| --- |
| 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-xs |
| results: |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_counterfactual |
| name: MTEB AmazonCounterfactualClassification (en) |
| config: en |
| split: test |
| revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
| metrics: |
| - type: accuracy |
| value: 65.08955223880598 |
| - type: ap |
| value: 28.514291209445364 |
| - type: f1 |
| value: 59.2604580112738 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_polarity |
| name: MTEB AmazonPolarityClassification |
| config: default |
| split: test |
| revision: e2d317d38cd51312af73b3d32a06d1a08b442046 |
| metrics: |
| - type: accuracy |
| value: 70.035375 |
| - type: ap |
| value: 64.29444264250405 |
| - type: f1 |
| value: 69.78382333907138 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_reviews_multi |
| name: MTEB AmazonReviewsClassification (en) |
| config: en |
| split: test |
| revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
| metrics: |
| - type: accuracy |
| value: 35.343999999999994 |
| - type: f1 |
| value: 34.69618251902858 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/arguana |
| name: MTEB ArguAna |
| config: default |
| split: test |
| revision: c22ab2a51041ffd869aaddef7af8d8215647e41a |
| metrics: |
| - type: map_at_1 |
| value: 28.592000000000002 |
| - type: map_at_10 |
| value: 43.597 |
| - type: map_at_100 |
| value: 44.614 |
| - type: map_at_1000 |
| value: 44.624 |
| - type: map_at_3 |
| value: 38.928000000000004 |
| - type: map_at_5 |
| value: 41.453 |
| - type: mrr_at_1 |
| value: 29.232000000000003 |
| - type: mrr_at_10 |
| value: 43.829 |
| - type: mrr_at_100 |
| value: 44.852 |
| - type: mrr_at_1000 |
| value: 44.862 |
| - type: mrr_at_3 |
| value: 39.118 |
| - type: mrr_at_5 |
| value: 41.703 |
| - type: ndcg_at_1 |
| value: 28.592000000000002 |
| - type: ndcg_at_10 |
| value: 52.081 |
| - type: ndcg_at_100 |
| value: 56.37 |
| - type: ndcg_at_1000 |
| value: 56.598000000000006 |
| - type: ndcg_at_3 |
| value: 42.42 |
| - type: ndcg_at_5 |
| value: 46.965 |
| - type: precision_at_1 |
| value: 28.592000000000002 |
| - type: precision_at_10 |
| value: 7.922999999999999 |
| - type: precision_at_100 |
| value: 0.979 |
| - type: precision_at_1000 |
| value: 0.1 |
| - type: precision_at_3 |
| value: 17.52 |
| - type: precision_at_5 |
| value: 12.717 |
| - type: recall_at_1 |
| value: 28.592000000000002 |
| - type: recall_at_10 |
| value: 79.232 |
| - type: recall_at_100 |
| value: 97.866 |
| - type: recall_at_1000 |
| value: 99.57300000000001 |
| - type: recall_at_3 |
| value: 52.559999999999995 |
| - type: recall_at_5 |
| value: 63.585 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/arxiv-clustering-p2p |
| name: MTEB ArxivClusteringP2P |
| config: default |
| split: test |
| revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d |
| metrics: |
| - type: v_measure |
| value: 43.50220588953974 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/arxiv-clustering-s2s |
| name: MTEB ArxivClusteringS2S |
| config: default |
| split: test |
| revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 |
| metrics: |
| - type: v_measure |
| value: 32.08725826118282 |
| - task: |
| type: Reranking |
| dataset: |
| type: mteb/askubuntudupquestions-reranking |
| name: MTEB AskUbuntuDupQuestions |
| config: default |
| split: test |
| revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 |
| metrics: |
| - type: map |
| value: 60.25381587694928 |
| - type: mrr |
| value: 73.79776194873148 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/biosses-sts |
| name: MTEB BIOSSES |
| config: default |
| split: test |
| revision: d3fb88f8f02e40887cd149695127462bbcf29b4a |
| metrics: |
| - type: cos_sim_pearson |
| value: 85.47489332445278 |
| - type: cos_sim_spearman |
| value: 84.05432487336698 |
| - type: euclidean_pearson |
| value: 84.5108222177219 |
| - type: euclidean_spearman |
| value: 84.05432487336698 |
| - type: manhattan_pearson |
| value: 84.20440618321464 |
| - type: manhattan_spearman |
| value: 83.9290208134097 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/banking77 |
| name: MTEB Banking77Classification |
| config: default |
| split: test |
| revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 |
| metrics: |
| - type: accuracy |
| value: 76.37337662337663 |
| - type: f1 |
| value: 75.33296834885043 |
| - task: |
| type: Clustering |
| dataset: |
| type: jinaai/big-patent-clustering |
| name: MTEB BigPatentClustering |
| config: default |
| split: test |
| revision: 62d5330920bca426ce9d3c76ea914f15fc83e891 |
| metrics: |
| - type: v_measure |
| value: 21.31174373264835 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/biorxiv-clustering-p2p |
| name: MTEB BiorxivClusteringP2P |
| config: default |
| split: test |
| revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 |
| metrics: |
| - type: v_measure |
| value: 34.481973521597844 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/biorxiv-clustering-s2s |
| name: MTEB BiorxivClusteringS2S |
| config: default |
| split: test |
| revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 |
| metrics: |
| - type: v_measure |
| value: 26.14094256567341 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/cqadupstack-android |
| name: MTEB CQADupstackAndroidRetrieval |
| config: default |
| split: test |
| revision: f46a197baaae43b4f621051089b82a364682dfeb |
| metrics: |
| - type: map_at_1 |
| value: 32.527 |
| - type: map_at_10 |
| value: 43.699 |
| - type: map_at_100 |
| value: 45.03 |
| - type: map_at_1000 |
| value: 45.157000000000004 |
| - type: map_at_3 |
| value: 39.943 |
| - type: map_at_5 |
| value: 42.324 |
| - type: mrr_at_1 |
| value: 39.771 |
| - type: mrr_at_10 |
| value: 49.277 |
| - type: mrr_at_100 |
| value: 49.956 |
| - type: mrr_at_1000 |
| value: 50.005 |
| - type: mrr_at_3 |
| value: 46.304 |
| - type: mrr_at_5 |
| value: 48.493 |
| - type: ndcg_at_1 |
| value: 39.771 |
| - type: ndcg_at_10 |
| value: 49.957 |
| - type: ndcg_at_100 |
| value: 54.678000000000004 |
| - type: ndcg_at_1000 |
| value: 56.751 |
| - type: ndcg_at_3 |
| value: 44.608 |
| - type: ndcg_at_5 |
| value: 47.687000000000005 |
| - type: precision_at_1 |
| value: 39.771 |
| - type: precision_at_10 |
| value: 9.557 |
| - type: precision_at_100 |
| value: 1.5010000000000001 |
| - type: precision_at_1000 |
| value: 0.194 |
| - type: precision_at_3 |
| value: 21.173000000000002 |
| - type: precision_at_5 |
| value: 15.794 |
| - type: recall_at_1 |
| value: 32.527 |
| - type: recall_at_10 |
| value: 61.791 |
| - type: recall_at_100 |
| value: 81.49300000000001 |
| - type: recall_at_1000 |
| value: 95.014 |
| - type: recall_at_3 |
| value: 46.605000000000004 |
| - type: recall_at_5 |
| value: 54.83 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/cqadupstack-english |
| name: MTEB CQADupstackEnglishRetrieval |
| config: default |
| split: test |
| revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 |
| metrics: |
| - type: map_at_1 |
| value: 29.424 |
| - type: map_at_10 |
| value: 38.667 |
| - type: map_at_100 |
| value: 39.771 |
| - type: map_at_1000 |
| value: 39.899 |
| - type: map_at_3 |
| value: 35.91 |
| - type: map_at_5 |
| value: 37.45 |
| - type: mrr_at_1 |
| value: 36.687999999999995 |
| - type: mrr_at_10 |
| value: 44.673 |
| - type: mrr_at_100 |
| value: 45.289 |
| - type: mrr_at_1000 |
| value: 45.338 |
| - type: mrr_at_3 |
| value: 42.601 |
| - type: mrr_at_5 |
| value: 43.875 |
| - type: ndcg_at_1 |
| value: 36.687999999999995 |
| - type: ndcg_at_10 |
| value: 44.013000000000005 |
| - type: ndcg_at_100 |
| value: 48.13 |
| - type: ndcg_at_1000 |
| value: 50.294000000000004 |
| - type: ndcg_at_3 |
| value: 40.056999999999995 |
| - type: ndcg_at_5 |
| value: 41.902 |
| - type: precision_at_1 |
| value: 36.687999999999995 |
| - type: precision_at_10 |
| value: 8.158999999999999 |
| - type: precision_at_100 |
| value: 1.321 |
| - type: precision_at_1000 |
| value: 0.179 |
| - type: precision_at_3 |
| value: 19.045 |
| - type: precision_at_5 |
| value: 13.427 |
| - type: recall_at_1 |
| value: 29.424 |
| - type: recall_at_10 |
| value: 53.08500000000001 |
| - type: recall_at_100 |
| value: 70.679 |
| - type: recall_at_1000 |
| value: 84.66 |
| - type: recall_at_3 |
| value: 41.399 |
| - type: recall_at_5 |
| value: 46.632 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/cqadupstack-gaming |
| name: MTEB CQADupstackGamingRetrieval |
| config: default |
| split: test |
| revision: 4885aa143210c98657558c04aaf3dc47cfb54340 |
| metrics: |
| - type: map_at_1 |
| value: 39.747 |
| - type: map_at_10 |
| value: 51.452 |
| - type: map_at_100 |
| value: 52.384 |
| - type: map_at_1000 |
| value: 52.437 |
| - type: map_at_3 |
| value: 48.213 |
| - type: map_at_5 |
| value: 50.195 |
| - type: mrr_at_1 |
| value: 45.391999999999996 |
| - type: mrr_at_10 |
| value: 54.928 |
| - type: mrr_at_100 |
| value: 55.532000000000004 |
| - type: mrr_at_1000 |
| value: 55.565 |
| - type: mrr_at_3 |
| value: 52.456 |
| - type: mrr_at_5 |
| value: 54.054 |
| - type: ndcg_at_1 |
| value: 45.391999999999996 |
| - type: ndcg_at_10 |
| value: 57.055 |
| - type: ndcg_at_100 |
| value: 60.751999999999995 |
| - type: ndcg_at_1000 |
| value: 61.864 |
| - type: ndcg_at_3 |
| value: 51.662 |
| - type: ndcg_at_5 |
| value: 54.613 |
| - type: precision_at_1 |
| value: 45.391999999999996 |
| - type: precision_at_10 |
| value: 9.103 |
| - type: precision_at_100 |
| value: 1.1780000000000002 |
| - type: precision_at_1000 |
| value: 0.132 |
| - type: precision_at_3 |
| value: 22.717000000000002 |
| - type: precision_at_5 |
| value: 15.812000000000001 |
| - type: recall_at_1 |
| value: 39.747 |
| - type: recall_at_10 |
| value: 70.10499999999999 |
| - type: recall_at_100 |
| value: 86.23100000000001 |
| - type: recall_at_1000 |
| value: 94.025 |
| - type: recall_at_3 |
| value: 55.899 |
| - type: recall_at_5 |
| value: 63.05500000000001 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/cqadupstack-gis |
| name: MTEB CQADupstackGisRetrieval |
| config: default |
| split: test |
| revision: 5003b3064772da1887988e05400cf3806fe491f2 |
| metrics: |
| - type: map_at_1 |
| value: 27.168999999999997 |
| - type: map_at_10 |
| value: 34.975 |
| - type: map_at_100 |
| value: 35.94 |
| - type: map_at_1000 |
| value: 36.021 |
| - type: map_at_3 |
| value: 32.35 |
| - type: map_at_5 |
| value: 33.831 |
| - type: mrr_at_1 |
| value: 28.701 |
| - type: mrr_at_10 |
| value: 36.698 |
| - type: mrr_at_100 |
| value: 37.546 |
| - type: mrr_at_1000 |
| value: 37.613 |
| - type: mrr_at_3 |
| value: 34.256 |
| - type: mrr_at_5 |
| value: 35.685 |
| - type: ndcg_at_1 |
| value: 28.701 |
| - type: ndcg_at_10 |
| value: 39.639 |
| - type: ndcg_at_100 |
| value: 44.389 |
| - type: ndcg_at_1000 |
| value: 46.46 |
| - type: ndcg_at_3 |
| value: 34.52 |
| - type: ndcg_at_5 |
| value: 37.076 |
| - type: precision_at_1 |
| value: 28.701 |
| - type: precision_at_10 |
| value: 5.955 |
| - type: precision_at_100 |
| value: 0.8880000000000001 |
| - type: precision_at_1000 |
| value: 0.109 |
| - type: precision_at_3 |
| value: 14.274999999999999 |
| - type: precision_at_5 |
| value: 10.011000000000001 |
| - type: recall_at_1 |
| value: 27.168999999999997 |
| - type: recall_at_10 |
| value: 52.347 |
| - type: recall_at_100 |
| value: 74.1 |
| - type: recall_at_1000 |
| value: 89.739 |
| - type: recall_at_3 |
| value: 38.567 |
| - type: recall_at_5 |
| value: 44.767 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/cqadupstack-mathematica |
| name: MTEB CQADupstackMathematicaRetrieval |
| config: default |
| split: test |
| revision: 90fceea13679c63fe563ded68f3b6f06e50061de |
| metrics: |
| - type: map_at_1 |
| value: 15.872 |
| - type: map_at_10 |
| value: 23.153000000000002 |
| - type: map_at_100 |
| value: 24.311 |
| - type: map_at_1000 |
| value: 24.432000000000002 |
| - type: map_at_3 |
| value: 20.707 |
| - type: map_at_5 |
| value: 21.921 |
| - type: mrr_at_1 |
| value: 19.776 |
| - type: mrr_at_10 |
| value: 27.755999999999997 |
| - type: mrr_at_100 |
| value: 28.709 |
| - type: mrr_at_1000 |
| value: 28.778 |
| - type: mrr_at_3 |
| value: 25.186999999999998 |
| - type: mrr_at_5 |
| value: 26.43 |
| - type: ndcg_at_1 |
| value: 19.776 |
| - type: ndcg_at_10 |
| value: 28.288999999999998 |
| - type: ndcg_at_100 |
| value: 34.011 |
| - type: ndcg_at_1000 |
| value: 36.916 |
| - type: ndcg_at_3 |
| value: 23.551 |
| - type: ndcg_at_5 |
| value: 25.429000000000002 |
| - type: precision_at_1 |
| value: 19.776 |
| - type: precision_at_10 |
| value: 5.311 |
| - type: precision_at_100 |
| value: 0.9440000000000001 |
| - type: precision_at_1000 |
| value: 0.132 |
| - type: precision_at_3 |
| value: 11.360000000000001 |
| - type: precision_at_5 |
| value: 8.209 |
| - type: recall_at_1 |
| value: 15.872 |
| - type: recall_at_10 |
| value: 39.726 |
| - type: recall_at_100 |
| value: 65.035 |
| - type: recall_at_1000 |
| value: 85.846 |
| - type: recall_at_3 |
| value: 26.432 |
| - type: recall_at_5 |
| value: 31.22 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/cqadupstack-physics |
| name: MTEB CQADupstackPhysicsRetrieval |
| config: default |
| split: test |
| revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 |
| metrics: |
| - type: map_at_1 |
| value: 28.126 |
| - type: map_at_10 |
| value: 37.537 |
| - type: map_at_100 |
| value: 38.807 |
| - type: map_at_1000 |
| value: 38.923 |
| - type: map_at_3 |
| value: 34.65 |
| - type: map_at_5 |
| value: 36.248000000000005 |
| - type: mrr_at_1 |
| value: 34.649 |
| - type: mrr_at_10 |
| value: 42.893 |
| - type: mrr_at_100 |
| value: 43.721 |
| - type: mrr_at_1000 |
| value: 43.775999999999996 |
| - type: mrr_at_3 |
| value: 40.488 |
| - type: mrr_at_5 |
| value: 41.729 |
| - type: ndcg_at_1 |
| value: 34.649 |
| - type: ndcg_at_10 |
| value: 43.072 |
| - type: ndcg_at_100 |
| value: 48.464 |
| - type: ndcg_at_1000 |
| value: 50.724000000000004 |
| - type: ndcg_at_3 |
| value: 38.506 |
| - type: ndcg_at_5 |
| value: 40.522000000000006 |
| - type: precision_at_1 |
| value: 34.649 |
| - type: precision_at_10 |
| value: 7.68 |
| - type: precision_at_100 |
| value: 1.214 |
| - type: precision_at_1000 |
| value: 0.16 |
| - type: precision_at_3 |
| value: 18.029999999999998 |
| - type: precision_at_5 |
| value: 12.666 |
| - type: recall_at_1 |
| value: 28.126 |
| - type: recall_at_10 |
| value: 54.396 |
| - type: recall_at_100 |
| value: 76.988 |
| - type: recall_at_1000 |
| value: 91.85799999999999 |
| - type: recall_at_3 |
| value: 41.169 |
| - type: recall_at_5 |
| value: 46.658 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/cqadupstack-programmers |
| name: MTEB CQADupstackProgrammersRetrieval |
| config: default |
| split: test |
| revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 |
| metrics: |
| - type: map_at_1 |
| value: 26.68 |
| - type: map_at_10 |
| value: 35.702 |
| - type: map_at_100 |
| value: 36.864999999999995 |
| - type: map_at_1000 |
| value: 36.977 |
| - type: map_at_3 |
| value: 32.828 |
| - type: map_at_5 |
| value: 34.481 |
| - type: mrr_at_1 |
| value: 32.991 |
| - type: mrr_at_10 |
| value: 40.993 |
| - type: mrr_at_100 |
| value: 41.827 |
| - type: mrr_at_1000 |
| value: 41.887 |
| - type: mrr_at_3 |
| value: 38.623000000000005 |
| - type: mrr_at_5 |
| value: 40.021 |
| - type: ndcg_at_1 |
| value: 32.991 |
| - type: ndcg_at_10 |
| value: 41.036 |
| - type: ndcg_at_100 |
| value: 46.294000000000004 |
| - type: ndcg_at_1000 |
| value: 48.644 |
| - type: ndcg_at_3 |
| value: 36.419000000000004 |
| - type: ndcg_at_5 |
| value: 38.618 |
| - type: precision_at_1 |
| value: 32.991 |
| - type: precision_at_10 |
| value: 7.385999999999999 |
| - type: precision_at_100 |
| value: 1.176 |
| - type: precision_at_1000 |
| value: 0.151 |
| - type: precision_at_3 |
| value: 17.122999999999998 |
| - type: precision_at_5 |
| value: 12.215 |
| - type: recall_at_1 |
| value: 26.68 |
| - type: recall_at_10 |
| value: 51.644 |
| - type: recall_at_100 |
| value: 74.55000000000001 |
| - type: recall_at_1000 |
| value: 90.825 |
| - type: recall_at_3 |
| value: 38.579 |
| - type: recall_at_5 |
| value: 44.512 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/cqadupstack |
| name: MTEB CQADupstackRetrieval |
| config: default |
| split: test |
| revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 |
| metrics: |
| - type: map_at_1 |
| value: 26.30825 |
| - type: map_at_10 |
| value: 34.97866666666666 |
| - type: map_at_100 |
| value: 36.109249999999996 |
| - type: map_at_1000 |
| value: 36.22508333333333 |
| - type: map_at_3 |
| value: 32.239083333333326 |
| - type: map_at_5 |
| value: 33.75933333333334 |
| - type: mrr_at_1 |
| value: 31.05308333333333 |
| - type: mrr_at_10 |
| value: 39.099833333333336 |
| - type: mrr_at_100 |
| value: 39.92008333333334 |
| - type: mrr_at_1000 |
| value: 39.980000000000004 |
| - type: mrr_at_3 |
| value: 36.75958333333333 |
| - type: mrr_at_5 |
| value: 38.086416666666665 |
| - type: ndcg_at_1 |
| value: 31.05308333333333 |
| - type: ndcg_at_10 |
| value: 40.11558333333334 |
| - type: ndcg_at_100 |
| value: 45.05966666666667 |
| - type: ndcg_at_1000 |
| value: 47.36516666666667 |
| - type: ndcg_at_3 |
| value: 35.490833333333335 |
| - type: ndcg_at_5 |
| value: 37.64541666666666 |
| - type: precision_at_1 |
| value: 31.05308333333333 |
| - type: precision_at_10 |
| value: 6.968416666666666 |
| - type: precision_at_100 |
| value: 1.1156666666666666 |
| - type: precision_at_1000 |
| value: 0.14950000000000002 |
| - type: precision_at_3 |
| value: 16.123 |
| - type: precision_at_5 |
| value: 11.451166666666666 |
| - type: recall_at_1 |
| value: 26.30825 |
| - type: recall_at_10 |
| value: 51.19283333333333 |
| - type: recall_at_100 |
| value: 73.0285 |
| - type: recall_at_1000 |
| value: 89.11133333333333 |
| - type: recall_at_3 |
| value: 38.26208333333333 |
| - type: recall_at_5 |
| value: 43.855916666666666 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/cqadupstack-stats |
| name: MTEB CQADupstackStatsRetrieval |
| config: default |
| split: test |
| revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a |
| metrics: |
| - type: map_at_1 |
| value: 23.363999999999997 |
| - type: map_at_10 |
| value: 30.606 |
| - type: map_at_100 |
| value: 31.491999999999997 |
| - type: map_at_1000 |
| value: 31.578 |
| - type: map_at_3 |
| value: 28.610000000000003 |
| - type: map_at_5 |
| value: 29.602 |
| - type: mrr_at_1 |
| value: 26.38 |
| - type: mrr_at_10 |
| value: 33.472 |
| - type: mrr_at_100 |
| value: 34.299 |
| - type: mrr_at_1000 |
| value: 34.361999999999995 |
| - type: mrr_at_3 |
| value: 31.696999999999996 |
| - type: mrr_at_5 |
| value: 32.503 |
| - type: ndcg_at_1 |
| value: 26.38 |
| - type: ndcg_at_10 |
| value: 34.772999999999996 |
| - type: ndcg_at_100 |
| value: 39.334 |
| - type: ndcg_at_1000 |
| value: 41.676 |
| - type: ndcg_at_3 |
| value: 31.097 |
| - type: ndcg_at_5 |
| value: 32.561 |
| - type: precision_at_1 |
| value: 26.38 |
| - type: precision_at_10 |
| value: 5.475 |
| - type: precision_at_100 |
| value: 0.84 |
| - type: precision_at_1000 |
| value: 0.11100000000000002 |
| - type: precision_at_3 |
| value: 13.395000000000001 |
| - type: precision_at_5 |
| value: 9.11 |
| - type: recall_at_1 |
| value: 23.363999999999997 |
| - type: recall_at_10 |
| value: 44.656 |
| - type: recall_at_100 |
| value: 65.77199999999999 |
| - type: recall_at_1000 |
| value: 83.462 |
| - type: recall_at_3 |
| value: 34.213 |
| - type: recall_at_5 |
| value: 38.091 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/cqadupstack-tex |
| name: MTEB CQADupstackTexRetrieval |
| config: default |
| split: test |
| revision: 46989137a86843e03a6195de44b09deda022eec7 |
| metrics: |
| - type: map_at_1 |
| value: 17.971999999999998 |
| - type: map_at_10 |
| value: 24.913 |
| - type: map_at_100 |
| value: 25.916 |
| - type: map_at_1000 |
| value: 26.049 |
| - type: map_at_3 |
| value: 22.569 |
| - type: map_at_5 |
| value: 23.858999999999998 |
| - type: mrr_at_1 |
| value: 21.748 |
| - type: mrr_at_10 |
| value: 28.711 |
| - type: mrr_at_100 |
| value: 29.535 |
| - type: mrr_at_1000 |
| value: 29.621 |
| - type: mrr_at_3 |
| value: 26.484999999999996 |
| - type: mrr_at_5 |
| value: 27.701999999999998 |
| - type: ndcg_at_1 |
| value: 21.748 |
| - type: ndcg_at_10 |
| value: 29.412 |
| - type: ndcg_at_100 |
| value: 34.204 |
| - type: ndcg_at_1000 |
| value: 37.358000000000004 |
| - type: ndcg_at_3 |
| value: 25.202 |
| - type: ndcg_at_5 |
| value: 27.128000000000004 |
| - type: precision_at_1 |
| value: 21.748 |
| - type: precision_at_10 |
| value: 5.279 |
| - type: precision_at_100 |
| value: 0.902 |
| - type: precision_at_1000 |
| value: 0.135 |
| - type: precision_at_3 |
| value: 11.551 |
| - type: precision_at_5 |
| value: 8.437999999999999 |
| - type: recall_at_1 |
| value: 17.971999999999998 |
| - type: recall_at_10 |
| value: 39.186 |
| - type: recall_at_100 |
| value: 60.785999999999994 |
| - type: recall_at_1000 |
| value: 83.372 |
| - type: recall_at_3 |
| value: 27.584999999999997 |
| - type: recall_at_5 |
| value: 32.448 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/cqadupstack-unix |
| name: MTEB CQADupstackUnixRetrieval |
| config: default |
| split: test |
| revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 |
| metrics: |
| - type: map_at_1 |
| value: 26.684 |
| - type: map_at_10 |
| value: 35.188 |
| - type: map_at_100 |
| value: 36.379 |
| - type: map_at_1000 |
| value: 36.481 |
| - type: map_at_3 |
| value: 32.401 |
| - type: map_at_5 |
| value: 34.132 |
| - type: mrr_at_1 |
| value: 31.063000000000002 |
| - type: mrr_at_10 |
| value: 39.104 |
| - type: mrr_at_100 |
| value: 40.062999999999995 |
| - type: mrr_at_1000 |
| value: 40.119 |
| - type: mrr_at_3 |
| value: 36.692 |
| - type: mrr_at_5 |
| value: 38.161 |
| - type: ndcg_at_1 |
| value: 31.063000000000002 |
| - type: ndcg_at_10 |
| value: 40.096 |
| - type: ndcg_at_100 |
| value: 45.616 |
| - type: ndcg_at_1000 |
| value: 47.869 |
| - type: ndcg_at_3 |
| value: 35.256 |
| - type: ndcg_at_5 |
| value: 37.826 |
| - type: precision_at_1 |
| value: 31.063000000000002 |
| - type: precision_at_10 |
| value: 6.622999999999999 |
| - type: precision_at_100 |
| value: 1.046 |
| - type: precision_at_1000 |
| value: 0.135 |
| - type: precision_at_3 |
| value: 15.641 |
| - type: precision_at_5 |
| value: 11.231 |
| - type: recall_at_1 |
| value: 26.684 |
| - type: recall_at_10 |
| value: 51.092999999999996 |
| - type: recall_at_100 |
| value: 75.099 |
| - type: recall_at_1000 |
| value: 90.644 |
| - type: recall_at_3 |
| value: 38.063 |
| - type: recall_at_5 |
| value: 44.518 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/cqadupstack-webmasters |
| name: MTEB CQADupstackWebmastersRetrieval |
| config: default |
| split: test |
| revision: 160c094312a0e1facb97e55eeddb698c0abe3571 |
| metrics: |
| - type: map_at_1 |
| value: 26.249 |
| - type: map_at_10 |
| value: 34.694 |
| - type: map_at_100 |
| value: 36.208 |
| - type: map_at_1000 |
| value: 36.443 |
| - type: map_at_3 |
| value: 31.868000000000002 |
| - type: map_at_5 |
| value: 33.018 |
| - type: mrr_at_1 |
| value: 31.818 |
| - type: mrr_at_10 |
| value: 39.416000000000004 |
| - type: mrr_at_100 |
| value: 40.327 |
| - type: mrr_at_1000 |
| value: 40.388000000000005 |
| - type: mrr_at_3 |
| value: 37.120999999999995 |
| - type: mrr_at_5 |
| value: 38.07 |
| - type: ndcg_at_1 |
| value: 31.818 |
| - type: ndcg_at_10 |
| value: 40.405 |
| - type: ndcg_at_100 |
| value: 45.816 |
| - type: ndcg_at_1000 |
| value: 48.403 |
| - type: ndcg_at_3 |
| value: 35.823 |
| - type: ndcg_at_5 |
| value: 37.191 |
| - type: precision_at_1 |
| value: 31.818 |
| - type: precision_at_10 |
| value: 7.806 |
| - type: precision_at_100 |
| value: 1.518 |
| - type: precision_at_1000 |
| value: 0.241 |
| - type: precision_at_3 |
| value: 16.535 |
| - type: precision_at_5 |
| value: 11.738999999999999 |
| - type: recall_at_1 |
| value: 26.249 |
| - type: recall_at_10 |
| value: 50.928 |
| - type: recall_at_100 |
| value: 75.271 |
| - type: recall_at_1000 |
| value: 91.535 |
| - type: recall_at_3 |
| value: 37.322 |
| - type: recall_at_5 |
| value: 41.318 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/cqadupstack-wordpress |
| name: MTEB CQADupstackWordpressRetrieval |
| config: default |
| split: test |
| revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 |
| metrics: |
| - type: map_at_1 |
| value: 21.884999999999998 |
| - type: map_at_10 |
| value: 29.158 |
| - type: map_at_100 |
| value: 30.208000000000002 |
| - type: map_at_1000 |
| value: 30.304 |
| - type: map_at_3 |
| value: 26.82 |
| - type: map_at_5 |
| value: 28.051 |
| - type: mrr_at_1 |
| value: 23.66 |
| - type: mrr_at_10 |
| value: 31.277 |
| - type: mrr_at_100 |
| value: 32.237 |
| - type: mrr_at_1000 |
| value: 32.308 |
| - type: mrr_at_3 |
| value: 29.205 |
| - type: mrr_at_5 |
| value: 30.314000000000004 |
| - type: ndcg_at_1 |
| value: 23.66 |
| - type: ndcg_at_10 |
| value: 33.64 |
| - type: ndcg_at_100 |
| value: 39.028 |
| - type: ndcg_at_1000 |
| value: 41.423 |
| - type: ndcg_at_3 |
| value: 29.189 |
| - type: ndcg_at_5 |
| value: 31.191999999999997 |
| - type: precision_at_1 |
| value: 23.66 |
| - type: precision_at_10 |
| value: 5.287 |
| - type: precision_at_100 |
| value: 0.86 |
| - type: precision_at_1000 |
| value: 0.11499999999999999 |
| - type: precision_at_3 |
| value: 12.631 |
| - type: precision_at_5 |
| value: 8.762 |
| - type: recall_at_1 |
| value: 21.884999999999998 |
| - type: recall_at_10 |
| value: 45.357 |
| - type: recall_at_100 |
| value: 70.338 |
| - type: recall_at_1000 |
| value: 88.356 |
| - type: recall_at_3 |
| value: 33.312000000000005 |
| - type: recall_at_5 |
| value: 38.222 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/climate-fever |
| name: MTEB ClimateFEVER |
| config: default |
| split: test |
| revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 |
| metrics: |
| - type: map_at_1 |
| value: 13.058 |
| - type: map_at_10 |
| value: 21.549 |
| - type: map_at_100 |
| value: 23.287 |
| - type: map_at_1000 |
| value: 23.444000000000003 |
| - type: map_at_3 |
| value: 18.18 |
| - type: map_at_5 |
| value: 19.886 |
| - type: mrr_at_1 |
| value: 28.73 |
| - type: mrr_at_10 |
| value: 40.014 |
| - type: mrr_at_100 |
| value: 40.827000000000005 |
| - type: mrr_at_1000 |
| value: 40.866 |
| - type: mrr_at_3 |
| value: 36.602000000000004 |
| - type: mrr_at_5 |
| value: 38.702 |
| - type: ndcg_at_1 |
| value: 28.73 |
| - type: ndcg_at_10 |
| value: 29.881 |
| - type: ndcg_at_100 |
| value: 36.662 |
| - type: ndcg_at_1000 |
| value: 39.641999999999996 |
| - type: ndcg_at_3 |
| value: 24.661 |
| - type: ndcg_at_5 |
| value: 26.548 |
| - type: precision_at_1 |
| value: 28.73 |
| - type: precision_at_10 |
| value: 9.094 |
| - type: precision_at_100 |
| value: 1.6480000000000001 |
| - type: precision_at_1000 |
| value: 0.22100000000000003 |
| - type: precision_at_3 |
| value: 17.98 |
| - type: precision_at_5 |
| value: 13.811000000000002 |
| - type: recall_at_1 |
| value: 13.058 |
| - type: recall_at_10 |
| value: 35.458 |
| - type: recall_at_100 |
| value: 58.719 |
| - type: recall_at_1000 |
| value: 75.495 |
| - type: recall_at_3 |
| value: 22.607 |
| - type: recall_at_5 |
| value: 28.067999999999998 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/dbpedia |
| name: MTEB DBPedia |
| config: default |
| split: test |
| revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 |
| metrics: |
| - type: map_at_1 |
| value: 8.811 |
| - type: map_at_10 |
| value: 19.134999999999998 |
| - type: map_at_100 |
| value: 26.905 |
| - type: map_at_1000 |
| value: 28.503 |
| - type: map_at_3 |
| value: 13.863 |
| - type: map_at_5 |
| value: 16.062 |
| - type: mrr_at_1 |
| value: 67 |
| - type: mrr_at_10 |
| value: 74.607 |
| - type: mrr_at_100 |
| value: 74.941 |
| - type: mrr_at_1000 |
| value: 74.954 |
| - type: mrr_at_3 |
| value: 73.042 |
| - type: mrr_at_5 |
| value: 73.992 |
| - type: ndcg_at_1 |
| value: 52.87500000000001 |
| - type: ndcg_at_10 |
| value: 40.199 |
| - type: ndcg_at_100 |
| value: 44.901 |
| - type: ndcg_at_1000 |
| value: 52.239999999999995 |
| - type: ndcg_at_3 |
| value: 44.983000000000004 |
| - type: ndcg_at_5 |
| value: 42.137 |
| - type: precision_at_1 |
| value: 67 |
| - type: precision_at_10 |
| value: 31.8 |
| - type: precision_at_100 |
| value: 10.315000000000001 |
| - type: precision_at_1000 |
| value: 2.0420000000000003 |
| - type: precision_at_3 |
| value: 48.667 |
| - type: precision_at_5 |
| value: 40.9 |
| - type: recall_at_1 |
| value: 8.811 |
| - type: recall_at_10 |
| value: 24.503 |
| - type: recall_at_100 |
| value: 51.288999999999994 |
| - type: recall_at_1000 |
| value: 74.827 |
| - type: recall_at_3 |
| value: 15.254999999999999 |
| - type: recall_at_5 |
| value: 18.698999999999998 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/emotion |
| name: MTEB EmotionClassification |
| config: default |
| split: test |
| revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 |
| metrics: |
| - type: accuracy |
| value: 41.839999999999996 |
| - type: f1 |
| value: 37.78718146306379 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/fever |
| name: MTEB FEVER |
| config: default |
| split: test |
| revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 |
| metrics: |
| - type: map_at_1 |
| value: 68.47999999999999 |
| - type: map_at_10 |
| value: 78.782 |
| - type: map_at_100 |
| value: 79.021 |
| - type: map_at_1000 |
| value: 79.035 |
| - type: map_at_3 |
| value: 77.389 |
| - type: map_at_5 |
| value: 78.347 |
| - type: mrr_at_1 |
| value: 73.837 |
| - type: mrr_at_10 |
| value: 83.41499999999999 |
| - type: mrr_at_100 |
| value: 83.53399999999999 |
| - type: mrr_at_1000 |
| value: 83.535 |
| - type: mrr_at_3 |
| value: 82.32300000000001 |
| - type: mrr_at_5 |
| value: 83.13000000000001 |
| - type: ndcg_at_1 |
| value: 73.837 |
| - type: ndcg_at_10 |
| value: 83.404 |
| - type: ndcg_at_100 |
| value: 84.287 |
| - type: ndcg_at_1000 |
| value: 84.52199999999999 |
| - type: ndcg_at_3 |
| value: 81.072 |
| - type: ndcg_at_5 |
| value: 82.537 |
| - type: precision_at_1 |
| value: 73.837 |
| - type: precision_at_10 |
| value: 10.254000000000001 |
| - type: precision_at_100 |
| value: 1.088 |
| - type: precision_at_1000 |
| value: 0.11299999999999999 |
| - type: precision_at_3 |
| value: 31.538 |
| - type: precision_at_5 |
| value: 19.811 |
| - type: recall_at_1 |
| value: 68.47999999999999 |
| - type: recall_at_10 |
| value: 92.98100000000001 |
| - type: recall_at_100 |
| value: 96.50800000000001 |
| - type: recall_at_1000 |
| value: 97.925 |
| - type: recall_at_3 |
| value: 86.764 |
| - type: recall_at_5 |
| value: 90.39 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/fiqa |
| name: MTEB FiQA2018 |
| config: default |
| split: test |
| revision: 27a168819829fe9bcd655c2df245fb19452e8e06 |
| metrics: |
| - type: map_at_1 |
| value: 16.786 |
| - type: map_at_10 |
| value: 26.97 |
| - type: map_at_100 |
| value: 28.488000000000003 |
| - type: map_at_1000 |
| value: 28.665000000000003 |
| - type: map_at_3 |
| value: 23.3 |
| - type: map_at_5 |
| value: 25.249 |
| - type: mrr_at_1 |
| value: 33.025 |
| - type: mrr_at_10 |
| value: 41.86 |
| - type: mrr_at_100 |
| value: 42.673 |
| - type: mrr_at_1000 |
| value: 42.714 |
| - type: mrr_at_3 |
| value: 39.403 |
| - type: mrr_at_5 |
| value: 40.723 |
| - type: ndcg_at_1 |
| value: 33.025 |
| - type: ndcg_at_10 |
| value: 34.522999999999996 |
| - type: ndcg_at_100 |
| value: 40.831 |
| - type: ndcg_at_1000 |
| value: 44.01 |
| - type: ndcg_at_3 |
| value: 30.698999999999998 |
| - type: ndcg_at_5 |
| value: 31.832 |
| - type: precision_at_1 |
| value: 33.025 |
| - type: precision_at_10 |
| value: 9.583 |
| - type: precision_at_100 |
| value: 1.619 |
| - type: precision_at_1000 |
| value: 0.22100000000000003 |
| - type: precision_at_3 |
| value: 20.216 |
| - type: precision_at_5 |
| value: 15.031 |
| - type: recall_at_1 |
| value: 16.786 |
| - type: recall_at_10 |
| value: 41.969 |
| - type: recall_at_100 |
| value: 66.353 |
| - type: recall_at_1000 |
| value: 85.299 |
| - type: recall_at_3 |
| value: 28.111000000000004 |
| - type: recall_at_5 |
| value: 33.645 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/hotpotqa |
| name: MTEB HotpotQA |
| config: default |
| split: test |
| revision: ab518f4d6fcca38d87c25209f94beba119d02014 |
| metrics: |
| - type: map_at_1 |
| value: 37.346000000000004 |
| - type: map_at_10 |
| value: 56.184999999999995 |
| - type: map_at_100 |
| value: 57.062000000000005 |
| - type: map_at_1000 |
| value: 57.126999999999995 |
| - type: map_at_3 |
| value: 52.815 |
| - type: map_at_5 |
| value: 54.893 |
| - type: mrr_at_1 |
| value: 74.693 |
| - type: mrr_at_10 |
| value: 81.128 |
| - type: mrr_at_100 |
| value: 81.356 |
| - type: mrr_at_1000 |
| value: 81.363 |
| - type: mrr_at_3 |
| value: 80.05600000000001 |
| - type: mrr_at_5 |
| value: 80.74 |
| - type: ndcg_at_1 |
| value: 74.693 |
| - type: ndcg_at_10 |
| value: 65.249 |
| - type: ndcg_at_100 |
| value: 68.357 |
| - type: ndcg_at_1000 |
| value: 69.64200000000001 |
| - type: ndcg_at_3 |
| value: 60.377 |
| - type: ndcg_at_5 |
| value: 63.044 |
| - type: precision_at_1 |
| value: 74.693 |
| - type: precision_at_10 |
| value: 13.630999999999998 |
| - type: precision_at_100 |
| value: 1.606 |
| - type: precision_at_1000 |
| value: 0.178 |
| - type: precision_at_3 |
| value: 38.222 |
| - type: precision_at_5 |
| value: 25.040000000000003 |
| - type: recall_at_1 |
| value: 37.346000000000004 |
| - type: recall_at_10 |
| value: 68.157 |
| - type: recall_at_100 |
| value: 80.297 |
| - type: recall_at_1000 |
| value: 88.832 |
| - type: recall_at_3 |
| value: 57.333 |
| - type: recall_at_5 |
| value: 62.6 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/imdb |
| name: MTEB ImdbClassification |
| config: default |
| split: test |
| revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 |
| metrics: |
| - type: accuracy |
| value: 62.80240000000001 |
| - type: ap |
| value: 58.22949464075975 |
| - type: f1 |
| value: 62.55694937343487 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/msmarco |
| name: MTEB MSMARCO |
| config: default |
| split: dev |
| revision: c5a29a104738b98a9e76336939199e264163d4a0 |
| metrics: |
| - type: map_at_1 |
| value: 20.918 |
| - type: map_at_10 |
| value: 32.732 |
| - type: map_at_100 |
| value: 33.922000000000004 |
| - type: map_at_1000 |
| value: 33.976 |
| - type: map_at_3 |
| value: 29.051 |
| - type: map_at_5 |
| value: 31.101 |
| - type: mrr_at_1 |
| value: 21.418 |
| - type: mrr_at_10 |
| value: 33.284000000000006 |
| - type: mrr_at_100 |
| value: 34.426 |
| - type: mrr_at_1000 |
| value: 34.473 |
| - type: mrr_at_3 |
| value: 29.644 |
| - type: mrr_at_5 |
| value: 31.691000000000003 |
| - type: ndcg_at_1 |
| value: 21.418 |
| - type: ndcg_at_10 |
| value: 39.427 |
| - type: ndcg_at_100 |
| value: 45.190999999999995 |
| - type: ndcg_at_1000 |
| value: 46.544000000000004 |
| - type: ndcg_at_3 |
| value: 31.885 |
| - type: ndcg_at_5 |
| value: 35.555 |
| - type: precision_at_1 |
| value: 21.418 |
| - type: precision_at_10 |
| value: 6.254999999999999 |
| - type: precision_at_100 |
| value: 0.915 |
| - type: precision_at_1000 |
| value: 0.10300000000000001 |
| - type: precision_at_3 |
| value: 13.591000000000001 |
| - type: precision_at_5 |
| value: 10.011000000000001 |
| - type: recall_at_1 |
| value: 20.918 |
| - type: recall_at_10 |
| value: 60.074000000000005 |
| - type: recall_at_100 |
| value: 86.726 |
| - type: recall_at_1000 |
| value: 97.116 |
| - type: recall_at_3 |
| value: 39.506 |
| - type: recall_at_5 |
| value: 48.319 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/mtop_domain |
| name: MTEB MTOPDomainClassification (en) |
| config: en |
| split: test |
| revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
| metrics: |
| - type: accuracy |
| value: 90.79799361605106 |
| - type: f1 |
| value: 90.0757957511057 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/mtop_intent |
| name: MTEB MTOPIntentClassification (en) |
| config: en |
| split: test |
| revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
| metrics: |
| - type: accuracy |
| value: 58.00501595987233 |
| - type: f1 |
| value: 39.85731569133947 |
| - task: |
| type: Classification |
| dataset: |
| type: masakhane/masakhanews |
| name: MTEB MasakhaNEWSClassification (eng) |
| config: eng |
| split: test |
| revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60 |
| metrics: |
| - type: accuracy |
| value: 77.10970464135022 |
| - type: f1 |
| value: 76.12037616356896 |
| - task: |
| type: Clustering |
| dataset: |
| type: masakhane/masakhanews |
| name: MTEB MasakhaNEWSClusteringP2P (eng) |
| config: eng |
| split: test |
| revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60 |
| metrics: |
| - type: v_measure |
| value: 69.81323966287493 |
| - task: |
| type: Clustering |
| dataset: |
| type: masakhane/masakhanews |
| name: MTEB MasakhaNEWSClusteringS2S (eng) |
| config: eng |
| split: test |
| revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60 |
| metrics: |
| - type: v_measure |
| value: 33.112774215788455 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (en) |
| config: en |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
| - type: accuracy |
| value: 63.51042367182246 |
| - type: f1 |
| value: 60.99310361578824 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_scenario |
| name: MTEB MassiveScenarioClassification (en) |
| config: en |
| split: test |
| revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| metrics: |
| - type: accuracy |
| value: 71.0053799596503 |
| - type: f1 |
| value: 69.7794673003686 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/medrxiv-clustering-p2p |
| name: MTEB MedrxivClusteringP2P |
| config: default |
| split: test |
| revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 |
| metrics: |
| - type: v_measure |
| value: 30.56899174856954 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/medrxiv-clustering-s2s |
| name: MTEB MedrxivClusteringS2S |
| config: default |
| split: test |
| revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 |
| metrics: |
| - type: v_measure |
| value: 26.21848014733929 |
| - task: |
| type: Reranking |
| dataset: |
| type: mteb/mind_small |
| name: MTEB MindSmallReranking |
| config: default |
| split: test |
| revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 |
| metrics: |
| - type: map |
| value: 30.256308756916646 |
| - type: mrr |
| value: 31.123872086825656 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/nfcorpus |
| name: MTEB NFCorpus |
| config: default |
| split: test |
| revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 |
| metrics: |
| - type: map_at_1 |
| value: 5.07 |
| - type: map_at_10 |
| value: 11.286999999999999 |
| - type: map_at_100 |
| value: 13.630999999999998 |
| - type: map_at_1000 |
| value: 14.844 |
| - type: map_at_3 |
| value: 8.395 |
| - type: map_at_5 |
| value: 9.721 |
| - type: mrr_at_1 |
| value: 41.486000000000004 |
| - type: mrr_at_10 |
| value: 51.041000000000004 |
| - type: mrr_at_100 |
| value: 51.661 |
| - type: mrr_at_1000 |
| value: 51.7 |
| - type: mrr_at_3 |
| value: 49.226 |
| - type: mrr_at_5 |
| value: 50.526 |
| - type: ndcg_at_1 |
| value: 39.783 |
| - type: ndcg_at_10 |
| value: 30.885 |
| - type: ndcg_at_100 |
| value: 27.459 |
| - type: ndcg_at_1000 |
| value: 35.988 |
| - type: ndcg_at_3 |
| value: 36.705 |
| - type: ndcg_at_5 |
| value: 34.156 |
| - type: precision_at_1 |
| value: 41.486000000000004 |
| - type: precision_at_10 |
| value: 22.415 |
| - type: precision_at_100 |
| value: 6.819999999999999 |
| - type: precision_at_1000 |
| value: 1.8980000000000001 |
| - type: precision_at_3 |
| value: 34.572 |
| - type: precision_at_5 |
| value: 29.287999999999997 |
| - type: recall_at_1 |
| value: 5.07 |
| - type: recall_at_10 |
| value: 14.576 |
| - type: recall_at_100 |
| value: 27.112000000000002 |
| - type: recall_at_1000 |
| value: 57.995 |
| - type: recall_at_3 |
| value: 9.242 |
| - type: recall_at_5 |
| value: 11.668000000000001 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/nq |
| name: MTEB NQ |
| config: default |
| split: test |
| revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 |
| metrics: |
| - type: map_at_1 |
| value: 32.263999999999996 |
| - type: map_at_10 |
| value: 47.219 |
| - type: map_at_100 |
| value: 48.209999999999994 |
| - type: map_at_1000 |
| value: 48.24 |
| - type: map_at_3 |
| value: 42.905 |
| - type: map_at_5 |
| value: 45.501000000000005 |
| - type: mrr_at_1 |
| value: 36.153 |
| - type: mrr_at_10 |
| value: 49.636 |
| - type: mrr_at_100 |
| value: 50.357 |
| - type: mrr_at_1000 |
| value: 50.378 |
| - type: mrr_at_3 |
| value: 46.094 |
| - type: mrr_at_5 |
| value: 48.233 |
| - type: ndcg_at_1 |
| value: 36.124 |
| - type: ndcg_at_10 |
| value: 54.764 |
| - type: ndcg_at_100 |
| value: 58.867999999999995 |
| - type: ndcg_at_1000 |
| value: 59.548 |
| - type: ndcg_at_3 |
| value: 46.717999999999996 |
| - type: ndcg_at_5 |
| value: 50.981 |
| - type: precision_at_1 |
| value: 36.124 |
| - type: precision_at_10 |
| value: 8.931000000000001 |
| - type: precision_at_100 |
| value: 1.126 |
| - type: precision_at_1000 |
| value: 0.11900000000000001 |
| - type: precision_at_3 |
| value: 21.051000000000002 |
| - type: precision_at_5 |
| value: 15.104000000000001 |
| - type: recall_at_1 |
| value: 32.263999999999996 |
| - type: recall_at_10 |
| value: 75.39099999999999 |
| - type: recall_at_100 |
| value: 93.038 |
| - type: recall_at_1000 |
| value: 98.006 |
| - type: recall_at_3 |
| value: 54.562999999999995 |
| - type: recall_at_5 |
| value: 64.352 |
| - task: |
| type: Classification |
| dataset: |
| type: ag_news |
| name: MTEB NewsClassification |
| config: default |
| split: test |
| revision: eb185aade064a813bc0b7f42de02595523103ca4 |
| metrics: |
| - type: accuracy |
| value: 77.75 |
| - type: f1 |
| value: 77.504243291547 |
| - 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: 61.75000000000001 |
| - type: cos_sim_ap |
| value: 57.9482264289061 |
| - type: cos_sim_f1 |
| value: 62.444061962134256 |
| - type: cos_sim_precision |
| value: 45.3953953953954 |
| - type: cos_sim_recall |
| value: 100 |
| - type: dot_accuracy |
| value: 61.75000000000001 |
| - type: dot_ap |
| value: 57.94808038610475 |
| - type: dot_f1 |
| value: 62.444061962134256 |
| - type: dot_precision |
| value: 45.3953953953954 |
| - type: dot_recall |
| value: 100 |
| - type: euclidean_accuracy |
| value: 61.75000000000001 |
| - type: euclidean_ap |
| value: 57.94808038610475 |
| - type: euclidean_f1 |
| value: 62.444061962134256 |
| - type: euclidean_precision |
| value: 45.3953953953954 |
| - type: euclidean_recall |
| value: 100 |
| - type: manhattan_accuracy |
| value: 61.7 |
| - type: manhattan_ap |
| value: 57.996119308184966 |
| - type: manhattan_f1 |
| value: 62.46078773091669 |
| - type: manhattan_precision |
| value: 45.66768603465851 |
| - type: manhattan_recall |
| value: 98.78721058434398 |
| - type: max_accuracy |
| value: 61.75000000000001 |
| - type: max_ap |
| value: 57.996119308184966 |
| - type: max_f1 |
| value: 62.46078773091669 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/quora |
| name: MTEB QuoraRetrieval |
| config: default |
| split: test |
| revision: e4e08e0b7dbe3c8700f0daef558ff32256715259 |
| metrics: |
| - type: map_at_1 |
| value: 69.001 |
| - type: map_at_10 |
| value: 82.573 |
| - type: map_at_100 |
| value: 83.226 |
| - type: map_at_1000 |
| value: 83.246 |
| - type: map_at_3 |
| value: 79.625 |
| - type: map_at_5 |
| value: 81.491 |
| - type: mrr_at_1 |
| value: 79.44 |
| - type: mrr_at_10 |
| value: 85.928 |
| - type: mrr_at_100 |
| value: 86.05199999999999 |
| - type: mrr_at_1000 |
| value: 86.054 |
| - type: mrr_at_3 |
| value: 84.847 |
| - type: mrr_at_5 |
| value: 85.596 |
| - type: ndcg_at_1 |
| value: 79.41 |
| - type: ndcg_at_10 |
| value: 86.568 |
| - type: ndcg_at_100 |
| value: 87.965 |
| - type: ndcg_at_1000 |
| value: 88.134 |
| - type: ndcg_at_3 |
| value: 83.55900000000001 |
| - type: ndcg_at_5 |
| value: 85.244 |
| - type: precision_at_1 |
| value: 79.41 |
| - type: precision_at_10 |
| value: 13.108 |
| - type: precision_at_100 |
| value: 1.509 |
| - type: precision_at_1000 |
| value: 0.156 |
| - type: precision_at_3 |
| value: 36.443 |
| - type: precision_at_5 |
| value: 24.03 |
| - type: recall_at_1 |
| value: 69.001 |
| - type: recall_at_10 |
| value: 94.132 |
| - type: recall_at_100 |
| value: 99.043 |
| - type: recall_at_1000 |
| value: 99.878 |
| - type: recall_at_3 |
| value: 85.492 |
| - type: recall_at_5 |
| value: 90.226 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/reddit-clustering |
| name: MTEB RedditClustering |
| config: default |
| split: test |
| revision: 24640382cdbf8abc73003fb0fa6d111a705499eb |
| metrics: |
| - type: v_measure |
| value: 48.3161352736264 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/reddit-clustering-p2p |
| name: MTEB RedditClusteringP2P |
| config: default |
| split: test |
| revision: 385e3cb46b4cfa89021f56c4380204149d0efe33 |
| metrics: |
| - type: v_measure |
| value: 57.83784484156747 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/scidocs |
| name: MTEB SCIDOCS |
| config: default |
| split: test |
| revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88 |
| metrics: |
| - type: map_at_1 |
| value: 4.403 |
| - type: map_at_10 |
| value: 10.922 |
| - type: map_at_100 |
| value: 12.626000000000001 |
| - type: map_at_1000 |
| value: 12.883 |
| - type: map_at_3 |
| value: 7.982 |
| - type: map_at_5 |
| value: 9.442 |
| - type: mrr_at_1 |
| value: 21.7 |
| - type: mrr_at_10 |
| value: 31.653 |
| - type: mrr_at_100 |
| value: 32.757999999999996 |
| - type: mrr_at_1000 |
| value: 32.824999999999996 |
| - type: mrr_at_3 |
| value: 28.266999999999996 |
| - type: mrr_at_5 |
| value: 30.127 |
| - type: ndcg_at_1 |
| value: 21.7 |
| - type: ndcg_at_10 |
| value: 18.355 |
| - type: ndcg_at_100 |
| value: 25.228 |
| - type: ndcg_at_1000 |
| value: 30.164 |
| - type: ndcg_at_3 |
| value: 17.549 |
| - type: ndcg_at_5 |
| value: 15.260000000000002 |
| - type: precision_at_1 |
| value: 21.7 |
| - type: precision_at_10 |
| value: 9.47 |
| - type: precision_at_100 |
| value: 1.9290000000000003 |
| - type: precision_at_1000 |
| value: 0.312 |
| - type: precision_at_3 |
| value: 16.3 |
| - type: precision_at_5 |
| value: 13.28 |
| - type: recall_at_1 |
| value: 4.403 |
| - type: recall_at_10 |
| value: 19.18 |
| - type: recall_at_100 |
| value: 39.182 |
| - type: recall_at_1000 |
| value: 63.378 |
| - type: recall_at_3 |
| value: 9.934999999999999 |
| - type: recall_at_5 |
| value: 13.459999999999999 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sickr-sts |
| name: MTEB SICK-R |
| config: default |
| split: test |
| revision: 20a6d6f312dd54037fe07a32d58e5e168867909d |
| metrics: |
| - type: cos_sim_pearson |
| value: 76.90841073432534 |
| - type: cos_sim_spearman |
| value: 69.2566375434526 |
| - type: euclidean_pearson |
| value: 73.00183878559413 |
| - type: euclidean_spearman |
| value: 69.25664656235413 |
| - type: manhattan_pearson |
| value: 72.89594756197533 |
| - type: manhattan_spearman |
| value: 69.23247111043545 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts12-sts |
| name: MTEB STS12 |
| config: default |
| split: test |
| revision: a0d554a64d88156834ff5ae9920b964011b16384 |
| metrics: |
| - type: cos_sim_pearson |
| value: 69.60878511794063 |
| - type: cos_sim_spearman |
| value: 65.89916377105551 |
| - type: euclidean_pearson |
| value: 66.90761876557181 |
| - type: euclidean_spearman |
| value: 65.89915018368384 |
| - type: manhattan_pearson |
| value: 66.78502575257721 |
| - type: manhattan_spearman |
| value: 65.79977053467938 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts13-sts |
| name: MTEB STS13 |
| config: default |
| split: test |
| revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca |
| metrics: |
| - type: cos_sim_pearson |
| value: 77.2869334987418 |
| - type: cos_sim_spearman |
| value: 77.86961921643416 |
| - type: euclidean_pearson |
| value: 77.43179820479914 |
| - type: euclidean_spearman |
| value: 77.86961921643416 |
| - type: manhattan_pearson |
| value: 77.18900647348373 |
| - type: manhattan_spearman |
| value: 77.61209060062608 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts14-sts |
| name: MTEB STS14 |
| config: default |
| split: test |
| revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 |
| metrics: |
| - type: cos_sim_pearson |
| value: 76.26453932960364 |
| - type: cos_sim_spearman |
| value: 72.81574657995401 |
| - type: euclidean_pearson |
| value: 75.0708953437423 |
| - type: euclidean_spearman |
| value: 72.81574657995401 |
| - type: manhattan_pearson |
| value: 74.88396609999512 |
| - type: manhattan_spearman |
| value: 72.65437562156805 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts15-sts |
| name: MTEB STS15 |
| config: default |
| split: test |
| revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 |
| metrics: |
| - type: cos_sim_pearson |
| value: 82.37827653919395 |
| - type: cos_sim_spearman |
| value: 83.4885552472602 |
| - type: euclidean_pearson |
| value: 82.89377087926749 |
| - type: euclidean_spearman |
| value: 83.4885552472602 |
| - type: manhattan_pearson |
| value: 82.82440771787735 |
| - type: manhattan_spearman |
| value: 83.41449537888975 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts16-sts |
| name: MTEB STS16 |
| config: default |
| split: test |
| revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 |
| metrics: |
| - type: cos_sim_pearson |
| value: 78.7995043673964 |
| - type: cos_sim_spearman |
| value: 80.57804447517638 |
| - type: euclidean_pearson |
| value: 80.03013884278195 |
| - type: euclidean_spearman |
| value: 80.57804447517638 |
| - type: manhattan_pearson |
| value: 80.13406111544424 |
| - type: manhattan_spearman |
| value: 80.65354602648962 |
| - 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: 83.63565989937278 |
| - type: cos_sim_spearman |
| value: 84.4948593656943 |
| - type: euclidean_pearson |
| value: 84.68743074820951 |
| - type: euclidean_spearman |
| value: 84.4948593656943 |
| - type: manhattan_pearson |
| value: 84.43639397781811 |
| - type: manhattan_spearman |
| value: 84.32595552115242 |
| - 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: 65.06382649277246 |
| - type: cos_sim_spearman |
| value: 66.28447782018655 |
| - type: euclidean_pearson |
| value: 67.09895930908392 |
| - type: euclidean_spearman |
| value: 66.28447782018655 |
| - type: manhattan_pearson |
| value: 66.96342453888376 |
| - type: manhattan_spearman |
| value: 66.33876259551842 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/stsbenchmark-sts |
| name: MTEB STSBenchmark |
| config: default |
| split: test |
| revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 |
| metrics: |
| - type: cos_sim_pearson |
| value: 78.43883428940346 |
| - type: cos_sim_spearman |
| value: 79.18395553127085 |
| - type: euclidean_pearson |
| value: 79.22986635457109 |
| - type: euclidean_spearman |
| value: 79.18395553127085 |
| - type: manhattan_pearson |
| value: 79.10921229934691 |
| - type: manhattan_spearman |
| value: 79.02283553930171 |
| - 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: 78.43883433444418 |
| - type: cos_sim_spearman |
| value: 79.18395553127085 |
| - type: euclidean_pearson |
| value: 79.22986642351681 |
| - type: euclidean_spearman |
| value: 79.18395553127085 |
| - type: manhattan_pearson |
| value: 79.10921236746302 |
| - type: manhattan_spearman |
| value: 79.02283553930171 |
| - task: |
| type: Reranking |
| dataset: |
| type: mteb/scidocs-reranking |
| name: MTEB SciDocsRR |
| config: default |
| split: test |
| revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab |
| metrics: |
| - type: map |
| value: 76.9361627171417 |
| - type: mrr |
| value: 93.06577046773126 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/scifact |
| name: MTEB SciFact |
| config: default |
| split: test |
| revision: 0228b52cf27578f30900b9e5271d331663a030d7 |
| metrics: |
| - type: map_at_1 |
| value: 50.693999999999996 |
| - type: map_at_10 |
| value: 59.784000000000006 |
| - type: map_at_100 |
| value: 60.443000000000005 |
| - type: map_at_1000 |
| value: 60.480000000000004 |
| - type: map_at_3 |
| value: 57.028 |
| - type: map_at_5 |
| value: 58.306999999999995 |
| - type: mrr_at_1 |
| value: 53.333 |
| - type: mrr_at_10 |
| value: 61.565000000000005 |
| - type: mrr_at_100 |
| value: 62.095 |
| - type: mrr_at_1000 |
| value: 62.131 |
| - type: mrr_at_3 |
| value: 59.721999999999994 |
| - type: mrr_at_5 |
| value: 60.589000000000006 |
| - type: ndcg_at_1 |
| value: 53.333 |
| - type: ndcg_at_10 |
| value: 64.512 |
| - type: ndcg_at_100 |
| value: 67.366 |
| - type: ndcg_at_1000 |
| value: 68.46799999999999 |
| - type: ndcg_at_3 |
| value: 59.748999999999995 |
| - type: ndcg_at_5 |
| value: 61.526 |
| - type: precision_at_1 |
| value: 53.333 |
| - type: precision_at_10 |
| value: 8.733 |
| - type: precision_at_100 |
| value: 1.027 |
| - type: precision_at_1000 |
| value: 0.11199999999999999 |
| - type: precision_at_3 |
| value: 23.222 |
| - type: precision_at_5 |
| value: 15.2 |
| - type: recall_at_1 |
| value: 50.693999999999996 |
| - type: recall_at_10 |
| value: 77.333 |
| - type: recall_at_100 |
| value: 90.10000000000001 |
| - type: recall_at_1000 |
| value: 99 |
| - type: recall_at_3 |
| value: 64.39399999999999 |
| - type: recall_at_5 |
| value: 68.7 |
| - task: |
| type: PairClassification |
| dataset: |
| type: mteb/sprintduplicatequestions-pairclassification |
| name: MTEB SprintDuplicateQuestions |
| config: default |
| split: test |
| revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 |
| metrics: |
| - type: cos_sim_accuracy |
| value: 99.81386138613861 |
| - type: cos_sim_ap |
| value: 94.96375600031361 |
| - type: cos_sim_f1 |
| value: 90.36885245901641 |
| - type: cos_sim_precision |
| value: 92.64705882352942 |
| - type: cos_sim_recall |
| value: 88.2 |
| - type: dot_accuracy |
| value: 99.81386138613861 |
| - type: dot_ap |
| value: 94.96375600031361 |
| - type: dot_f1 |
| value: 90.36885245901641 |
| - type: dot_precision |
| value: 92.64705882352942 |
| - type: dot_recall |
| value: 88.2 |
| - type: euclidean_accuracy |
| value: 99.81386138613861 |
| - type: euclidean_ap |
| value: 94.96375600031361 |
| - type: euclidean_f1 |
| value: 90.36885245901641 |
| - type: euclidean_precision |
| value: 92.64705882352942 |
| - type: euclidean_recall |
| value: 88.2 |
| - type: manhattan_accuracy |
| value: 99.81287128712871 |
| - type: manhattan_ap |
| value: 94.92563500640084 |
| - type: manhattan_f1 |
| value: 90.27277406073082 |
| - type: manhattan_precision |
| value: 93.00106044538707 |
| - type: manhattan_recall |
| value: 87.7 |
| - type: max_accuracy |
| value: 99.81386138613861 |
| - type: max_ap |
| value: 94.96375600031361 |
| - type: max_f1 |
| value: 90.36885245901641 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/stackexchange-clustering |
| name: MTEB StackExchangeClustering |
| config: default |
| split: test |
| revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 |
| metrics: |
| - type: v_measure |
| value: 57.486984956276274 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/stackexchange-clustering-p2p |
| name: MTEB StackExchangeClusteringP2P |
| config: default |
| split: test |
| revision: 815ca46b2622cec33ccafc3735d572c266efdb44 |
| metrics: |
| - type: v_measure |
| value: 34.58453023612073 |
| - task: |
| type: Reranking |
| dataset: |
| type: mteb/stackoverflowdupquestions-reranking |
| name: MTEB StackOverflowDupQuestions |
| config: default |
| split: test |
| revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 |
| metrics: |
| - type: map |
| value: 50.16317315282306 |
| - type: mrr |
| value: 50.82617137764197 |
| - task: |
| type: Summarization |
| dataset: |
| type: mteb/summeval |
| name: MTEB SummEval |
| config: default |
| split: test |
| revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c |
| metrics: |
| - type: cos_sim_pearson |
| value: 30.2927995133324 |
| - type: cos_sim_spearman |
| value: 30.09648622523191 |
| - type: dot_pearson |
| value: 30.29279853541771 |
| - type: dot_spearman |
| value: 30.09648622523191 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/trec-covid |
| name: MTEB TRECCOVID |
| config: default |
| split: test |
| revision: bb9466bac8153a0349341eb1b22e06409e78ef4e |
| metrics: |
| - type: map_at_1 |
| value: 0.23500000000000001 |
| - type: map_at_10 |
| value: 2.01 |
| - type: map_at_100 |
| value: 12.064 |
| - type: map_at_1000 |
| value: 27.437 |
| - type: map_at_3 |
| value: 0.6649999999999999 |
| - type: map_at_5 |
| value: 1.0959999999999999 |
| - type: mrr_at_1 |
| value: 88 |
| - type: mrr_at_10 |
| value: 92.667 |
| - type: mrr_at_100 |
| value: 92.667 |
| - type: mrr_at_1000 |
| value: 92.667 |
| - type: mrr_at_3 |
| value: 91.667 |
| - type: mrr_at_5 |
| value: 92.667 |
| - type: ndcg_at_1 |
| value: 84 |
| - type: ndcg_at_10 |
| value: 79.431 |
| - type: ndcg_at_100 |
| value: 60.914 |
| - type: ndcg_at_1000 |
| value: 52.005 |
| - type: ndcg_at_3 |
| value: 82.285 |
| - type: ndcg_at_5 |
| value: 81.565 |
| - type: precision_at_1 |
| value: 88 |
| - type: precision_at_10 |
| value: 84.8 |
| - type: precision_at_100 |
| value: 62.32 |
| - type: precision_at_1000 |
| value: 23.014000000000003 |
| - type: precision_at_3 |
| value: 86.667 |
| - type: precision_at_5 |
| value: 87.2 |
| - type: recall_at_1 |
| value: 0.23500000000000001 |
| - type: recall_at_10 |
| value: 2.19 |
| - type: recall_at_100 |
| value: 14.904 |
| - type: recall_at_1000 |
| value: 47.875 |
| - type: recall_at_3 |
| value: 0.695 |
| - type: recall_at_5 |
| value: 1.165 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/touche2020 |
| name: MTEB Touche2020 |
| config: default |
| split: test |
| revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f |
| metrics: |
| - type: map_at_1 |
| value: 3.639 |
| - type: map_at_10 |
| value: 14.184 |
| - type: map_at_100 |
| value: 20.61 |
| - type: map_at_1000 |
| value: 22.377 |
| - type: map_at_3 |
| value: 9.163 |
| - type: map_at_5 |
| value: 10.773000000000001 |
| - type: mrr_at_1 |
| value: 46.939 |
| - type: mrr_at_10 |
| value: 59.345000000000006 |
| - type: mrr_at_100 |
| value: 60.07599999999999 |
| - type: mrr_at_1000 |
| value: 60.07599999999999 |
| - type: mrr_at_3 |
| value: 55.782 |
| - type: mrr_at_5 |
| value: 58.231 |
| - type: ndcg_at_1 |
| value: 41.837 |
| - type: ndcg_at_10 |
| value: 32.789 |
| - type: ndcg_at_100 |
| value: 42.232 |
| - type: ndcg_at_1000 |
| value: 53.900999999999996 |
| - type: ndcg_at_3 |
| value: 41.963 |
| - type: ndcg_at_5 |
| value: 35.983 |
| - type: precision_at_1 |
| value: 46.939 |
| - type: precision_at_10 |
| value: 28.163 |
| - type: precision_at_100 |
| value: 8.102 |
| - type: precision_at_1000 |
| value: 1.59 |
| - type: precision_at_3 |
| value: 44.897999999999996 |
| - type: precision_at_5 |
| value: 34.694 |
| - type: recall_at_1 |
| value: 3.639 |
| - type: recall_at_10 |
| value: 19.308 |
| - type: recall_at_100 |
| value: 48.992000000000004 |
| - type: recall_at_1000 |
| value: 84.59400000000001 |
| - type: recall_at_3 |
| value: 9.956 |
| - type: recall_at_5 |
| value: 12.33 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/toxic_conversations_50k |
| name: MTEB ToxicConversationsClassification |
| config: default |
| split: test |
| revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de |
| metrics: |
| - type: accuracy |
| value: 64.305 |
| - type: ap |
| value: 11.330746746072599 |
| - type: f1 |
| value: 49.290704382387865 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/tweet_sentiment_extraction |
| name: MTEB TweetSentimentExtractionClassification |
| config: default |
| split: test |
| revision: d604517c81ca91fe16a244d1248fc021f9ecee7a |
| metrics: |
| - type: accuracy |
| value: 56.1941143180532 |
| - type: f1 |
| value: 56.40189765095578 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/twentynewsgroups-clustering |
| name: MTEB TwentyNewsgroupsClustering |
| config: default |
| split: test |
| revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 |
| metrics: |
| - type: v_measure |
| value: 36.28189332526842 |
| - task: |
| type: PairClassification |
| dataset: |
| type: mteb/twittersemeval2015-pairclassification |
| name: MTEB TwitterSemEval2015 |
| config: default |
| split: test |
| revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 |
| metrics: |
| - type: cos_sim_accuracy |
| value: 83.1912737676581 |
| - type: cos_sim_ap |
| value: 64.31536990146257 |
| - type: cos_sim_f1 |
| value: 61.095167030191696 |
| - type: cos_sim_precision |
| value: 54.074375127006704 |
| - type: cos_sim_recall |
| value: 70.21108179419525 |
| - type: dot_accuracy |
| value: 83.1912737676581 |
| - type: dot_ap |
| value: 64.31539216162541 |
| - type: dot_f1 |
| value: 61.095167030191696 |
| - type: dot_precision |
| value: 54.074375127006704 |
| - type: dot_recall |
| value: 70.21108179419525 |
| - type: euclidean_accuracy |
| value: 83.1912737676581 |
| - type: euclidean_ap |
| value: 64.31538391358727 |
| - type: euclidean_f1 |
| value: 61.095167030191696 |
| - type: euclidean_precision |
| value: 54.074375127006704 |
| - type: euclidean_recall |
| value: 70.21108179419525 |
| - type: manhattan_accuracy |
| value: 83.07206294331525 |
| - type: manhattan_ap |
| value: 64.14646315556838 |
| - type: manhattan_f1 |
| value: 61.194029850746254 |
| - type: manhattan_precision |
| value: 54.166666666666664 |
| - type: manhattan_recall |
| value: 70.31662269129288 |
| - type: max_accuracy |
| value: 83.1912737676581 |
| - type: max_ap |
| value: 64.31539216162541 |
| - type: max_f1 |
| value: 61.194029850746254 |
| - task: |
| type: PairClassification |
| dataset: |
| type: mteb/twitterurlcorpus-pairclassification |
| name: MTEB TwitterURLCorpus |
| config: default |
| split: test |
| revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf |
| metrics: |
| - type: cos_sim_accuracy |
| value: 88.38242713548337 |
| - type: cos_sim_ap |
| value: 84.70041255196017 |
| - type: cos_sim_f1 |
| value: 77.13222561986515 |
| - type: cos_sim_precision |
| value: 73.95266690215472 |
| - type: cos_sim_recall |
| value: 80.59747459193102 |
| - type: dot_accuracy |
| value: 88.38242713548337 |
| - type: dot_ap |
| value: 84.7004118720222 |
| - type: dot_f1 |
| value: 77.13222561986515 |
| - type: dot_precision |
| value: 73.95266690215472 |
| - type: dot_recall |
| value: 80.59747459193102 |
| - type: euclidean_accuracy |
| value: 88.38242713548337 |
| - type: euclidean_ap |
| value: 84.70041593996575 |
| - type: euclidean_f1 |
| value: 77.13222561986515 |
| - type: euclidean_precision |
| value: 73.95266690215472 |
| - type: euclidean_recall |
| value: 80.59747459193102 |
| - type: manhattan_accuracy |
| value: 88.36108200411378 |
| - type: manhattan_ap |
| value: 84.66897701572054 |
| - type: manhattan_f1 |
| value: 77.00707640360645 |
| - type: manhattan_precision |
| value: 72.17695778062082 |
| - type: manhattan_recall |
| value: 82.53002771789343 |
| - type: max_accuracy |
| value: 88.38242713548337 |
| - type: max_ap |
| value: 84.70041593996575 |
| - type: max_f1 |
| value: 77.13222561986515 |
| - task: |
| type: Clustering |
| dataset: |
| type: jinaai/cities_wiki_clustering |
| name: MTEB WikiCitiesClustering |
| config: default |
| split: test |
| revision: ddc9ee9242fa65332597f70e967ecc38b9d734fa |
| metrics: |
| - type: v_measure |
| value: 81.46426354153643 |
| --- |
| <h1 align="center">Snowflake's Arctic-embed-xs</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 [infloat/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-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-xs") |
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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? |
| 0.57515126 The Data Cloud! |
| 0.45798576 Mexico City of Course! |
| Query: Where can I get the best tacos? |
| 0.5636022 Mexico City of Course! |
| 0.5044898 The Data Cloud! |
| ``` |
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| ### Using Huggingface transformers |
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| You can use the transformers package for a 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-xs') |
| model = AutoModel.from_pretrained('Snowflake/snowflake-arctic-embed-xs', 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-xs', { |
| 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); // [0.5044895661144148, 0.5636021124426508] |
| ``` |
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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=15cd6ef8-397b-4e85-9d74-27ebdc7e9765" /> |
