: 27.638 |
| - type: precision_at_1 |
| value: 30.619000000000003 |
| - type: precision_at_10 |
| value: 9.687 |
| - type: precision_at_100 |
| value: 1.718 |
| - type: precision_at_1000 |
| value: 0.22999999999999998 |
| - type: precision_at_3 |
| value: 18.849 |
| - type: precision_at_5 |
| value: 14.463000000000001 |
| - type: recall_at_1 |
| value: 13.624 |
| - type: recall_at_10 |
| value: 36.693999999999996 |
| - type: recall_at_100 |
| value: 60.9 |
| - type: recall_at_1000 |
| value: 78.46 |
| - type: recall_at_3 |
| value: 23.354 |
| - type: recall_at_5 |
| value: 28.756999999999998 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/dbpedia |
| name: MTEB DBPedia |
| config: default |
| split: test |
| revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 |
| metrics: |
| - type: map_at_1 |
| value: 9.077 |
| - type: map_at_10 |
| value: 19.813 |
| - type: map_at_100 |
| value: 27.822999999999997 |
| - type: map_at_1000 |
| value: 29.485 |
| - type: map_at_3 |
| value: 14.255999999999998 |
| - type: map_at_5 |
| value: 16.836000000000002 |
| - type: mrr_at_1 |
| value: 69.25 |
| - type: mrr_at_10 |
| value: 77.059 |
| - type: mrr_at_100 |
| value: 77.41 |
| - type: mrr_at_1000 |
| value: 77.416 |
| - type: mrr_at_3 |
| value: 75.625 |
| - type: mrr_at_5 |
| value: 76.512 |
| - type: ndcg_at_1 |
| value: 55.75 |
| - type: ndcg_at_10 |
| value: 41.587 |
| - type: ndcg_at_100 |
| value: 46.048 |
| - type: ndcg_at_1000 |
| value: 53.172 |
| - type: ndcg_at_3 |
| value: 46.203 |
| - type: ndcg_at_5 |
| value: 43.696 |
| - type: precision_at_1 |
| value: 69.25 |
| - type: precision_at_10 |
| value: 32.95 |
| - type: precision_at_100 |
| value: 10.555 |
| - type: precision_at_1000 |
| value: 2.136 |
| - type: precision_at_3 |
| value: 49.667 |
| - type: precision_at_5 |
| value: 42.5 |
| - type: recall_at_1 |
| value: 9.077 |
| - type: recall_at_10 |
| value: 25.249 |
| - type: recall_at_100 |
| value: 51.964 |
| - type: recall_at_1000 |
| value: 74.51 |
| - type: recall_at_3 |
| value: 15.584000000000001 |
| - type: recall_at_5 |
| value: 19.717000000000002 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/emotion |
| name: MTEB EmotionClassification |
| config: default |
| split: test |
| revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 |
| metrics: |
| - type: accuracy |
| value: 45.769999999999996 |
| - type: f1 |
| value: 41.64144711933962 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/fever |
| name: MTEB FEVER |
| config: default |
| split: test |
| revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 |
| metrics: |
| - type: map_at_1 |
| value: 67.098 |
| - type: map_at_10 |
| value: 77.69800000000001 |
| - type: map_at_100 |
| value: 77.947 |
| - type: map_at_1000 |
| value: 77.961 |
| - type: map_at_3 |
| value: 76.278 |
| - type: map_at_5 |
| value: 77.217 |
| - type: mrr_at_1 |
| value: 72.532 |
| - type: mrr_at_10 |
| value: 82.41199999999999 |
| - type: mrr_at_100 |
| value: 82.527 |
| - type: mrr_at_1000 |
| value: 82.529 |
| - type: mrr_at_3 |
| value: 81.313 |
| - type: mrr_at_5 |
| value: 82.069 |
| - type: ndcg_at_1 |
| value: 72.532 |
| - type: ndcg_at_10 |
| value: 82.488 |
| - type: ndcg_at_100 |
| value: 83.382 |
| - type: ndcg_at_1000 |
| value: 83.622 |
| - type: ndcg_at_3 |
| value: 80.101 |
| - type: ndcg_at_5 |
| value: 81.52199999999999 |
| - type: precision_at_1 |
| value: 72.532 |
| - type: precision_at_10 |
| value: 10.203 |
| - type: precision_at_100 |
| value: 1.082 |
| - type: precision_at_1000 |
| value: 0.11199999999999999 |
| - type: precision_at_3 |
| value: 31.308000000000003 |
| - type: precision_at_5 |
| value: 19.652 |
| - type: recall_at_1 |
| value: 67.098 |
| - type: recall_at_10 |
| value: 92.511 |
| - type: recall_at_100 |
| value: 96.06099999999999 |
| - type: recall_at_1000 |
| value: 97.548 |
| - type: recall_at_3 |
| value: 86.105 |
| - type: recall_at_5 |
| value: 89.661 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/fiqa |
| name: MTEB FiQA2018 |
| config: default |
| split: test |
| revision: 27a168819829fe9bcd655c2df245fb19452e8e06 |
| metrics: |
| - type: map_at_1 |
| value: 18.681 |
| - type: map_at_10 |
| value: 31.739 |
| - type: map_at_100 |
| value: 33.503 |
| - type: map_at_1000 |
| value: 33.69 |
| - type: map_at_3 |
| value: 27.604 |
| - type: map_at_5 |
| value: 29.993 |
| - type: mrr_at_1 |
| value: 37.5 |
| - type: mrr_at_10 |
| value: 46.933 |
| - type: mrr_at_100 |
| value: 47.771 |
| - type: mrr_at_1000 |
| value: 47.805 |
| - type: mrr_at_3 |
| value: 44.239 |
| - type: mrr_at_5 |
| value: 45.766 |
| - type: ndcg_at_1 |
| value: 37.5 |
| - type: ndcg_at_10 |
| value: 39.682 |
| - type: ndcg_at_100 |
| value: 46.127 |
| - type: ndcg_at_1000 |
| value: 48.994 |
| - type: ndcg_at_3 |
| value: 35.655 |
| - type: ndcg_at_5 |
| value: 37.036 |
| - type: precision_at_1 |
| value: 37.5 |
| - type: precision_at_10 |
| value: 11.08 |
| - type: precision_at_100 |
| value: 1.765 |
| - type: precision_at_1000 |
| value: 0.22999999999999998 |
| - type: precision_at_3 |
| value: 23.919999999999998 |
| - type: precision_at_5 |
| value: 17.809 |
| - type: recall_at_1 |
| value: 18.681 |
| - type: recall_at_10 |
| value: 47.548 |
| - type: recall_at_100 |
| value: 71.407 |
| - type: recall_at_1000 |
| value: 87.805 |
| - type: recall_at_3 |
| value: 32.979 |
| - type: recall_at_5 |
| value: 39.192 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/hotpotqa |
| name: MTEB HotpotQA |
| config: default |
| split: test |
| revision: ab518f4d6fcca38d87c25209f94beba119d02014 |
| metrics: |
| - type: map_at_1 |
| value: 38.257999999999996 |
| - type: map_at_10 |
| value: 57.605 |
| - type: map_at_100 |
| value: 58.50300000000001 |
| - type: map_at_1000 |
| value: 58.568 |
| - type: map_at_3 |
| value: 54.172 |
| - type: map_at_5 |
| value: 56.323 |
| - type: mrr_at_1 |
| value: 76.51599999999999 |
| - type: mrr_at_10 |
| value: 82.584 |
| - type: mrr_at_100 |
| value: 82.78 |
| - type: mrr_at_1000 |
| value: 82.787 |
| - type: mrr_at_3 |
| value: 81.501 |
| - type: mrr_at_5 |
| value: 82.185 |
| - type: ndcg_at_1 |
| value: 76.51599999999999 |
| - type: ndcg_at_10 |
| value: 66.593 |
| - type: ndcg_at_100 |
| value: 69.699 |
| - type: ndcg_at_1000 |
| value: 70.953 |
| - type: ndcg_at_3 |
| value: 61.673 |
| - type: ndcg_at_5 |
| value: 64.42 |
| - type: precision_at_1 |
| value: 76.51599999999999 |
| - type: precision_at_10 |
| value: 13.857 |
| - type: precision_at_100 |
| value: 1.628 |
| - type: precision_at_1000 |
| value: 0.179 |
| - type: precision_at_3 |
| value: 38.956 |
| - type: precision_at_5 |
| value: 25.541999999999998 |
| - type: recall_at_1 |
| value: 38.257999999999996 |
| - type: recall_at_10 |
| value: 69.284 |
| - type: recall_at_100 |
| value: 81.391 |
| - type: recall_at_1000 |
| value: 89.689 |
| - type: recall_at_3 |
| value: 58.433 |
| - type: recall_at_5 |
| value: 63.856 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/imdb |
| name: MTEB ImdbClassification |
| config: default |
| split: test |
| revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 |
| metrics: |
| - type: accuracy |
| value: 69.48679999999999 |
| - type: ap |
| value: 63.97638838971138 |
| - type: f1 |
| value: 69.22731638841675 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/msmarco |
| name: MTEB MSMARCO |
| config: default |
| split: dev |
| revision: c5a29a104738b98a9e76336939199e264163d4a0 |
| metrics: |
| - type: map_at_1 |
| value: 20.916999999999998 |
| - type: map_at_10 |
| value: 32.929 |
| - type: map_at_100 |
| value: 34.1 |
| - type: map_at_1000 |
| value: 34.152 |
| - type: map_at_3 |
| value: 29.065 |
| - type: map_at_5 |
| value: 31.287 |
| - type: mrr_at_1 |
| value: 21.562 |
| - type: mrr_at_10 |
| value: 33.533 |
| - type: mrr_at_100 |
| value: 34.644000000000005 |
| - type: mrr_at_1000 |
| value: 34.69 |
| - type: mrr_at_3 |
| value: 29.735 |
| - type: mrr_at_5 |
| value: 31.928 |
| - type: ndcg_at_1 |
| value: 21.562 |
| - type: ndcg_at_10 |
| value: 39.788000000000004 |
| - type: ndcg_at_100 |
| value: 45.434999999999995 |
| - type: ndcg_at_1000 |
| value: 46.75 |
| - type: ndcg_at_3 |
| value: 31.942999999999998 |
| - type: ndcg_at_5 |
| value: 35.888 |
| - type: precision_at_1 |
| value: 21.562 |
| - type: precision_at_10 |
| value: 6.348 |
| - type: precision_at_100 |
| value: 0.918 |
| - type: precision_at_1000 |
| value: 0.10300000000000001 |
| - type: precision_at_3 |
| value: 13.682 |
| - type: precision_at_5 |
| value: 10.189 |
| - type: recall_at_1 |
| value: 20.916999999999998 |
| - type: recall_at_10 |
| value: 60.926 |
| - type: recall_at_100 |
| value: 87.03800000000001 |
| - type: recall_at_1000 |
| value: 97.085 |
| - type: recall_at_3 |
| value: 39.637 |
| - type: recall_at_5 |
| value: 49.069 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/mtop_domain |
| name: MTEB MTOPDomainClassification (en) |
| config: en |
| split: test |
| revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
| metrics: |
| - type: accuracy |
| value: 90.93935248518011 |
| - type: f1 |
| value: 90.56439321844506 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/mtop_intent |
| name: MTEB MTOPIntentClassification (en) |
| config: en |
| split: test |
| revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
| metrics: |
| - type: accuracy |
| value: 58.62517099863203 |
| - type: f1 |
| value: 40.69925681703197 |
| - task: |
| type: Classification |
| dataset: |
| type: masakhane/masakhanews |
| name: MTEB MasakhaNEWSClassification (eng) |
| config: eng |
| split: test |
| revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60 |
| metrics: |
| - type: accuracy |
| value: 76.29746835443039 |
| - type: f1 |
| value: 75.31702672039506 |
| - task: |
| type: Clustering |
| dataset: |
| type: masakhane/masakhanews |
| name: MTEB MasakhaNEWSClusteringP2P (eng) |
| config: eng |
| split: test |
| revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60 |
| metrics: |
| - type: v_measure |
| value: 43.05495067062023 |
| - task: |
| type: Clustering |
| dataset: |
| type: masakhane/masakhanews |
| name: MTEB MasakhaNEWSClusteringS2S (eng) |
| config: eng |
| split: test |
| revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60 |
| metrics: |
| - type: v_measure |
| value: 19.625272848173843 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (en) |
| config: en |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
| - type: accuracy |
| value: 64.76126429051781 |
| - type: f1 |
| value: 62.60284261265268 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_scenario |
| name: MTEB MassiveScenarioClassification (en) |
| config: en |
| split: test |
| revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| metrics: |
| - type: accuracy |
| value: 70.05043712172159 |
| - type: f1 |
| value: 69.08340521169049 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/medrxiv-clustering-p2p |
| name: MTEB MedrxivClusteringP2P |
| config: default |
| split: test |
| revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 |
| metrics: |
| - type: v_measure |
| value: 30.78969229005989 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/medrxiv-clustering-s2s |
| name: MTEB MedrxivClusteringS2S |
| config: default |
| split: test |
| revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 |
| metrics: |
| - type: v_measure |
| value: 27.954325178520335 |
| - task: |
| type: Reranking |
| dataset: |
| type: mteb/mind_small |
| name: MTEB MindSmallReranking |
| config: default |
| split: test |
| revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 |
| metrics: |
| - type: map |
| value: 30.601827413968596 |
| - type: mrr |
| value: 31.515372019474196 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/nfcorpus |
| name: MTEB NFCorpus |
| config: default |
| split: test |
| revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 |
| metrics: |
| - type: map_at_1 |
| value: 5.4559999999999995 |
| - type: map_at_10 |
| value: 12.039 |
| - type: map_at_100 |
| value: 14.804999999999998 |
| - type: map_at_1000 |
| value: 16.081 |
| - type: map_at_3 |
| value: 8.996 |
| - type: map_at_5 |
| value: 10.357 |
| - type: mrr_at_1 |
| value: 45.82 |
| - type: mrr_at_10 |
| value: 53.583999999999996 |
| - type: mrr_at_100 |
| value: 54.330999999999996 |
| - type: mrr_at_1000 |
| value: 54.366 |
| - type: mrr_at_3 |
| value: 52.166999999999994 |
| - type: mrr_at_5 |
| value: 52.971999999999994 |
| - type: ndcg_at_1 |
| value: 44.427 |
| - type: ndcg_at_10 |
| value: 32.536 |
| - type: ndcg_at_100 |
| value: 29.410999999999998 |
| - type: ndcg_at_1000 |
| value: 38.012 |
| - type: ndcg_at_3 |
| value: 38.674 |
| - type: ndcg_at_5 |
| value: 36.107 |
| - type: precision_at_1 |
| value: 45.82 |
| - type: precision_at_10 |
| value: 23.591 |
| - type: precision_at_100 |
| value: 7.35 |
| - type: precision_at_1000 |
| value: 1.9769999999999999 |
| - type: precision_at_3 |
| value: 36.016999999999996 |
| - type: precision_at_5 |
| value: 30.959999999999997 |
| - type: recall_at_1 |
| value: 5.4559999999999995 |
| - type: recall_at_10 |
| value: 15.387 |
| - type: recall_at_100 |
| value: 28.754999999999995 |
| - type: recall_at_1000 |
| value: 59.787 |
| - type: recall_at_3 |
| value: 10.137 |
| - type: recall_at_5 |
| value: 12.200999999999999 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/nq |
| name: MTEB NQ |
| config: default |
| split: test |
| revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 |
| metrics: |
| - type: map_at_1 |
| value: 32.609 |
| - type: map_at_10 |
| value: 48.522 |
| - type: map_at_100 |
| value: 49.468 |
| - type: map_at_1000 |
| value: 49.497 |
| - type: map_at_3 |
| value: 44.327 |
| - type: map_at_5 |
| value: 46.937 |
| - type: mrr_at_1 |
| value: 36.616 |
| - type: mrr_at_10 |
| value: 50.943000000000005 |
| - type: mrr_at_100 |
| value: 51.626000000000005 |
| - type: mrr_at_1000 |
| value: 51.647 |
| - type: mrr_at_3 |
| value: 47.532999999999994 |
| - type: mrr_at_5 |
| value: 49.714000000000006 |
| - type: ndcg_at_1 |
| value: 36.586999999999996 |
| - type: ndcg_at_10 |
| value: 56.19499999999999 |
| - type: ndcg_at_100 |
| value: 60.014 |
| - type: ndcg_at_1000 |
| value: 60.707 |
| - type: ndcg_at_3 |
| value: 48.486000000000004 |
| - type: ndcg_at_5 |
| value: 52.791999999999994 |
| - type: precision_at_1 |
| value: 36.586999999999996 |
| - type: precision_at_10 |
| value: 9.139999999999999 |
| - type: precision_at_100 |
| value: 1.129 |
| - type: precision_at_1000 |
| value: 0.11900000000000001 |
| - type: precision_at_3 |
| value: 22.171 |
| - type: precision_at_5 |
| value: 15.787999999999998 |
| - type: recall_at_1 |
| value: 32.609 |
| - type: recall_at_10 |
| value: 77.011 |
| - type: recall_at_100 |
| value: 93.202 |
| - type: recall_at_1000 |
| value: 98.344 |
| - type: recall_at_3 |
| value: 57.286 |
| - type: recall_at_5 |
| value: 67.181 |
| - task: |
| type: Classification |
| dataset: |
| type: ag_news |
| name: MTEB NewsClassification |
| config: default |
| split: test |
| revision: eb185aade064a813bc0b7f42de02595523103ca4 |
| metrics: |
| - type: accuracy |
| value: 77.4421052631579 |
| - type: f1 |
| value: 77.23976860913628 |
| - 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: 61.25000000000001 |
| - type: cos_sim_ap |
| value: 59.23166242799505 |
| - type: cos_sim_f1 |
| value: 62.53016201309893 |
| - type: cos_sim_precision |
| value: 45.486459378134406 |
| - type: cos_sim_recall |
| value: 100 |
| - type: dot_accuracy |
| value: 61.25000000000001 |
| - type: dot_ap |
| value: 59.23109306756652 |
| - type: dot_f1 |
| value: 62.53016201309893 |
| - type: dot_precision |
| value: 45.486459378134406 |
| - type: dot_recall |
| value: 100 |
| - type: euclidean_accuracy |
| value: 61.25000000000001 |
| - type: euclidean_ap |
| value: 59.23166242799505 |
| - type: euclidean_f1 |
| value: 62.53016201309893 |
| - type: euclidean_precision |
| value: 45.486459378134406 |
| - type: euclidean_recall |
| value: 100 |
| - type: manhattan_accuracy |
| value: 61.25000000000001 |
| - type: manhattan_ap |
| value: 59.23015114712089 |
| - type: manhattan_f1 |
| value: 62.50861474844934 |
| - type: manhattan_precision |
| value: 45.46365914786967 |
| - type: manhattan_recall |
| value: 100 |
| - type: max_accuracy |
| value: 61.25000000000001 |
| - type: max_ap |
| value: 59.23166242799505 |
| - type: max_f1 |
| value: 62.53016201309893 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/quora |
| name: MTEB QuoraRetrieval |
| config: default |
| split: test |
| revision: e4e08e0b7dbe3c8700f0daef558ff32256715259 |
| metrics: |
| - type: map_at_1 |
| value: 69.919 |
| - type: map_at_10 |
| value: 83.636 |
| - type: map_at_100 |
| value: 84.27 |
| - type: map_at_1000 |
| value: 84.289 |
| - type: map_at_3 |
| value: 80.744 |
| - type: map_at_5 |
| value: 82.509 |
| - type: mrr_at_1 |
| value: 80.52 |
| - type: mrr_at_10 |
| value: 86.751 |
| - type: mrr_at_100 |
| value: 86.875 |
| - type: mrr_at_1000 |
| value: 86.876 |
| - type: mrr_at_3 |
| value: 85.798 |
| - type: mrr_at_5 |
| value: 86.414 |
| - type: ndcg_at_1 |
| value: 80.53 |
| - type: ndcg_at_10 |
| value: 87.465 |
| - type: ndcg_at_100 |
| value: 88.762 |
| - type: ndcg_at_1000 |
| value: 88.90599999999999 |
| - type: ndcg_at_3 |
| value: 84.634 |
| - type: ndcg_at_5 |
| value: 86.09400000000001 |
| - type: precision_at_1 |
| value: 80.53 |
| - type: precision_at_10 |
| value: 13.263 |
| - type: precision_at_100 |
| value: 1.517 |
| - type: precision_at_1000 |
| value: 0.156 |
| - type: precision_at_3 |
| value: 36.973 |
| - type: precision_at_5 |
| value: 24.25 |
| - type: recall_at_1 |
| value: 69.919 |
| - type: recall_at_10 |
| value: 94.742 |
| - type: recall_at_100 |
| value: 99.221 |
| - type: recall_at_1000 |
| value: 99.917 |
| - type: recall_at_3 |
| value: 86.506 |
| - type: recall_at_5 |
| value: 90.736 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/reddit-clustering |
| name: MTEB RedditClustering |
| config: default |
| split: test |
| revision: 24640382cdbf8abc73003fb0fa6d111a705499eb |
| metrics: |
| - type: v_measure |
| value: 50.47309147963901 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/reddit-clustering-p2p |
| name: MTEB RedditClusteringP2P |
| config: default |
| split: test |
| revision: 385e3cb46b4cfa89021f56c4380204149d0efe33 |
| metrics: |
| - type: v_measure |
| value: 60.53779561923047 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/scidocs |
| name: MTEB SCIDOCS |
| config: default |
| split: test |
| revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88 |
| metrics: |
| - type: map_at_1 |
| value: 4.843 |
| - type: map_at_10 |
| value: 11.664 |
| - type: map_at_100 |
| value: 13.499 |
| - type: map_at_1000 |
| value: 13.771 |
| - type: map_at_3 |
| value: 8.602 |
| - type: map_at_5 |
| value: 10.164 |
| - type: mrr_at_1 |
| value: 23.9 |
| - type: mrr_at_10 |
| value: 34.018 |
| - type: mrr_at_100 |
| value: 35.099000000000004 |
| - type: mrr_at_1000 |
| value: 35.162 |
| - type: mrr_at_3 |
| value: 31.233 |
| - type: mrr_at_5 |
| value: 32.793 |
| - type: ndcg_at_1 |
| value: 23.9 |
| - type: ndcg_at_10 |
| value: 19.42 |
| - type: ndcg_at_100 |
| value: 26.715 |
| - type: ndcg_at_1000 |
| value: 31.776 |
| - type: ndcg_at_3 |
| value: 19.165 |
| - type: ndcg_at_5 |
| value: 16.46 |
| - type: precision_at_1 |
| value: 23.9 |
| - type: precision_at_10 |
| value: 9.82 |
| - type: precision_at_100 |
| value: 2.0340000000000003 |
| - type: precision_at_1000 |
| value: 0.325 |
| - type: precision_at_3 |
| value: 17.767 |
| - type: precision_at_5 |
| value: 14.24 |
| - type: recall_at_1 |
| value: 4.843 |
| - type: recall_at_10 |
| value: 19.895 |
| - type: recall_at_100 |
| value: 41.302 |
| - type: recall_at_1000 |
| value: 66.077 |
| - type: recall_at_3 |
| value: 10.803 |
| - type: recall_at_5 |
| value: 14.418000000000001 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sickr-sts |
| name: MTEB SICK-R |
| config: default |
| split: test |
| revision: 20a6d6f312dd54037fe07a32d58e5e168867909d |
| metrics: |
| - type: cos_sim_pearson |
| value: 76.94120735638143 |
| - type: cos_sim_spearman |
| value: 69.66114097154585 |
| - type: euclidean_pearson |
| value: 73.11242035696426 |
| - type: euclidean_spearman |
| value: 69.66114271982464 |
| - type: manhattan_pearson |
| value: 73.07993034858605 |
| - type: manhattan_spearman |
| value: 69.6457893357314 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts12-sts |
| name: MTEB STS12 |
| config: default |
| split: test |
| revision: a0d554a64d88156834ff5ae9920b964011b16384 |
| metrics: |
| - type: cos_sim_pearson |
| value: 74.72893353272778 |
| - type: cos_sim_spearman |
| value: 68.78540928870311 |
| - type: euclidean_pearson |
| value: 71.13907970605574 |
| - type: euclidean_spearman |
| value: 68.78540928870311 |
| - type: manhattan_pearson |
| value: 71.02709590547859 |
| - type: manhattan_spearman |
| value: 68.71685896660532 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts13-sts |
| name: MTEB STS13 |
| config: default |
| split: test |
| revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca |
| metrics: |
| - type: cos_sim_pearson |
| value: 79.30142652684971 |
| - type: cos_sim_spearman |
| value: 79.61879435615303 |
| - type: euclidean_pearson |
| value: 79.08730432883864 |
| - type: euclidean_spearman |
| value: 79.61879435615303 |
| - type: manhattan_pearson |
| value: 78.99621073156322 |
| - type: manhattan_spearman |
| value: 79.53806342308278 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts14-sts |
| name: MTEB STS14 |
| config: default |
| split: test |
| revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 |
| metrics: |
| - type: cos_sim_pearson |
| value: 78.99585233036139 |
| - type: cos_sim_spearman |
| value: 75.57574519760183 |
| - type: euclidean_pearson |
| value: 77.33835658613162 |
| - type: euclidean_spearman |
| value: 75.57573873503655 |
| - type: manhattan_pearson |
| value: 77.12175044789362 |
| - type: manhattan_spearman |
| value: 75.41293517634836 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts15-sts |
| name: MTEB STS15 |
| config: default |
| split: test |
| revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 |
| metrics: |
| - type: cos_sim_pearson |
| value: 83.9694268253376 |
| - type: cos_sim_spearman |
| value: 84.64256921939338 |
| - type: euclidean_pearson |
| value: 83.92322958711 |
| - type: euclidean_spearman |
| value: 84.64257976421872 |
| - type: manhattan_pearson |
| value: 83.93503107204337 |
| - type: manhattan_spearman |
| value: 84.63611608236032 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts16-sts |
| name: MTEB STS16 |
| config: default |
| split: test |
| revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 |
| metrics: |
| - type: cos_sim_pearson |
| value: 81.09041419790253 |
| - type: cos_sim_spearman |
| value: 82.39869157752557 |
| - type: euclidean_pearson |
| value: 82.04595698258301 |
| - type: euclidean_spearman |
| value: 82.39869157752557 |
| - type: manhattan_pearson |
| value: 81.97581168053004 |
| - type: manhattan_spearman |
| value: 82.34255320578193 |
| - 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: 86.35210432821825 |
| - type: cos_sim_spearman |
| value: 86.73200885328937 |
| - type: euclidean_pearson |
| value: 86.8527089168747 |
| - type: euclidean_spearman |
| value: 86.73200885328937 |
| - type: manhattan_pearson |
| value: 86.95671235295457 |
| - type: manhattan_spearman |
| value: 86.77713700838545 |
| - 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: 68.91106612960657 |
| - type: cos_sim_spearman |
| value: 69.48524490302286 |
| - type: euclidean_pearson |
| value: 70.51347841618035 |
| - type: euclidean_spearman |
| value: 69.48524490302286 |
| - type: manhattan_pearson |
| value: 70.31770181334245 |
| - type: manhattan_spearman |
| value: 69.12494700138238 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/stsbenchmark-sts |
| name: MTEB STSBenchmark |
| config: default |
| split: test |
| revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 |
| metrics: |
| - type: cos_sim_pearson |
| value: 81.54104342761988 |
| - type: cos_sim_spearman |
| value: 81.18789220331483 |
| - type: euclidean_pearson |
| value: 81.5895544590969 |
| - type: euclidean_spearman |
| value: 81.18789220331483 |
| - type: manhattan_pearson |
| value: 81.4738562449809 |
| - type: manhattan_spearman |
| value: 81.06565101416024 |
| - 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: 81.54104346197056 |
| - type: cos_sim_spearman |
| value: 81.18789220331483 |
| - type: euclidean_pearson |
| value: 81.58955451690102 |
| - type: euclidean_spearman |
| value: 81.18789220331483 |
| - type: manhattan_pearson |
| value: 81.47385630064072 |
| - type: manhattan_spearman |
| value: 81.06565101416024 |
| - task: |
| type: Reranking |
| dataset: |
| type: mteb/scidocs-reranking |
| name: MTEB SciDocsRR |
| config: default |
| split: test |
| revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab |
| metrics: |
| - type: map |
| value: 79.34107964300796 |
| - type: mrr |
| value: 94.01917889662987 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/scifact |
| name: MTEB SciFact |
| config: default |
| split: test |
| revision: 0228b52cf27578f30900b9e5271d331663a030d7 |
| metrics: |
| - type: map_at_1 |
| value: 55.928 |
| - type: map_at_10 |
| value: 65.443 |
| - type: map_at_100 |
| value: 66.067 |
| - type: map_at_1000 |
| value: 66.091 |
| - type: map_at_3 |
| value: 62.629999999999995 |
| - type: map_at_5 |
| value: 64.35 |
| - type: mrr_at_1 |
| value: 59 |
| - type: mrr_at_10 |
| value: 66.845 |
| - type: mrr_at_100 |
| value: 67.31899999999999 |
| - type: mrr_at_1000 |
| value: 67.342 |
| - type: mrr_at_3 |
| value: 64.61099999999999 |
| - type: mrr_at_5 |
| value: 66.044 |
| - type: ndcg_at_1 |
| value: 59 |
| - type: ndcg_at_10 |
| value: 69.921 |
| - type: ndcg_at_100 |
| value: 72.365 |
| - type: ndcg_at_1000 |
| value: 73.055 |
| - type: ndcg_at_3 |
| value: 65.086 |
| - type: ndcg_at_5 |
| value: 67.62700000000001 |
| - type: precision_at_1 |
| value: 59 |
| - type: precision_at_10 |
| value: 9.3 |
| - type: precision_at_100 |
| value: 1.057 |
| - type: precision_at_1000 |
| value: 0.11100000000000002 |
| - type: precision_at_3 |
| value: 25.333 |
| - type: precision_at_5 |
| value: 16.866999999999997 |
| - type: recall_at_1 |
| value: 55.928 |
| - type: recall_at_10 |
| value: 82.289 |
| - type: recall_at_100 |
| value: 92.833 |
| - type: recall_at_1000 |
| value: 98.333 |
| - type: recall_at_3 |
| value: 69.172 |
| - type: recall_at_5 |
| value: 75.628 |
| - 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: 95.2776439040401 |
| - type: cos_sim_f1 |
| value: 90.74355083459787 |
| - type: cos_sim_precision |
| value: 91.81166837256909 |
| - type: cos_sim_recall |
| value: 89.7 |
| - type: dot_accuracy |
| value: 99.81881188118813 |
| - type: dot_ap |
| value: 95.27764092100406 |
| - type: dot_f1 |
| value: 90.74355083459787 |
| - type: dot_precision |
| value: 91.81166837256909 |
| - type: dot_recall |
| value: 89.7 |
| - type: euclidean_accuracy |
| value: 99.81881188118813 |
| - type: euclidean_ap |
| value: 95.27764091101388 |
| - type: euclidean_f1 |
| value: 90.74355083459787 |
| - type: euclidean_precision |
| value: 91.81166837256909 |
| - type: euclidean_recall |
| value: 89.7 |
| - type: manhattan_accuracy |
| value: 99.82079207920792 |
| - type: manhattan_ap |
| value: 95.25081634689418 |
| - type: manhattan_f1 |
| value: 90.75114971895759 |
| - type: manhattan_precision |
| value: 92.78996865203762 |
| - type: manhattan_recall |
| value: 88.8 |
| - type: max_accuracy |
| value: 99.82079207920792 |
| - type: max_ap |
| value: 95.2776439040401 |
| - type: max_f1 |
| value: 90.75114971895759 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/stackexchange-clustering |
| name: MTEB StackExchangeClustering |
| config: default |
| split: test |
| revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 |
| metrics: |
| - type: v_measure |
| value: 60.69855369728728 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/stackexchange-clustering-p2p |
| name: MTEB StackExchangeClusteringP2P |
| config: default |
| split: test |
| revision: 815ca46b2622cec33ccafc3735d572c266efdb44 |
| metrics: |
| - type: v_measure |
| value: 33.98191834367251 |
| - task: |
| type: Reranking |
| dataset: |
| type: mteb/stackoverflowdupquestions-reranking |
| name: MTEB StackOverflowDupQuestions |
| config: default |
| split: test |
| revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 |
| metrics: |
| - type: map |
| value: 50.156163330429614 |
| - type: mrr |
| value: 50.90145148968678 |
| - task: |
| type: Summarization |
| dataset: |
| type: mteb/summeval |
| name: MTEB SummEval |
| config: default |
| split: test |
| revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c |
| metrics: |
| - type: cos_sim_pearson |
| value: 31.16938079808134 |
| - type: cos_sim_spearman |
| value: 31.74655874538245 |
| - type: dot_pearson |
| value: 31.169380299671705 |
| - type: dot_spearman |
| value: 31.74655874538245 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/trec-covid |
| name: MTEB TRECCOVID |
| config: default |
| split: test |
| revision: bb9466bac8153a0349341eb1b22e06409e78ef4e |
| metrics: |
| - type: map_at_1 |
| value: 0.252 |
| - type: map_at_10 |
| value: 2.009 |
| - type: map_at_100 |
| value: 11.611 |
| - type: map_at_1000 |
| value: 27.811999999999998 |
| - type: map_at_3 |
| value: 0.685 |
| - type: map_at_5 |
| value: 1.08 |
| - type: mrr_at_1 |
| value: 94 |
| - type: mrr_at_10 |
| value: 97 |
| - type: mrr_at_100 |
| value: 97 |
| - type: mrr_at_1000 |
| value: 97 |
| - type: mrr_at_3 |
| value: 97 |
| - type: mrr_at_5 |
| value: 97 |
| - type: ndcg_at_1 |
| value: 88 |
| - type: ndcg_at_10 |
| value: 81.388 |
| - type: ndcg_at_100 |
| value: 60.629 |
| - type: ndcg_at_1000 |
| value: 52.38 |
| - type: ndcg_at_3 |
| value: 86.827 |
| - type: ndcg_at_5 |
| value: 84.597 |
| - type: precision_at_1 |
| value: 94 |
| - type: precision_at_10 |
| value: 85.8 |
| - type: precision_at_100 |
| value: 62.419999999999995 |
| - type: precision_at_1000 |
| value: 23.31 |
| - type: precision_at_3 |
| value: 90.667 |
| - type: precision_at_5 |
| value: 88.4 |
| - type: recall_at_1 |
| value: 0.252 |
| - type: recall_at_10 |
| value: 2.164 |
| - type: recall_at_100 |
| value: 14.613999999999999 |
| - type: recall_at_1000 |
| value: 48.730000000000004 |
| - type: recall_at_3 |
| value: 0.7020000000000001 |
| - type: recall_at_5 |
| value: 1.122 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/touche2020 |
| name: MTEB Touche2020 |
| config: default |
| split: test |
| revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f |
| metrics: |
| - type: map_at_1 |
| value: 3.476 |
| - type: map_at_10 |
| value: 13.442000000000002 |
| - type: map_at_100 |
| value: 20.618 |
| - type: map_at_1000 |
| value: 22.175 |
| - type: map_at_3 |
| value: 6.968000000000001 |
| - type: map_at_5 |
| value: 9.214 |
| - type: mrr_at_1 |
| value: 44.897999999999996 |
| - type: mrr_at_10 |
| value: 56.77100000000001 |
| - type: mrr_at_100 |
| value: 57.226 |
| - type: mrr_at_1000 |
| value: 57.226 |
| - type: mrr_at_3 |
| value: 52.381 |
| - type: mrr_at_5 |
| value: 54.523999999999994 |
| - type: ndcg_at_1 |
| value: 42.857 |
| - type: ndcg_at_10 |
| value: 32.507999999999996 |
| - type: ndcg_at_100 |
| value: 43.614000000000004 |
| - type: ndcg_at_1000 |
| value: 53.82 |
| - type: ndcg_at_3 |
| value: 36.818 |
| - type: ndcg_at_5 |
| value: 33.346 |
| - type: precision_at_1 |
| value: 44.897999999999996 |
| - type: precision_at_10 |
| value: 28.571 |
| - type: precision_at_100 |
| value: 8.652999999999999 |
| - type: precision_at_1000 |
| value: 1.5709999999999997 |
| - type: precision_at_3 |
| value: 38.095 |
| - type: precision_at_5 |
| value: 32.245000000000005 |
| - type: recall_at_1 |
| value: 3.476 |
| - type: recall_at_10 |
| value: 20.827 |
| - type: recall_at_100 |
| value: 53.04299999999999 |
| - type: recall_at_1000 |
| value: 84.221 |
| - type: recall_at_3 |
| value: 8.200000000000001 |
| - type: recall_at_5 |
| value: 11.651 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/toxic_conversations_50k |
| name: MTEB ToxicConversationsClassification |
| config: default |
| split: test |
| revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de |
| metrics: |
| - type: accuracy |
| value: 61.96360000000001 |
| - type: ap |
| value: 11.256160324436445 |
| - type: f1 |
| value: 48.07712827691349 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/tweet_sentiment_extraction |
| name: MTEB TweetSentimentExtractionClassification |
| config: default |
| split: test |
| revision: d604517c81ca91fe16a244d1248fc021f9ecee7a |
| metrics: |
| - type: accuracy |
| value: 58.90492359932088 |
| - type: f1 |
| value: 59.12542417513503 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/twentynewsgroups-clustering |
| name: MTEB TwentyNewsgroupsClustering |
| config: default |
| split: test |
| revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 |
| metrics: |
| - type: v_measure |
| value: 38.284935353315355 |
| - task: |
| type: PairClassification |
| dataset: |
| type: mteb/twittersemeval2015-pairclassification |
| name: MTEB TwitterSemEval2015 |
| config: default |
| split: test |
| revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 |
| metrics: |
| - type: cos_sim_accuracy |
| value: 83.4714192048638 |
| - type: cos_sim_ap |
| value: 65.77588263185375 |
| - type: cos_sim_f1 |
| value: 62.459508098380326 |
| - type: cos_sim_precision |
| value: 57.27172717271727 |
| - type: cos_sim_recall |
| value: 68.68073878627968 |
| - type: dot_accuracy |
| value: 83.4714192048638 |
| - type: dot_ap |
| value: 65.77588818364636 |
| - type: dot_f1 |
| value: 62.459508098380326 |
| - type: dot_precision |
| value: 57.27172717271727 |
| - type: dot_recall |
| value: 68.68073878627968 |
| - type: euclidean_accuracy |
| value: 83.4714192048638 |
| - type: euclidean_ap |
| value: 65.77587693431595 |
| - type: euclidean_f1 |
| value: 62.459508098380326 |
| - type: euclidean_precision |
| value: 57.27172717271727 |
| - type: euclidean_recall |
| value: 68.68073878627968 |
| - type: manhattan_accuracy |
| value: 83.47737974608094 |
| - type: manhattan_ap |
| value: 65.65957745829654 |
| - type: manhattan_f1 |
| value: 62.22760290556902 |
| - type: manhattan_precision |
| value: 57.494407158836694 |
| - type: manhattan_recall |
| value: 67.81002638522428 |
| - type: max_accuracy |
| value: 83.47737974608094 |
| - type: max_ap |
| value: 65.77588818364636 |
| - type: max_f1 |
| value: 62.459508098380326 |
| - task: |
| type: PairClassification |
| dataset: |
| type: mteb/twitterurlcorpus-pairclassification |
| name: MTEB TwitterURLCorpus |
| config: default |
| split: test |
| revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf |
| metrics: |
| - type: cos_sim_accuracy |
| value: 88.64244964489463 |
| - type: cos_sim_ap |
| value: 85.154122301394 |
| - type: cos_sim_f1 |
| value: 77.45617911327146 |
| - type: cos_sim_precision |
| value: 74.23066064370413 |
| - type: cos_sim_recall |
| value: 80.97474591931014 |
| - type: dot_accuracy |
| value: 88.64244964489463 |
| - type: dot_ap |
| value: 85.15411965587543 |
| - type: dot_f1 |
| value: 77.45617911327146 |
| - type: dot_precision |
| value: 74.23066064370413 |
| - type: dot_recall |
| value: 80.97474591931014 |
| - type: euclidean_accuracy |
| value: 88.64244964489463 |
| - type: euclidean_ap |
| value: 85.15414684113986 |
| - type: euclidean_f1 |
| value: 77.45617911327146 |
| - type: euclidean_precision |
| value: 74.23066064370413 |
| - type: euclidean_recall |
| value: 80.97474591931014 |
| - type: manhattan_accuracy |
| value: 88.57841425078588 |
| - type: manhattan_ap |
| value: 85.12472268567576 |
| - type: manhattan_f1 |
| value: 77.39497339937627 |
| - type: manhattan_precision |
| value: 73.92584285413892 |
| - type: manhattan_recall |
| value: 81.20572836464429 |
| - type: max_accuracy |
| value: 88.64244964489463 |
| - type: max_ap |
| value: 85.15414684113986 |
| - type: max_f1 |
| value: 77.45617911327146 |
| - task: |
| type: Clustering |
| dataset: |
| type: jinaai/cities_wiki_clustering |
| name: MTEB WikiCitiesClustering |
| config: default |
| split: test |
| revision: ddc9ee9242fa65332597f70e967ecc38b9d734fa |
| metrics: |
| - type: v_measure |
| value: 79.58576208710117 |
| license: apache-2.0 |
| --- |
| <h1 align="center">Snowflake's Arctic-embed-s</h1> |
| <h4 align="center"> |
| <p> |
| <a href=#news>News</a> | |
| <a href=#models>Models</a> | |
| <a href=#usage>Usage</a> | |
| <a href="#evaluation">Evaluation</a> | |
| <a href="#contact">Contact</a> | |
| <a href="#faq">FAQ</a> |
| <a href="#license">License</a> | |
| <a href="#acknowledgement">Acknowledgement</a> |
| <p> |
| </h4> |
| |
|
|
| ## 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). |
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| ## Models |
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| snowflake-arctic-embed is a suite of text embedding models that focuses on creating high-quality retrieval models optimized for performance. |
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| 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. |
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| 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). |
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| | 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 | |
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| 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. |
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| | 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 | |
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| ### [snowflake-arctic-embed-xs](https://huggingface.co/Snowflake/snowflake-arctic-embed-xs) |
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| 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. |
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| | 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 | |
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| ### [snowflake-arctic-embed-s](https://huggingface.co/Snowflake/snowflake-arctic-embed-s) |
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| 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. |
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| | 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 | |
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| ### [snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m/) |
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| 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. |
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| | 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 | |
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| ### [snowflake-arctic-embed-m-long](https://huggingface.co/Snowflake/snowflake-arctic-embed-m-long/) |
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| 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! |
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| | 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 | |
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| ### [snowflake-arctic-embed-l](https://huggingface.co/Snowflake/snowflake-arctic-embed-l/) |
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| 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. |
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| | 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 | |
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| ## Usage |
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| ### Using Sentence Transformers |
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| You can use the sentence-transformers package to use an snowflake-arctic-embed model, as shown below. |
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| ```python |
| from sentence_transformers import SentenceTransformer |
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| model = SentenceTransformer("Snowflake/snowflake-arctic-embed-s") |
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| queries = ['what is snowflake?', 'Where can I get the best tacos?'] |
| documents = ['The Data Cloud!', 'Mexico City of Course!'] |
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| query_embeddings = model.encode(queries, prompt_name="query") |
| document_embeddings = model.encode(documents) |
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| 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) |
| ``` |
| ``` |
| Query: what is snowflake? |
| 0.533809 The Data Cloud! |
| 0.49207097 Mexico City of Course! |
| Query: Where can I get the best tacos? |
| 0.56592476 Mexico City of Course! |
| 0.48255116 The Data Cloud! |
| ``` |
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| ### Using Huggingface transformers |
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| 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). |
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| ```python |
| import torch |
| from transformers import AutoModel, AutoTokenizer |
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| tokenizer = AutoTokenizer.from_pretrained('Snowflake/snowflake-arctic-embed-s') |
| model = AutoModel.from_pretrained('Snowflake/snowflake-arctic-embed-s', add_pooling_layer=False) |
| model.eval() |
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| 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) |
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| documents = ['The Data Cloud!', 'Mexico City of Course!'] |
| document_tokens = tokenizer(documents, padding=True, truncation=True, return_tensors='pt', max_length=512) |
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| # Compute token embeddings |
| with torch.no_grad(): |
| query_embeddings = model(**query_tokens)[0][:, 0] |
| document_embeddings = model(**document_tokens)[0][:, 0] |
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| # 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) |
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| 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) |
| ``` |
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| ### Using Transformers.js |
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| 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 |
| ``` |
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| You can then use the model to compute embeddings as follows: |
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| ```js |
| import { pipeline, dot } from '@xenova/transformers'; |
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| // Create feature extraction pipeline |
| const extractor = await pipeline('feature-extraction', 'Snowflake/snowflake-arctic-embed-s', { |
| quantized: false, // Comment out this line to use the quantized version |
| }); |
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| // 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' }); |
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| // Compute similarity scores |
| const [source_embeddings, ...document_embeddings ] = output.tolist(); |
| const similarities = document_embeddings.map(x => dot(source_embeddings, x)); |
| console.log(similarities); // [0.48255123876493394, 0.5659250100112143] |
| ``` |
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| ## FAQ |
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| TBD |
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| ## Contact |
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| 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). |
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| ## License |
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| 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. |
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| ## Acknowledgement |
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| 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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| <img referrerpolicy="no-referrer-when-downgrade" src="https://static.scarf.sh/a.png?x-pxid=26ca7731-2650-44be-942d-0c6809cfcf00" /> |
