| --- |
| license: apache-2.0 |
| pipeline_tag: sentence-similarity |
| tags: |
| - sentence-transformers |
| - feature-extraction |
| - sentence-similarity |
| - mteb |
| - arctic |
| - snowflake-arctic-embed |
| - transformers.js |
| model-index: |
| - name: snowflake-arctic-m-long |
| results: |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_counterfactual |
| name: MTEB AmazonCounterfactualClassification (en) |
| config: en |
| split: test |
| revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
| metrics: |
| - type: accuracy |
| value: 78.4776119402985 |
| - type: ap |
| value: 42.34374238166049 |
| - type: f1 |
| value: 72.51164234732224 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_polarity |
| name: MTEB AmazonPolarityClassification |
| config: default |
| split: test |
| revision: e2d317d38cd51312af73b3d32a06d1a08b442046 |
| metrics: |
| - type: accuracy |
| value: 78.7416 |
| - type: ap |
| value: 73.12074819362377 |
| - type: f1 |
| value: 78.64057339708795 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_reviews_multi |
| name: MTEB AmazonReviewsClassification (en) |
| config: en |
| split: test |
| revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
| metrics: |
| - type: accuracy |
| value: 39.926 |
| - type: f1 |
| value: 39.35531993117573 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/arguana |
| name: MTEB ArguAna |
| config: default |
| split: test |
| revision: c22ab2a51041ffd869aaddef7af8d8215647e41a |
| metrics: |
| - type: map_at_1 |
| value: 34.851 |
| - type: map_at_10 |
| value: 51.473 |
| - type: map_at_100 |
| value: 52.103 |
| - type: map_at_1000 |
| value: 52.105000000000004 |
| - type: map_at_3 |
| value: 46.776 |
| - type: map_at_5 |
| value: 49.617 |
| - type: mrr_at_1 |
| value: 35.491 |
| - type: mrr_at_10 |
| value: 51.73799999999999 |
| - type: mrr_at_100 |
| value: 52.37500000000001 |
| - type: mrr_at_1000 |
| value: 52.378 |
| - type: mrr_at_3 |
| value: 46.965 |
| - type: mrr_at_5 |
| value: 49.878 |
| - type: ndcg_at_1 |
| value: 34.851 |
| - type: ndcg_at_10 |
| value: 60.364 |
| - type: ndcg_at_100 |
| value: 62.888999999999996 |
| - type: ndcg_at_1000 |
| value: 62.946000000000005 |
| - type: ndcg_at_3 |
| value: 50.807 |
| - type: ndcg_at_5 |
| value: 55.901 |
| - type: precision_at_1 |
| value: 34.851 |
| - type: precision_at_10 |
| value: 8.855 |
| - type: precision_at_100 |
| value: 0.992 |
| - type: precision_at_1000 |
| value: 0.1 |
| - type: precision_at_3 |
| value: 20.839 |
| - type: precision_at_5 |
| value: 14.963999999999999 |
| - type: recall_at_1 |
| value: 34.851 |
| - type: recall_at_10 |
| value: 88.549 |
| - type: recall_at_100 |
| value: 99.21799999999999 |
| - type: recall_at_1000 |
| value: 99.644 |
| - type: recall_at_3 |
| value: 62.517999999999994 |
| - type: recall_at_5 |
| value: 74.822 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/arxiv-clustering-p2p |
| name: MTEB ArxivClusteringP2P |
| config: default |
| split: test |
| revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d |
| metrics: |
| - type: v_measure |
| value: 45.5554998405317 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/arxiv-clustering-s2s |
| name: MTEB ArxivClusteringS2S |
| config: default |
| split: test |
| revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 |
| metrics: |
| - type: v_measure |
| value: 35.614248811397005 |
| - task: |
| type: Reranking |
| dataset: |
| type: mteb/askubuntudupquestions-reranking |
| name: MTEB AskUbuntuDupQuestions |
| config: default |
| split: test |
| revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 |
| metrics: |
| - type: map |
| value: 61.355489424753884 |
| - type: mrr |
| value: 75.49443784900849 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/biosses-sts |
| name: MTEB BIOSSES |
| config: default |
| split: test |
| revision: d3fb88f8f02e40887cd149695127462bbcf29b4a |
| metrics: |
| - type: cos_sim_pearson |
| value: 89.17311056578292 |
| - type: cos_sim_spearman |
| value: 88.24237210809322 |
| - type: euclidean_pearson |
| value: 87.3188065853646 |
| - type: euclidean_spearman |
| value: 88.24237210809322 |
| - type: manhattan_pearson |
| value: 86.89499710049658 |
| - type: manhattan_spearman |
| value: 87.85441146091777 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/banking77 |
| name: MTEB Banking77Classification |
| config: default |
| split: test |
| revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 |
| metrics: |
| - type: accuracy |
| value: 80.26298701298703 |
| - type: f1 |
| value: 79.68356764080303 |
| - task: |
| type: Clustering |
| dataset: |
| type: jinaai/big-patent-clustering |
| name: MTEB BigPatentClustering |
| config: default |
| split: test |
| revision: 62d5330920bca426ce9d3c76ea914f15fc83e891 |
| metrics: |
| - type: v_measure |
| value: 20.923883720813706 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/biorxiv-clustering-p2p |
| name: MTEB BiorxivClusteringP2P |
| config: default |
| split: test |
| revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 |
| metrics: |
| - type: v_measure |
| value: 36.16058801465044 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/biorxiv-clustering-s2s |
| name: MTEB BiorxivClusteringS2S |
| config: default |
| split: test |
| revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 |
| metrics: |
| - type: v_measure |
| value: 30.1402356118627 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/cqadupstack-android |
| name: MTEB CQADupstackAndroidRetrieval |
| config: default |
| split: test |
| revision: f46a197baaae43b4f621051089b82a364682dfeb |
| metrics: |
| - type: map_at_1 |
| value: 35.612 |
| - type: map_at_10 |
| value: 47.117 |
| - type: map_at_100 |
| value: 48.711 |
| - type: map_at_1000 |
| value: 48.826 |
| - type: map_at_3 |
| value: 43.858999999999995 |
| - type: map_at_5 |
| value: 45.612 |
| - type: mrr_at_1 |
| value: 42.918 |
| - type: mrr_at_10 |
| value: 52.806 |
| - type: mrr_at_100 |
| value: 53.564 |
| - type: mrr_at_1000 |
| value: 53.596999999999994 |
| - type: mrr_at_3 |
| value: 50.453 |
| - type: mrr_at_5 |
| value: 51.841 |
| - type: ndcg_at_1 |
| value: 42.918 |
| - type: ndcg_at_10 |
| value: 53.291999999999994 |
| - type: ndcg_at_100 |
| value: 58.711999999999996 |
| - type: ndcg_at_1000 |
| value: 60.317 |
| - type: ndcg_at_3 |
| value: 48.855 |
| - type: ndcg_at_5 |
| value: 50.778 |
| - type: precision_at_1 |
| value: 42.918 |
| - type: precision_at_10 |
| value: 9.927999999999999 |
| - type: precision_at_100 |
| value: 1.592 |
| - type: precision_at_1000 |
| value: 0.201 |
| - type: precision_at_3 |
| value: 23.366999999999997 |
| - type: precision_at_5 |
| value: 16.366 |
| - type: recall_at_1 |
| value: 35.612 |
| - type: recall_at_10 |
| value: 64.671 |
| - type: recall_at_100 |
| value: 86.97 |
| - type: recall_at_1000 |
| value: 96.99600000000001 |
| - type: recall_at_3 |
| value: 51.37199999999999 |
| - type: recall_at_5 |
| value: 57.094 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/cqadupstack-english |
| name: MTEB CQADupstackEnglishRetrieval |
| config: default |
| split: test |
| revision: ad9991cb51e31e31e430383c75ffb2885547b5f0 |
| metrics: |
| - type: map_at_1 |
| value: 33.742 |
| - type: map_at_10 |
| value: 44.49 |
| - type: map_at_100 |
| value: 45.781 |
| - type: map_at_1000 |
| value: 45.902 |
| - type: map_at_3 |
| value: 41.453 |
| - type: map_at_5 |
| value: 43.251 |
| - type: mrr_at_1 |
| value: 42.357 |
| - type: mrr_at_10 |
| value: 50.463 |
| - type: mrr_at_100 |
| value: 51.17 |
| - type: mrr_at_1000 |
| value: 51.205999999999996 |
| - type: mrr_at_3 |
| value: 48.397 |
| - type: mrr_at_5 |
| value: 49.649 |
| - type: ndcg_at_1 |
| value: 42.357 |
| - type: ndcg_at_10 |
| value: 50.175000000000004 |
| - type: ndcg_at_100 |
| value: 54.491 |
| - type: ndcg_at_1000 |
| value: 56.282 |
| - type: ndcg_at_3 |
| value: 46.159 |
| - type: ndcg_at_5 |
| value: 48.226 |
| - type: precision_at_1 |
| value: 42.357 |
| - type: precision_at_10 |
| value: 9.382 |
| - type: precision_at_100 |
| value: 1.473 |
| - type: precision_at_1000 |
| value: 0.191 |
| - type: precision_at_3 |
| value: 22.187 |
| - type: precision_at_5 |
| value: 15.758 |
| - type: recall_at_1 |
| value: 33.742 |
| - type: recall_at_10 |
| value: 59.760999999999996 |
| - type: recall_at_100 |
| value: 77.89500000000001 |
| - type: recall_at_1000 |
| value: 89.005 |
| - type: recall_at_3 |
| value: 47.872 |
| - type: recall_at_5 |
| value: 53.559 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/cqadupstack-gaming |
| name: MTEB CQADupstackGamingRetrieval |
| config: default |
| split: test |
| revision: 4885aa143210c98657558c04aaf3dc47cfb54340 |
| metrics: |
| - type: map_at_1 |
| value: 43.883 |
| - type: map_at_10 |
| value: 56.464999999999996 |
| - type: map_at_100 |
| value: 57.394 |
| - type: map_at_1000 |
| value: 57.443999999999996 |
| - type: map_at_3 |
| value: 53.169 |
| - type: map_at_5 |
| value: 54.984 |
| - type: mrr_at_1 |
| value: 50.470000000000006 |
| - type: mrr_at_10 |
| value: 59.997 |
| - type: mrr_at_100 |
| value: 60.586 |
| - type: mrr_at_1000 |
| value: 60.61 |
| - type: mrr_at_3 |
| value: 57.837 |
| - type: mrr_at_5 |
| value: 59.019 |
| - type: ndcg_at_1 |
| value: 50.470000000000006 |
| - type: ndcg_at_10 |
| value: 62.134 |
| - type: ndcg_at_100 |
| value: 65.69500000000001 |
| - type: ndcg_at_1000 |
| value: 66.674 |
| - type: ndcg_at_3 |
| value: 56.916999999999994 |
| - type: ndcg_at_5 |
| value: 59.312 |
| - type: precision_at_1 |
| value: 50.470000000000006 |
| - type: precision_at_10 |
| value: 9.812 |
| - type: precision_at_100 |
| value: 1.25 |
| - type: precision_at_1000 |
| value: 0.13699999999999998 |
| - type: precision_at_3 |
| value: 25.119999999999997 |
| - type: precision_at_5 |
| value: 17.016000000000002 |
| - type: recall_at_1 |
| value: 43.883 |
| - type: recall_at_10 |
| value: 75.417 |
| - type: recall_at_100 |
| value: 90.545 |
| - type: recall_at_1000 |
| value: 97.44500000000001 |
| - type: recall_at_3 |
| value: 61.306000000000004 |
| - type: recall_at_5 |
| value: 67.244 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/cqadupstack-gis |
| name: MTEB CQADupstackGisRetrieval |
| config: default |
| split: test |
| revision: 5003b3064772da1887988e05400cf3806fe491f2 |
| metrics: |
| - type: map_at_1 |
| value: 29.813000000000002 |
| - type: map_at_10 |
| value: 38.627 |
| - type: map_at_100 |
| value: 39.735 |
| - type: map_at_1000 |
| value: 39.806000000000004 |
| - type: map_at_3 |
| value: 36.283 |
| - type: map_at_5 |
| value: 37.491 |
| - type: mrr_at_1 |
| value: 32.316 |
| - type: mrr_at_10 |
| value: 40.752 |
| - type: mrr_at_100 |
| value: 41.699000000000005 |
| - type: mrr_at_1000 |
| value: 41.749 |
| - type: mrr_at_3 |
| value: 38.531 |
| - type: mrr_at_5 |
| value: 39.706 |
| - type: ndcg_at_1 |
| value: 32.316 |
| - type: ndcg_at_10 |
| value: 43.524 |
| - type: ndcg_at_100 |
| value: 48.648 |
| - type: ndcg_at_1000 |
| value: 50.405 |
| - type: ndcg_at_3 |
| value: 38.928000000000004 |
| - type: ndcg_at_5 |
| value: 40.967 |
| - type: precision_at_1 |
| value: 32.316 |
| - type: precision_at_10 |
| value: 6.451999999999999 |
| - type: precision_at_100 |
| value: 0.9490000000000001 |
| - type: precision_at_1000 |
| value: 0.11299999999999999 |
| - type: precision_at_3 |
| value: 16.384 |
| - type: precision_at_5 |
| value: 11.006 |
| - type: recall_at_1 |
| value: 29.813000000000002 |
| - type: recall_at_10 |
| value: 56.562999999999995 |
| - type: recall_at_100 |
| value: 79.452 |
| - type: recall_at_1000 |
| value: 92.715 |
| - type: recall_at_3 |
| value: 43.985 |
| - type: recall_at_5 |
| value: 49.001 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/cqadupstack-mathematica |
| name: MTEB CQADupstackMathematicaRetrieval |
| config: default |
| split: test |
| revision: 90fceea13679c63fe563ded68f3b6f06e50061de |
| metrics: |
| - type: map_at_1 |
| value: 19.961000000000002 |
| - type: map_at_10 |
| value: 28.026 |
| - type: map_at_100 |
| value: 29.212 |
| - type: map_at_1000 |
| value: 29.332 |
| - type: map_at_3 |
| value: 25.296999999999997 |
| - type: map_at_5 |
| value: 26.832 |
| - type: mrr_at_1 |
| value: 24.627 |
| - type: mrr_at_10 |
| value: 33.045 |
| - type: mrr_at_100 |
| value: 33.944 |
| - type: mrr_at_1000 |
| value: 34.013 |
| - type: mrr_at_3 |
| value: 30.307000000000002 |
| - type: mrr_at_5 |
| value: 31.874000000000002 |
| - type: ndcg_at_1 |
| value: 24.627 |
| - type: ndcg_at_10 |
| value: 33.414 |
| - type: ndcg_at_100 |
| value: 39.061 |
| - type: ndcg_at_1000 |
| value: 41.795 |
| - type: ndcg_at_3 |
| value: 28.377000000000002 |
| - type: ndcg_at_5 |
| value: 30.781999999999996 |
| - type: precision_at_1 |
| value: 24.627 |
| - type: precision_at_10 |
| value: 6.02 |
| - type: precision_at_100 |
| value: 1.035 |
| - type: precision_at_1000 |
| value: 0.13899999999999998 |
| - type: precision_at_3 |
| value: 13.516 |
| - type: precision_at_5 |
| value: 9.851 |
| - type: recall_at_1 |
| value: 19.961000000000002 |
| - type: recall_at_10 |
| value: 45.174 |
| - type: recall_at_100 |
| value: 69.69 |
| - type: recall_at_1000 |
| value: 89.24600000000001 |
| - type: recall_at_3 |
| value: 31.062 |
| - type: recall_at_5 |
| value: 37.193 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/cqadupstack-physics |
| name: MTEB CQADupstackPhysicsRetrieval |
| config: default |
| split: test |
| revision: 79531abbd1fb92d06c6d6315a0cbbbf5bb247ea4 |
| metrics: |
| - type: map_at_1 |
| value: 32.080999999999996 |
| - type: map_at_10 |
| value: 42.177 |
| - type: map_at_100 |
| value: 43.431999999999995 |
| - type: map_at_1000 |
| value: 43.533 |
| - type: map_at_3 |
| value: 38.721 |
| - type: map_at_5 |
| value: 40.669 |
| - type: mrr_at_1 |
| value: 38.787 |
| - type: mrr_at_10 |
| value: 47.762 |
| - type: mrr_at_100 |
| value: 48.541000000000004 |
| - type: mrr_at_1000 |
| value: 48.581 |
| - type: mrr_at_3 |
| value: 45.123999999999995 |
| - type: mrr_at_5 |
| value: 46.639 |
| - type: ndcg_at_1 |
| value: 38.787 |
| - type: ndcg_at_10 |
| value: 48.094 |
| - type: ndcg_at_100 |
| value: 53.291 |
| - type: ndcg_at_1000 |
| value: 55.21 |
| - type: ndcg_at_3 |
| value: 42.721 |
| - type: ndcg_at_5 |
| value: 45.301 |
| - type: precision_at_1 |
| value: 38.787 |
| - type: precision_at_10 |
| value: 8.576 |
| - type: precision_at_100 |
| value: 1.306 |
| - type: precision_at_1000 |
| value: 0.164 |
| - type: precision_at_3 |
| value: 19.698 |
| - type: precision_at_5 |
| value: 14.013 |
| - type: recall_at_1 |
| value: 32.080999999999996 |
| - type: recall_at_10 |
| value: 59.948 |
| - type: recall_at_100 |
| value: 81.811 |
| - type: recall_at_1000 |
| value: 94.544 |
| - type: recall_at_3 |
| value: 44.903999999999996 |
| - type: recall_at_5 |
| value: 51.763999999999996 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/cqadupstack-programmers |
| name: MTEB CQADupstackProgrammersRetrieval |
| config: default |
| split: test |
| revision: 6184bc1440d2dbc7612be22b50686b8826d22b32 |
| metrics: |
| - type: map_at_1 |
| value: 28.869 |
| - type: map_at_10 |
| value: 38.954 |
| - type: map_at_100 |
| value: 40.233000000000004 |
| - type: map_at_1000 |
| value: 40.332 |
| - type: map_at_3 |
| value: 35.585 |
| - type: map_at_5 |
| value: 37.476 |
| - type: mrr_at_1 |
| value: 35.959 |
| - type: mrr_at_10 |
| value: 44.800000000000004 |
| - type: mrr_at_100 |
| value: 45.609 |
| - type: mrr_at_1000 |
| value: 45.655 |
| - type: mrr_at_3 |
| value: 42.333 |
| - type: mrr_at_5 |
| value: 43.68 |
| - type: ndcg_at_1 |
| value: 35.959 |
| - type: ndcg_at_10 |
| value: 44.957 |
| - type: ndcg_at_100 |
| value: 50.275000000000006 |
| - type: ndcg_at_1000 |
| value: 52.29899999999999 |
| - type: ndcg_at_3 |
| value: 39.797 |
| - type: ndcg_at_5 |
| value: 42.128 |
| - type: precision_at_1 |
| value: 35.959 |
| - type: precision_at_10 |
| value: 8.185 |
| - type: precision_at_100 |
| value: 1.261 |
| - type: precision_at_1000 |
| value: 0.159 |
| - type: precision_at_3 |
| value: 18.988 |
| - type: precision_at_5 |
| value: 13.516 |
| - type: recall_at_1 |
| value: 28.869 |
| - type: recall_at_10 |
| value: 57.154 |
| - type: recall_at_100 |
| value: 79.764 |
| - type: recall_at_1000 |
| value: 93.515 |
| - type: recall_at_3 |
| value: 42.364000000000004 |
| - type: recall_at_5 |
| value: 48.756 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/cqadupstack |
| name: MTEB CQADupstackRetrieval |
| config: default |
| split: test |
| revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 |
| metrics: |
| - type: map_at_1 |
| value: 29.31008333333333 |
| - type: map_at_10 |
| value: 38.81849999999999 |
| - type: map_at_100 |
| value: 40.05058333333334 |
| - type: map_at_1000 |
| value: 40.16116666666667 |
| - type: map_at_3 |
| value: 35.91441666666667 |
| - type: map_at_5 |
| value: 37.526583333333335 |
| - type: mrr_at_1 |
| value: 34.60066666666667 |
| - type: mrr_at_10 |
| value: 43.08858333333333 |
| - type: mrr_at_100 |
| value: 43.927749999999996 |
| - type: mrr_at_1000 |
| value: 43.97866666666667 |
| - type: mrr_at_3 |
| value: 40.72775 |
| - type: mrr_at_5 |
| value: 42.067249999999994 |
| - type: ndcg_at_1 |
| value: 34.60066666666667 |
| - type: ndcg_at_10 |
| value: 44.20841666666667 |
| - type: ndcg_at_100 |
| value: 49.32866666666667 |
| - type: ndcg_at_1000 |
| value: 51.373999999999995 |
| - type: ndcg_at_3 |
| value: 39.452083333333334 |
| - type: ndcg_at_5 |
| value: 41.67 |
| - type: precision_at_1 |
| value: 34.60066666666667 |
| - type: precision_at_10 |
| value: 7.616583333333334 |
| - type: precision_at_100 |
| value: 1.20175 |
| - type: precision_at_1000 |
| value: 0.156 |
| - type: precision_at_3 |
| value: 17.992 |
| - type: precision_at_5 |
| value: 12.658416666666666 |
| - type: recall_at_1 |
| value: 29.31008333333333 |
| - type: recall_at_10 |
| value: 55.81900000000001 |
| - type: recall_at_100 |
| value: 78.06308333333334 |
| - type: recall_at_1000 |
| value: 92.10641666666668 |
| - type: recall_at_3 |
| value: 42.50166666666667 |
| - type: recall_at_5 |
| value: 48.26108333333333 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/cqadupstack-stats |
| name: MTEB CQADupstackStatsRetrieval |
| config: default |
| split: test |
| revision: 65ac3a16b8e91f9cee4c9828cc7c335575432a2a |
| metrics: |
| - type: map_at_1 |
| value: 26.773000000000003 |
| - type: map_at_10 |
| value: 34.13 |
| - type: map_at_100 |
| value: 35.113 |
| - type: map_at_1000 |
| value: 35.211 |
| - type: map_at_3 |
| value: 31.958 |
| - type: map_at_5 |
| value: 33.080999999999996 |
| - type: mrr_at_1 |
| value: 30.061 |
| - type: mrr_at_10 |
| value: 37.061 |
| - type: mrr_at_100 |
| value: 37.865 |
| - type: mrr_at_1000 |
| value: 37.939 |
| - type: mrr_at_3 |
| value: 34.995 |
| - type: mrr_at_5 |
| value: 36.092 |
| - type: ndcg_at_1 |
| value: 30.061 |
| - type: ndcg_at_10 |
| value: 38.391999999999996 |
| - type: ndcg_at_100 |
| value: 43.13 |
| - type: ndcg_at_1000 |
| value: 45.449 |
| - type: ndcg_at_3 |
| value: 34.411 |
| - type: ndcg_at_5 |
| value: 36.163000000000004 |
| - type: precision_at_1 |
| value: 30.061 |
| - type: precision_at_10 |
| value: 5.982 |
| - type: precision_at_100 |
| value: 0.911 |
| - type: precision_at_1000 |
| value: 0.11800000000000001 |
| - type: precision_at_3 |
| value: 14.673 |
| - type: precision_at_5 |
| value: 10.030999999999999 |
| - type: recall_at_1 |
| value: 26.773000000000003 |
| - type: recall_at_10 |
| value: 48.445 |
| - type: recall_at_100 |
| value: 69.741 |
| - type: recall_at_1000 |
| value: 86.59 |
| - type: recall_at_3 |
| value: 37.576 |
| - type: recall_at_5 |
| value: 41.948 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/cqadupstack-tex |
| name: MTEB CQADupstackTexRetrieval |
| config: default |
| split: test |
| revision: 46989137a86843e03a6195de44b09deda022eec7 |
| metrics: |
| - type: map_at_1 |
| value: 18.556 |
| - type: map_at_10 |
| value: 26.340999999999998 |
| - type: map_at_100 |
| value: 27.560000000000002 |
| - type: map_at_1000 |
| value: 27.685 |
| - type: map_at_3 |
| value: 24.136 |
| - type: map_at_5 |
| value: 25.34 |
| - type: mrr_at_1 |
| value: 22.368 |
| - type: mrr_at_10 |
| value: 30.192999999999998 |
| - type: mrr_at_100 |
| value: 31.183 |
| - type: mrr_at_1000 |
| value: 31.258000000000003 |
| - type: mrr_at_3 |
| value: 28.223 |
| - type: mrr_at_5 |
| value: 29.294999999999998 |
| - type: ndcg_at_1 |
| value: 22.368 |
| - type: ndcg_at_10 |
| value: 31.029 |
| - type: ndcg_at_100 |
| value: 36.768 |
| - type: ndcg_at_1000 |
| value: 39.572 |
| - type: ndcg_at_3 |
| value: 27.197 |
| - type: ndcg_at_5 |
| value: 28.912 |
| - type: precision_at_1 |
| value: 22.368 |
| - type: precision_at_10 |
| value: 5.606 |
| - type: precision_at_100 |
| value: 0.9979999999999999 |
| - type: precision_at_1000 |
| value: 0.14100000000000001 |
| - type: precision_at_3 |
| value: 12.892999999999999 |
| - type: precision_at_5 |
| value: 9.16 |
| - type: recall_at_1 |
| value: 18.556 |
| - type: recall_at_10 |
| value: 41.087 |
| - type: recall_at_100 |
| value: 66.92 |
| - type: recall_at_1000 |
| value: 86.691 |
| - type: recall_at_3 |
| value: 30.415 |
| - type: recall_at_5 |
| value: 34.813 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/cqadupstack-unix |
| name: MTEB CQADupstackUnixRetrieval |
| config: default |
| split: test |
| revision: 6c6430d3a6d36f8d2a829195bc5dc94d7e063e53 |
| metrics: |
| - type: map_at_1 |
| value: 29.953999999999997 |
| - type: map_at_10 |
| value: 39.633 |
| - type: map_at_100 |
| value: 40.923 |
| - type: map_at_1000 |
| value: 41.016000000000005 |
| - type: map_at_3 |
| value: 36.609 |
| - type: map_at_5 |
| value: 38.443 |
| - type: mrr_at_1 |
| value: 35.354 |
| - type: mrr_at_10 |
| value: 43.718 |
| - type: mrr_at_100 |
| value: 44.651999999999994 |
| - type: mrr_at_1000 |
| value: 44.696000000000005 |
| - type: mrr_at_3 |
| value: 41.154 |
| - type: mrr_at_5 |
| value: 42.730000000000004 |
| - type: ndcg_at_1 |
| value: 35.354 |
| - type: ndcg_at_10 |
| value: 44.933 |
| - type: ndcg_at_100 |
| value: 50.577000000000005 |
| - type: ndcg_at_1000 |
| value: 52.428 |
| - type: ndcg_at_3 |
| value: 39.833 |
| - type: ndcg_at_5 |
| value: 42.465 |
| - type: precision_at_1 |
| value: 35.354 |
| - type: precision_at_10 |
| value: 7.416 |
| - type: precision_at_100 |
| value: 1.157 |
| - type: precision_at_1000 |
| value: 0.14100000000000001 |
| - type: precision_at_3 |
| value: 17.817 |
| - type: precision_at_5 |
| value: 12.687000000000001 |
| - type: recall_at_1 |
| value: 29.953999999999997 |
| - type: recall_at_10 |
| value: 56.932 |
| - type: recall_at_100 |
| value: 80.93900000000001 |
| - type: recall_at_1000 |
| value: 93.582 |
| - type: recall_at_3 |
| value: 43.192 |
| - type: recall_at_5 |
| value: 49.757 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/cqadupstack-webmasters |
| name: MTEB CQADupstackWebmastersRetrieval |
| config: default |
| split: test |
| revision: 160c094312a0e1facb97e55eeddb698c0abe3571 |
| metrics: |
| - type: map_at_1 |
| value: 27.85 |
| - type: map_at_10 |
| value: 37.68 |
| - type: map_at_100 |
| value: 39.295 |
| - type: map_at_1000 |
| value: 39.527 |
| - type: map_at_3 |
| value: 35.036 |
| - type: map_at_5 |
| value: 36.269 |
| - type: mrr_at_1 |
| value: 33.004 |
| - type: mrr_at_10 |
| value: 42.096000000000004 |
| - type: mrr_at_100 |
| value: 43.019 |
| - type: mrr_at_1000 |
| value: 43.071 |
| - type: mrr_at_3 |
| value: 39.987 |
| - type: mrr_at_5 |
| value: 40.995 |
| - type: ndcg_at_1 |
| value: 33.004 |
| - type: ndcg_at_10 |
| value: 43.461 |
| - type: ndcg_at_100 |
| value: 49.138 |
| - type: ndcg_at_1000 |
| value: 51.50900000000001 |
| - type: ndcg_at_3 |
| value: 39.317 |
| - type: ndcg_at_5 |
| value: 40.760999999999996 |
| - type: precision_at_1 |
| value: 33.004 |
| - type: precision_at_10 |
| value: 8.161999999999999 |
| - type: precision_at_100 |
| value: 1.583 |
| - type: precision_at_1000 |
| value: 0.245 |
| - type: precision_at_3 |
| value: 18.445 |
| - type: precision_at_5 |
| value: 12.885 |
| - type: recall_at_1 |
| value: 27.85 |
| - type: recall_at_10 |
| value: 54.419 |
| - type: recall_at_100 |
| value: 79.742 |
| - type: recall_at_1000 |
| value: 93.97 |
| - type: recall_at_3 |
| value: 42.149 |
| - type: recall_at_5 |
| value: 46.165 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/cqadupstack-wordpress |
| name: MTEB CQADupstackWordpressRetrieval |
| config: default |
| split: test |
| revision: 4ffe81d471b1924886b33c7567bfb200e9eec5c4 |
| metrics: |
| - type: map_at_1 |
| value: 24.627 |
| - type: map_at_10 |
| value: 32.182 |
| - type: map_at_100 |
| value: 33.217999999999996 |
| - type: map_at_1000 |
| value: 33.32 |
| - type: map_at_3 |
| value: 28.866999999999997 |
| - type: map_at_5 |
| value: 30.871 |
| - type: mrr_at_1 |
| value: 26.987 |
| - type: mrr_at_10 |
| value: 34.37 |
| - type: mrr_at_100 |
| value: 35.301 |
| - type: mrr_at_1000 |
| value: 35.369 |
| - type: mrr_at_3 |
| value: 31.391999999999996 |
| - type: mrr_at_5 |
| value: 33.287 |
| - type: ndcg_at_1 |
| value: 26.987 |
| - type: ndcg_at_10 |
| value: 37.096000000000004 |
| - type: ndcg_at_100 |
| value: 42.158 |
| - type: ndcg_at_1000 |
| value: 44.548 |
| - type: ndcg_at_3 |
| value: 30.913 |
| - type: ndcg_at_5 |
| value: 34.245 |
| - type: precision_at_1 |
| value: 26.987 |
| - type: precision_at_10 |
| value: 5.878 |
| - type: precision_at_100 |
| value: 0.906 |
| - type: precision_at_1000 |
| value: 0.123 |
| - type: precision_at_3 |
| value: 12.815999999999999 |
| - type: precision_at_5 |
| value: 9.612 |
| - type: recall_at_1 |
| value: 24.627 |
| - type: recall_at_10 |
| value: 50.257 |
| - type: recall_at_100 |
| value: 73.288 |
| - type: recall_at_1000 |
| value: 90.97800000000001 |
| - type: recall_at_3 |
| value: 33.823 |
| - type: recall_at_5 |
| value: 41.839 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/climate-fever |
| name: MTEB ClimateFEVER |
| config: default |
| split: test |
| revision: 47f2ac6acb640fc46020b02a5b59fdda04d39380 |
| metrics: |
| - type: map_at_1 |
| value: 17.343 |
| - type: map_at_10 |
| value: 28.59 |
| - type: map_at_100 |
| value: 30.591 |
| - type: map_at_1000 |
| value: 30.759999999999998 |
| - type: map_at_3 |
| value: 24.197 |
| - type: map_at_5 |
| value: 26.433 |
| - type: mrr_at_1 |
| value: 39.609 |
| - type: mrr_at_10 |
| value: 51.107 |
| - type: mrr_at_100 |
| value: 51.87199999999999 |
| - type: mrr_at_1000 |
| value: 51.894 |
| - type: mrr_at_3 |
| value: 48.154 |
| - type: mrr_at_5 |
| value: 49.939 |
| - type: ndcg_at_1 |
| value: 39.609 |
| - type: ndcg_at_10 |
| value: 38.329 |
| - type: ndcg_at_100 |
| value: 45.573 |
| - type: ndcg_at_1000 |
| value: 48.405 |
| - type: ndcg_at_3 |
| value: 32.506 |
| - type: ndcg_at_5 |
| value: 34.331 |
| - type: precision_at_1 |
| value: 39.609 |
| - type: precision_at_10 |
| value: 11.668000000000001 |
| - type: precision_at_100 |
| value: 1.9539999999999997 |
| - type: precision_at_1000 |
| value: 0.249 |
| - type: precision_at_3 |
| value: 23.952 |
| - type: precision_at_5 |
| value: 17.902 |
| - type: recall_at_1 |
| value: 17.343 |
| - type: recall_at_10 |
| value: 43.704 |
| - type: recall_at_100 |
| value: 68.363 |
| - type: recall_at_1000 |
| value: 84.04599999999999 |
| - type: recall_at_3 |
| value: 29.028 |
| - type: recall_at_5 |
| value: 35.022 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/dbpedia |
| name: MTEB DBPedia |
| config: default |
| split: test |
| revision: c0f706b76e590d620bd6618b3ca8efdd34e2d659 |
| metrics: |
| - type: map_at_1 |
| value: 9.934999999999999 |
| - type: map_at_10 |
| value: 22.081 |
| - type: map_at_100 |
| value: 32.036 |
| - type: map_at_1000 |
| value: 33.803 |
| - type: map_at_3 |
| value: 15.687999999999999 |
| - type: map_at_5 |
| value: 18.357 |
| - type: mrr_at_1 |
| value: 70.75 |
| - type: mrr_at_10 |
| value: 78.506 |
| - type: mrr_at_100 |
| value: 78.874 |
| - type: mrr_at_1000 |
| value: 78.88300000000001 |
| - type: mrr_at_3 |
| value: 77.667 |
| - type: mrr_at_5 |
| value: 78.342 |
| - type: ndcg_at_1 |
| value: 57.25 |
| - type: ndcg_at_10 |
| value: 45.286 |
| - type: ndcg_at_100 |
| value: 50.791 |
| - type: ndcg_at_1000 |
| value: 58.021 |
| - type: ndcg_at_3 |
| value: 49.504 |
| - type: ndcg_at_5 |
| value: 47.03 |
| - type: precision_at_1 |
| value: 70.75 |
| - type: precision_at_10 |
| value: 36.425000000000004 |
| - type: precision_at_100 |
| value: 11.953 |
| - type: precision_at_1000 |
| value: 2.248 |
| - type: precision_at_3 |
| value: 53.25 |
| - type: precision_at_5 |
| value: 46.150000000000006 |
| - type: recall_at_1 |
| value: 9.934999999999999 |
| - type: recall_at_10 |
| value: 27.592 |
| - type: recall_at_100 |
| value: 58.089 |
| - type: recall_at_1000 |
| value: 81.025 |
| - type: recall_at_3 |
| value: 17.048 |
| - type: recall_at_5 |
| value: 20.834 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/emotion |
| name: MTEB EmotionClassification |
| config: default |
| split: test |
| revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 |
| metrics: |
| - type: accuracy |
| value: 47.25999999999999 |
| - type: f1 |
| value: 43.83371155132253 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/fever |
| name: MTEB FEVER |
| config: default |
| split: test |
| revision: bea83ef9e8fb933d90a2f1d5515737465d613e12 |
| metrics: |
| - type: map_at_1 |
| value: 73.68900000000001 |
| - type: map_at_10 |
| value: 82.878 |
| - type: map_at_100 |
| value: 83.084 |
| - type: map_at_1000 |
| value: 83.097 |
| - type: map_at_3 |
| value: 81.528 |
| - type: map_at_5 |
| value: 82.432 |
| - type: mrr_at_1 |
| value: 79.49300000000001 |
| - type: mrr_at_10 |
| value: 87.24300000000001 |
| - type: mrr_at_100 |
| value: 87.3 |
| - type: mrr_at_1000 |
| value: 87.301 |
| - type: mrr_at_3 |
| value: 86.359 |
| - type: mrr_at_5 |
| value: 87.01 |
| - type: ndcg_at_1 |
| value: 79.49300000000001 |
| - type: ndcg_at_10 |
| value: 86.894 |
| - type: ndcg_at_100 |
| value: 87.6 |
| - type: ndcg_at_1000 |
| value: 87.79299999999999 |
| - type: ndcg_at_3 |
| value: 84.777 |
| - type: ndcg_at_5 |
| value: 86.08 |
| - type: precision_at_1 |
| value: 79.49300000000001 |
| - type: precision_at_10 |
| value: 10.578 |
| - type: precision_at_100 |
| value: 1.117 |
| - type: precision_at_1000 |
| value: 0.11499999999999999 |
| - type: precision_at_3 |
| value: 32.592999999999996 |
| - type: precision_at_5 |
| value: 20.423 |
| - type: recall_at_1 |
| value: 73.68900000000001 |
| - type: recall_at_10 |
| value: 94.833 |
| - type: recall_at_100 |
| value: 97.554 |
| - type: recall_at_1000 |
| value: 98.672 |
| - type: recall_at_3 |
| value: 89.236 |
| - type: recall_at_5 |
| value: 92.461 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/fiqa |
| name: MTEB FiQA2018 |
| config: default |
| split: test |
| revision: 27a168819829fe9bcd655c2df245fb19452e8e06 |
| metrics: |
| - type: map_at_1 |
| value: 20.59 |
| - type: map_at_10 |
| value: 34.089000000000006 |
| - type: map_at_100 |
| value: 35.796 |
| - type: map_at_1000 |
| value: 35.988 |
| - type: map_at_3 |
| value: 29.877 |
| - type: map_at_5 |
| value: 32.202999999999996 |
| - type: mrr_at_1 |
| value: 41.049 |
| - type: mrr_at_10 |
| value: 50.370000000000005 |
| - type: mrr_at_100 |
| value: 51.209 |
| - type: mrr_at_1000 |
| value: 51.247 |
| - type: mrr_at_3 |
| value: 48.122 |
| - type: mrr_at_5 |
| value: 49.326 |
| - type: ndcg_at_1 |
| value: 41.049 |
| - type: ndcg_at_10 |
| value: 42.163000000000004 |
| - type: ndcg_at_100 |
| value: 48.638999999999996 |
| - type: ndcg_at_1000 |
| value: 51.775000000000006 |
| - type: ndcg_at_3 |
| value: 38.435 |
| - type: ndcg_at_5 |
| value: 39.561 |
| - type: precision_at_1 |
| value: 41.049 |
| - type: precision_at_10 |
| value: 11.481 |
| - type: precision_at_100 |
| value: 1.8239999999999998 |
| - type: precision_at_1000 |
| value: 0.24 |
| - type: precision_at_3 |
| value: 25.257 |
| - type: precision_at_5 |
| value: 18.519 |
| - type: recall_at_1 |
| value: 20.59 |
| - type: recall_at_10 |
| value: 49.547999999999995 |
| - type: recall_at_100 |
| value: 73.676 |
| - type: recall_at_1000 |
| value: 92.269 |
| - type: recall_at_3 |
| value: 35.656 |
| - type: recall_at_5 |
| value: 41.455 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/hotpotqa |
| name: MTEB HotpotQA |
| config: default |
| split: test |
| revision: ab518f4d6fcca38d87c25209f94beba119d02014 |
| metrics: |
| - type: map_at_1 |
| value: 39.932 |
| - type: map_at_10 |
| value: 64.184 |
| - type: map_at_100 |
| value: 65.06 |
| - type: map_at_1000 |
| value: 65.109 |
| - type: map_at_3 |
| value: 60.27 |
| - type: map_at_5 |
| value: 62.732 |
| - type: mrr_at_1 |
| value: 79.865 |
| - type: mrr_at_10 |
| value: 85.99799999999999 |
| - type: mrr_at_100 |
| value: 86.13 |
| - type: mrr_at_1000 |
| value: 86.13300000000001 |
| - type: mrr_at_3 |
| value: 85.136 |
| - type: mrr_at_5 |
| value: 85.69200000000001 |
| - type: ndcg_at_1 |
| value: 79.865 |
| - type: ndcg_at_10 |
| value: 72.756 |
| - type: ndcg_at_100 |
| value: 75.638 |
| - type: ndcg_at_1000 |
| value: 76.589 |
| - type: ndcg_at_3 |
| value: 67.38199999999999 |
| - type: ndcg_at_5 |
| value: 70.402 |
| - type: precision_at_1 |
| value: 79.865 |
| - type: precision_at_10 |
| value: 15.387999999999998 |
| - type: precision_at_100 |
| value: 1.7610000000000001 |
| - type: precision_at_1000 |
| value: 0.189 |
| - type: precision_at_3 |
| value: 43.394 |
| - type: precision_at_5 |
| value: 28.424 |
| - type: recall_at_1 |
| value: 39.932 |
| - type: recall_at_10 |
| value: 76.941 |
| - type: recall_at_100 |
| value: 88.062 |
| - type: recall_at_1000 |
| value: 94.396 |
| - type: recall_at_3 |
| value: 65.091 |
| - type: recall_at_5 |
| value: 71.06 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/imdb |
| name: MTEB ImdbClassification |
| config: default |
| split: test |
| revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 |
| metrics: |
| - type: accuracy |
| value: 71.7904 |
| - type: ap |
| value: 65.82899456730257 |
| - type: f1 |
| value: 71.56611877410202 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/msmarco |
| name: MTEB MSMARCO |
| config: default |
| split: dev |
| revision: c5a29a104738b98a9e76336939199e264163d4a0 |
| metrics: |
| - type: map_at_1 |
| value: 21.931 |
| - type: map_at_10 |
| value: 34.849999999999994 |
| - type: map_at_100 |
| value: 36.033 |
| - type: map_at_1000 |
| value: 36.08 |
| - type: map_at_3 |
| value: 30.842000000000002 |
| - type: map_at_5 |
| value: 33.229 |
| - type: mrr_at_1 |
| value: 22.55 |
| - type: mrr_at_10 |
| value: 35.436 |
| - type: mrr_at_100 |
| value: 36.563 |
| - type: mrr_at_1000 |
| value: 36.604 |
| - type: mrr_at_3 |
| value: 31.507 |
| - type: mrr_at_5 |
| value: 33.851 |
| - type: ndcg_at_1 |
| value: 22.55 |
| - type: ndcg_at_10 |
| value: 41.969 |
| - type: ndcg_at_100 |
| value: 47.576 |
| - type: ndcg_at_1000 |
| value: 48.731 |
| - type: ndcg_at_3 |
| value: 33.894000000000005 |
| - type: ndcg_at_5 |
| value: 38.133 |
| - type: precision_at_1 |
| value: 22.55 |
| - type: precision_at_10 |
| value: 6.660000000000001 |
| - type: precision_at_100 |
| value: 0.946 |
| - type: precision_at_1000 |
| value: 0.104 |
| - type: precision_at_3 |
| value: 14.532 |
| - type: precision_at_5 |
| value: 10.865 |
| - type: recall_at_1 |
| value: 21.931 |
| - type: recall_at_10 |
| value: 63.841 |
| - type: recall_at_100 |
| value: 89.47699999999999 |
| - type: recall_at_1000 |
| value: 98.259 |
| - type: recall_at_3 |
| value: 42.063 |
| - type: recall_at_5 |
| value: 52.21 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/mtop_domain |
| name: MTEB MTOPDomainClassification (en) |
| config: en |
| split: test |
| revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
| metrics: |
| - type: accuracy |
| value: 93.03921568627452 |
| - type: f1 |
| value: 92.56400672314416 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/mtop_intent |
| name: MTEB MTOPIntentClassification (en) |
| config: en |
| split: test |
| revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
| metrics: |
| - type: accuracy |
| value: 63.515731874145 |
| - type: f1 |
| value: 44.922310875523216 |
| - task: |
| type: Classification |
| dataset: |
| type: masakhane/masakhanews |
| name: MTEB MasakhaNEWSClassification (eng) |
| config: eng |
| split: test |
| revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60 |
| metrics: |
| - type: accuracy |
| value: 77.57383966244727 |
| - type: f1 |
| value: 76.55222378218293 |
| - task: |
| type: Clustering |
| dataset: |
| type: masakhane/masakhanews |
| name: MTEB MasakhaNEWSClusteringP2P (eng) |
| config: eng |
| split: test |
| revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60 |
| metrics: |
| - type: v_measure |
| value: 62.74836240280833 |
| - task: |
| type: Clustering |
| dataset: |
| type: masakhane/masakhanews |
| name: MTEB MasakhaNEWSClusteringS2S (eng) |
| config: eng |
| split: test |
| revision: 8ccc72e69e65f40c70e117d8b3c08306bb788b60 |
| metrics: |
| - type: v_measure |
| value: 24.414348715238184 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (en) |
| config: en |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
| - type: accuracy |
| value: 66.54673839946201 |
| - type: f1 |
| value: 64.61004101532164 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_scenario |
| name: MTEB MassiveScenarioClassification (en) |
| config: en |
| split: test |
| revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| metrics: |
| - type: accuracy |
| value: 73.11365164761264 |
| - type: f1 |
| value: 72.01684013680978 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/medrxiv-clustering-p2p |
| name: MTEB MedrxivClusteringP2P |
| config: default |
| split: test |
| revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 |
| metrics: |
| - type: v_measure |
| value: 31.123671999617297 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/medrxiv-clustering-s2s |
| name: MTEB MedrxivClusteringS2S |
| config: default |
| split: test |
| revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 |
| metrics: |
| - type: v_measure |
| value: 26.72684341430875 |
| - task: |
| type: Reranking |
| dataset: |
| type: mteb/mind_small |
| name: MTEB MindSmallReranking |
| config: default |
| split: test |
| revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 |
| metrics: |
| - type: map |
| value: 29.910228061734816 |
| - type: mrr |
| value: 30.835255982532477 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/nfcorpus |
| name: MTEB NFCorpus |
| config: default |
| split: test |
| revision: ec0fa4fe99da2ff19ca1214b7966684033a58814 |
| metrics: |
| - type: map_at_1 |
| value: 5.6770000000000005 |
| - type: map_at_10 |
| value: 13.15 |
| - type: map_at_100 |
| value: 16.205 |
| - type: map_at_1000 |
| value: 17.580000000000002 |
| - type: map_at_3 |
| value: 9.651 |
| - type: map_at_5 |
| value: 11.142000000000001 |
| - type: mrr_at_1 |
| value: 47.678 |
| - type: mrr_at_10 |
| value: 56.257000000000005 |
| - type: mrr_at_100 |
| value: 56.708000000000006 |
| - type: mrr_at_1000 |
| value: 56.751 |
| - type: mrr_at_3 |
| value: 54.128 |
| - type: mrr_at_5 |
| value: 55.181000000000004 |
| - type: ndcg_at_1 |
| value: 45.511 |
| - type: ndcg_at_10 |
| value: 35.867 |
| - type: ndcg_at_100 |
| value: 31.566 |
| - type: ndcg_at_1000 |
| value: 40.077 |
| - type: ndcg_at_3 |
| value: 41.9 |
| - type: ndcg_at_5 |
| value: 39.367999999999995 |
| - type: precision_at_1 |
| value: 47.678 |
| - type: precision_at_10 |
| value: 26.842 |
| - type: precision_at_100 |
| value: 7.991 |
| - type: precision_at_1000 |
| value: 2.0469999999999997 |
| - type: precision_at_3 |
| value: 39.938 |
| - type: precision_at_5 |
| value: 34.613 |
| - type: recall_at_1 |
| value: 5.6770000000000005 |
| - type: recall_at_10 |
| value: 17.119999999999997 |
| - type: recall_at_100 |
| value: 30.828 |
| - type: recall_at_1000 |
| value: 62.082 |
| - type: recall_at_3 |
| value: 10.456 |
| - type: recall_at_5 |
| value: 12.903999999999998 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/nq |
| name: MTEB NQ |
| config: default |
| split: test |
| revision: b774495ed302d8c44a3a7ea25c90dbce03968f31 |
| metrics: |
| - type: map_at_1 |
| value: 39.021 |
| - type: map_at_10 |
| value: 54.976 |
| - type: map_at_100 |
| value: 55.793000000000006 |
| - type: map_at_1000 |
| value: 55.811 |
| - type: map_at_3 |
| value: 50.759 |
| - type: map_at_5 |
| value: 53.429 |
| - type: mrr_at_1 |
| value: 43.308 |
| - type: mrr_at_10 |
| value: 57.118 |
| - type: mrr_at_100 |
| value: 57.69499999999999 |
| - type: mrr_at_1000 |
| value: 57.704 |
| - type: mrr_at_3 |
| value: 53.848 |
| - type: mrr_at_5 |
| value: 55.915000000000006 |
| - type: ndcg_at_1 |
| value: 43.308 |
| - type: ndcg_at_10 |
| value: 62.33800000000001 |
| - type: ndcg_at_100 |
| value: 65.61099999999999 |
| - type: ndcg_at_1000 |
| value: 65.995 |
| - type: ndcg_at_3 |
| value: 54.723 |
| - type: ndcg_at_5 |
| value: 59.026 |
| - type: precision_at_1 |
| value: 43.308 |
| - type: precision_at_10 |
| value: 9.803 |
| - type: precision_at_100 |
| value: 1.167 |
| - type: precision_at_1000 |
| value: 0.121 |
| - type: precision_at_3 |
| value: 24.334 |
| - type: precision_at_5 |
| value: 17.144000000000002 |
| - type: recall_at_1 |
| value: 39.021 |
| - type: recall_at_10 |
| value: 82.37299999999999 |
| - type: recall_at_100 |
| value: 96.21499999999999 |
| - type: recall_at_1000 |
| value: 99.02499999999999 |
| - type: recall_at_3 |
| value: 63.031000000000006 |
| - type: recall_at_5 |
| value: 72.856 |
| - task: |
| type: Classification |
| dataset: |
| type: ag_news |
| name: MTEB NewsClassification |
| config: default |
| split: test |
| revision: eb185aade064a813bc0b7f42de02595523103ca4 |
| metrics: |
| - type: accuracy |
| value: 78.03289473684211 |
| - type: f1 |
| value: 77.89323745730803 |
| - 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: 100.0 |
| - type: cos_sim_f1 |
| value: 99.9490575649516 |
| - type: cos_sim_precision |
| value: 100.0 |
| - type: cos_sim_recall |
| value: 99.89816700610999 |
| - type: dot_accuracy |
| value: 99.89816700610999 |
| - type: dot_ap |
| value: 100.0 |
| - type: dot_f1 |
| value: 99.9490575649516 |
| - type: dot_precision |
| value: 100.0 |
| - type: dot_recall |
| value: 99.89816700610999 |
| - type: euclidean_accuracy |
| value: 99.89816700610999 |
| - type: euclidean_ap |
| value: 100.0 |
| - type: euclidean_f1 |
| value: 99.9490575649516 |
| - type: euclidean_precision |
| value: 100.0 |
| - type: euclidean_recall |
| value: 99.89816700610999 |
| - type: manhattan_accuracy |
| value: 99.89816700610999 |
| - type: manhattan_ap |
| value: 100.0 |
| - type: manhattan_f1 |
| value: 99.9490575649516 |
| - type: manhattan_precision |
| value: 100.0 |
| - type: manhattan_recall |
| value: 99.89816700610999 |
| - type: max_accuracy |
| value: 99.89816700610999 |
| - type: max_ap |
| value: 100.0 |
| - 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: 59.578879568280385 |
| - type: cos_sim_f1 |
| value: 62.50861474844934 |
| - type: cos_sim_precision |
| value: 45.46365914786967 |
| - type: cos_sim_recall |
| value: 100.0 |
| - type: dot_accuracy |
| value: 61.75000000000001 |
| - type: dot_ap |
| value: 59.57893088951573 |
| - type: dot_f1 |
| value: 62.50861474844934 |
| - type: dot_precision |
| value: 45.46365914786967 |
| - type: dot_recall |
| value: 100.0 |
| - type: euclidean_accuracy |
| value: 61.75000000000001 |
| - type: euclidean_ap |
| value: 59.578755624671686 |
| - type: euclidean_f1 |
| value: 62.50861474844934 |
| - type: euclidean_precision |
| value: 45.46365914786967 |
| - type: euclidean_recall |
| value: 100.0 |
| - type: manhattan_accuracy |
| value: 61.75000000000001 |
| - type: manhattan_ap |
| value: 59.58504334461159 |
| - type: manhattan_f1 |
| value: 62.50861474844934 |
| - type: manhattan_precision |
| value: 45.46365914786967 |
| - type: manhattan_recall |
| value: 100.0 |
| - type: max_accuracy |
| value: 61.75000000000001 |
| - type: max_ap |
| value: 59.58504334461159 |
| - type: max_f1 |
| value: 62.50861474844934 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/quora |
| name: MTEB QuoraRetrieval |
| config: default |
| split: test |
| revision: e4e08e0b7dbe3c8700f0daef558ff32256715259 |
| metrics: |
| - type: map_at_1 |
| value: 70.186 |
| - type: map_at_10 |
| value: 83.875 |
| - type: map_at_100 |
| value: 84.514 |
| - type: map_at_1000 |
| value: 84.53500000000001 |
| - type: map_at_3 |
| value: 80.926 |
| - type: map_at_5 |
| value: 82.797 |
| - type: mrr_at_1 |
| value: 80.82000000000001 |
| - type: mrr_at_10 |
| value: 87.068 |
| - type: mrr_at_100 |
| value: 87.178 |
| - type: mrr_at_1000 |
| value: 87.18 |
| - type: mrr_at_3 |
| value: 86.055 |
| - type: mrr_at_5 |
| value: 86.763 |
| - type: ndcg_at_1 |
| value: 80.84 |
| - type: ndcg_at_10 |
| value: 87.723 |
| - type: ndcg_at_100 |
| value: 88.98700000000001 |
| - type: ndcg_at_1000 |
| value: 89.13499999999999 |
| - type: ndcg_at_3 |
| value: 84.821 |
| - type: ndcg_at_5 |
| value: 86.441 |
| - type: precision_at_1 |
| value: 80.84 |
| - type: precision_at_10 |
| value: 13.270000000000001 |
| - type: precision_at_100 |
| value: 1.516 |
| - type: precision_at_1000 |
| value: 0.156 |
| - type: precision_at_3 |
| value: 37.013 |
| - type: precision_at_5 |
| value: 24.37 |
| - type: recall_at_1 |
| value: 70.186 |
| - type: recall_at_10 |
| value: 94.948 |
| - type: recall_at_100 |
| value: 99.223 |
| - type: recall_at_1000 |
| value: 99.932 |
| - type: recall_at_3 |
| value: 86.57000000000001 |
| - type: recall_at_5 |
| value: 91.157 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/reddit-clustering |
| name: MTEB RedditClustering |
| config: default |
| split: test |
| revision: 24640382cdbf8abc73003fb0fa6d111a705499eb |
| metrics: |
| - type: v_measure |
| value: 50.24198927949519 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/reddit-clustering-p2p |
| name: MTEB RedditClusteringP2P |
| config: default |
| split: test |
| revision: 385e3cb46b4cfa89021f56c4380204149d0efe33 |
| metrics: |
| - type: v_measure |
| value: 61.452073078765544 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/scidocs |
| name: MTEB SCIDOCS |
| config: default |
| split: test |
| revision: f8c2fcf00f625baaa80f62ec5bd9e1fff3b8ae88 |
| metrics: |
| - type: map_at_1 |
| value: 4.972 |
| - type: map_at_10 |
| value: 12.314 |
| - type: map_at_100 |
| value: 14.333000000000002 |
| - type: map_at_1000 |
| value: 14.628 |
| - type: map_at_3 |
| value: 8.972 |
| - type: map_at_5 |
| value: 10.724 |
| - type: mrr_at_1 |
| value: 24.4 |
| - type: mrr_at_10 |
| value: 35.257 |
| - type: mrr_at_100 |
| value: 36.297000000000004 |
| - type: mrr_at_1000 |
| value: 36.363 |
| - type: mrr_at_3 |
| value: 32.267 |
| - type: mrr_at_5 |
| value: 33.942 |
| - type: ndcg_at_1 |
| value: 24.4 |
| - type: ndcg_at_10 |
| value: 20.47 |
| - type: ndcg_at_100 |
| value: 28.111000000000004 |
| - type: ndcg_at_1000 |
| value: 33.499 |
| - type: ndcg_at_3 |
| value: 19.975 |
| - type: ndcg_at_5 |
| value: 17.293 |
| - type: precision_at_1 |
| value: 24.4 |
| - type: precision_at_10 |
| value: 10.440000000000001 |
| - type: precision_at_100 |
| value: 2.136 |
| - type: precision_at_1000 |
| value: 0.34299999999999997 |
| - type: precision_at_3 |
| value: 18.733 |
| - type: precision_at_5 |
| value: 15.120000000000001 |
| - type: recall_at_1 |
| value: 4.972 |
| - type: recall_at_10 |
| value: 21.157 |
| - type: recall_at_100 |
| value: 43.335 |
| - type: recall_at_1000 |
| value: 69.652 |
| - type: recall_at_3 |
| value: 11.417 |
| - type: recall_at_5 |
| value: 15.317 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sickr-sts |
| name: MTEB SICK-R |
| config: default |
| split: test |
| revision: 20a6d6f312dd54037fe07a32d58e5e168867909d |
| metrics: |
| - type: cos_sim_pearson |
| value: 76.70295978506286 |
| - type: cos_sim_spearman |
| value: 70.91162732446628 |
| - type: euclidean_pearson |
| value: 73.25693688746031 |
| - type: euclidean_spearman |
| value: 70.91162556180127 |
| - type: manhattan_pearson |
| value: 73.27735004735767 |
| - type: manhattan_spearman |
| value: 70.8856787022704 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts12-sts |
| name: MTEB STS12 |
| config: default |
| split: test |
| revision: a0d554a64d88156834ff5ae9920b964011b16384 |
| metrics: |
| - type: cos_sim_pearson |
| value: 67.55878682646774 |
| - type: cos_sim_spearman |
| value: 66.10824660353681 |
| - type: euclidean_pearson |
| value: 64.93937270068541 |
| - type: euclidean_spearman |
| value: 66.10824660353681 |
| - type: manhattan_pearson |
| value: 64.96325555978984 |
| - type: manhattan_spearman |
| value: 66.12052481638577 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts13-sts |
| name: MTEB STS13 |
| config: default |
| split: test |
| revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca |
| metrics: |
| - type: cos_sim_pearson |
| value: 79.79979774019496 |
| - type: cos_sim_spearman |
| value: 79.82293444619499 |
| - type: euclidean_pearson |
| value: 79.4830436509311 |
| - type: euclidean_spearman |
| value: 79.82293444619499 |
| - type: manhattan_pearson |
| value: 79.49785594799296 |
| - type: manhattan_spearman |
| value: 79.8280390479434 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts14-sts |
| name: MTEB STS14 |
| config: default |
| split: test |
| revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 |
| metrics: |
| - type: cos_sim_pearson |
| value: 76.36839628231121 |
| - type: cos_sim_spearman |
| value: 73.63809739428072 |
| - type: euclidean_pearson |
| value: 74.93718121215906 |
| - type: euclidean_spearman |
| value: 73.63810227650436 |
| - type: manhattan_pearson |
| value: 74.8737197659424 |
| - type: manhattan_spearman |
| value: 73.57534688126572 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts15-sts |
| name: MTEB STS15 |
| config: default |
| split: test |
| revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 |
| metrics: |
| - type: cos_sim_pearson |
| value: 82.67482138157656 |
| - type: cos_sim_spearman |
| value: 83.23485786963107 |
| - type: euclidean_pearson |
| value: 82.50847772197369 |
| - type: euclidean_spearman |
| value: 83.23485786963107 |
| - type: manhattan_pearson |
| value: 82.48916218377576 |
| - type: manhattan_spearman |
| value: 83.19756483500014 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts16-sts |
| name: MTEB STS16 |
| config: default |
| split: test |
| revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 |
| metrics: |
| - type: cos_sim_pearson |
| value: 81.11626268793967 |
| - type: cos_sim_spearman |
| value: 81.58184691061507 |
| - type: euclidean_pearson |
| value: 80.65900869004938 |
| - type: euclidean_spearman |
| value: 81.58184691061507 |
| - type: manhattan_pearson |
| value: 80.67912306966772 |
| - type: manhattan_spearman |
| value: 81.59957593393145 |
| - 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: 80.3140990821409 |
| - type: cos_sim_spearman |
| value: 80.59196586367551 |
| - type: euclidean_pearson |
| value: 80.73014029317672 |
| - type: euclidean_spearman |
| value: 80.59196586367551 |
| - type: manhattan_pearson |
| value: 80.5774325136987 |
| - type: manhattan_spearman |
| value: 80.35102610546238 |
| - 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: 68.34450491529164 |
| - type: cos_sim_spearman |
| value: 68.79451793414492 |
| - type: euclidean_pearson |
| value: 68.75619738499324 |
| - type: euclidean_spearman |
| value: 68.79451793414492 |
| - type: manhattan_pearson |
| value: 68.75256119543882 |
| - type: manhattan_spearman |
| value: 68.81836416978547 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/stsbenchmark-sts |
| name: MTEB STSBenchmark |
| config: default |
| split: test |
| revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 |
| metrics: |
| - type: cos_sim_pearson |
| value: 77.95580414975612 |
| - type: cos_sim_spearman |
| value: 77.89671867168987 |
| - type: euclidean_pearson |
| value: 77.61352097720862 |
| - type: euclidean_spearman |
| value: 77.89671867168987 |
| - type: manhattan_pearson |
| value: 77.65282228135632 |
| - type: manhattan_spearman |
| value: 77.91730533156762 |
| - 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: 77.95580421496413 |
| - type: cos_sim_spearman |
| value: 77.89671867168987 |
| - type: euclidean_pearson |
| value: 77.61352107168794 |
| - type: euclidean_spearman |
| value: 77.89671867168987 |
| - type: manhattan_pearson |
| value: 77.65282237231794 |
| - type: manhattan_spearman |
| value: 77.91730533156762 |
| - task: |
| type: Reranking |
| dataset: |
| type: mteb/scidocs-reranking |
| name: MTEB SciDocsRR |
| config: default |
| split: test |
| revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab |
| metrics: |
| - type: map |
| value: 79.22928110092924 |
| - type: mrr |
| value: 94.46700902583257 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/scifact |
| name: MTEB SciFact |
| config: default |
| split: test |
| revision: 0228b52cf27578f30900b9e5271d331663a030d7 |
| metrics: |
| - type: map_at_1 |
| value: 56.011 |
| - type: map_at_10 |
| value: 65.544 |
| - type: map_at_100 |
| value: 66.034 |
| - type: map_at_1000 |
| value: 66.065 |
| - type: map_at_3 |
| value: 63.077000000000005 |
| - type: map_at_5 |
| value: 64.354 |
| - type: mrr_at_1 |
| value: 59.0 |
| - type: mrr_at_10 |
| value: 66.74900000000001 |
| - type: mrr_at_100 |
| value: 67.176 |
| - type: mrr_at_1000 |
| value: 67.203 |
| - type: mrr_at_3 |
| value: 65.056 |
| - type: mrr_at_5 |
| value: 65.956 |
| - type: ndcg_at_1 |
| value: 59.0 |
| - type: ndcg_at_10 |
| value: 69.95599999999999 |
| - type: ndcg_at_100 |
| value: 72.27 |
| - type: ndcg_at_1000 |
| value: 73.066 |
| - type: ndcg_at_3 |
| value: 65.837 |
| - type: ndcg_at_5 |
| value: 67.633 |
| - type: precision_at_1 |
| value: 59.0 |
| - type: precision_at_10 |
| value: 9.333 |
| - type: precision_at_100 |
| value: 1.053 |
| - type: precision_at_1000 |
| value: 0.11199999999999999 |
| - type: precision_at_3 |
| value: 26.0 |
| - type: precision_at_5 |
| value: 16.866999999999997 |
| - type: recall_at_1 |
| value: 56.011 |
| - type: recall_at_10 |
| value: 82.133 |
| - type: recall_at_100 |
| value: 92.767 |
| - type: recall_at_1000 |
| value: 99.0 |
| - type: recall_at_3 |
| value: 70.95 |
| - type: recall_at_5 |
| value: 75.556 |
| - task: |
| type: PairClassification |
| dataset: |
| type: mteb/sprintduplicatequestions-pairclassification |
| name: MTEB SprintDuplicateQuestions |
| config: default |
| split: test |
| revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 |
| metrics: |
| - type: cos_sim_accuracy |
| value: 99.81584158415842 |
| - type: cos_sim_ap |
| value: 94.67482871230736 |
| - type: cos_sim_f1 |
| value: 90.67201604814443 |
| - type: cos_sim_precision |
| value: 90.94567404426559 |
| - type: cos_sim_recall |
| value: 90.4 |
| - type: dot_accuracy |
| value: 99.81584158415842 |
| - type: dot_ap |
| value: 94.67482871230737 |
| - type: dot_f1 |
| value: 90.67201604814443 |
| - type: dot_precision |
| value: 90.94567404426559 |
| - type: dot_recall |
| value: 90.4 |
| - type: euclidean_accuracy |
| value: 99.81584158415842 |
| - type: euclidean_ap |
| value: 94.67482871230737 |
| - type: euclidean_f1 |
| value: 90.67201604814443 |
| - type: euclidean_precision |
| value: 90.94567404426559 |
| - type: euclidean_recall |
| value: 90.4 |
| - type: manhattan_accuracy |
| value: 99.81188118811882 |
| - type: manhattan_ap |
| value: 94.6409082219286 |
| - type: manhattan_f1 |
| value: 90.50949050949052 |
| - type: manhattan_precision |
| value: 90.41916167664671 |
| - type: manhattan_recall |
| value: 90.60000000000001 |
| - type: max_accuracy |
| value: 99.81584158415842 |
| - type: max_ap |
| value: 94.67482871230737 |
| - type: max_f1 |
| value: 90.67201604814443 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/stackexchange-clustering |
| name: MTEB StackExchangeClustering |
| config: default |
| split: test |
| revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 |
| metrics: |
| - type: v_measure |
| value: 62.63494511649264 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/stackexchange-clustering-p2p |
| name: MTEB StackExchangeClusteringP2P |
| config: default |
| split: test |
| revision: 815ca46b2622cec33ccafc3735d572c266efdb44 |
| metrics: |
| - type: v_measure |
| value: 37.165838327685755 |
| - task: |
| type: Reranking |
| dataset: |
| type: mteb/stackoverflowdupquestions-reranking |
| name: MTEB StackOverflowDupQuestions |
| config: default |
| split: test |
| revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 |
| metrics: |
| - type: map |
| value: 51.384873075208084 |
| - type: mrr |
| value: 52.196439181733304 |
| - task: |
| type: Summarization |
| dataset: |
| type: mteb/summeval |
| name: MTEB SummEval |
| config: default |
| split: test |
| revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c |
| metrics: |
| - type: cos_sim_pearson |
| value: 32.13690355567596 |
| - type: cos_sim_spearman |
| value: 31.38349778638125 |
| - type: dot_pearson |
| value: 32.13689596691593 |
| - type: dot_spearman |
| value: 31.38349778638125 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/trec-covid |
| name: MTEB TRECCOVID |
| config: default |
| split: test |
| revision: bb9466bac8153a0349341eb1b22e06409e78ef4e |
| metrics: |
| - type: map_at_1 |
| value: 0.26 |
| - type: map_at_10 |
| value: 2.08 |
| - type: map_at_100 |
| value: 12.598 |
| - type: map_at_1000 |
| value: 30.119 |
| - type: map_at_3 |
| value: 0.701 |
| - type: map_at_5 |
| value: 1.11 |
| - type: mrr_at_1 |
| value: 96.0 |
| - type: mrr_at_10 |
| value: 97.167 |
| - type: mrr_at_100 |
| value: 97.167 |
| - type: mrr_at_1000 |
| value: 97.167 |
| - type: mrr_at_3 |
| value: 96.667 |
| - type: mrr_at_5 |
| value: 97.167 |
| - type: ndcg_at_1 |
| value: 91.0 |
| - type: ndcg_at_10 |
| value: 81.69800000000001 |
| - type: ndcg_at_100 |
| value: 62.9 |
| - type: ndcg_at_1000 |
| value: 55.245999999999995 |
| - type: ndcg_at_3 |
| value: 86.397 |
| - type: ndcg_at_5 |
| value: 84.286 |
| - type: precision_at_1 |
| value: 96.0 |
| - type: precision_at_10 |
| value: 87.0 |
| - type: precision_at_100 |
| value: 64.86 |
| - type: precision_at_1000 |
| value: 24.512 |
| - type: precision_at_3 |
| value: 90.667 |
| - type: precision_at_5 |
| value: 88.8 |
| - type: recall_at_1 |
| value: 0.26 |
| - type: recall_at_10 |
| value: 2.238 |
| - type: recall_at_100 |
| value: 15.488 |
| - type: recall_at_1000 |
| value: 51.6 |
| - type: recall_at_3 |
| value: 0.716 |
| - type: recall_at_5 |
| value: 1.151 |
| - task: |
| type: Retrieval |
| dataset: |
| type: mteb/touche2020 |
| name: MTEB Touche2020 |
| config: default |
| split: test |
| revision: a34f9a33db75fa0cbb21bb5cfc3dae8dc8bec93f |
| metrics: |
| - type: map_at_1 |
| value: 3.376 |
| - type: map_at_10 |
| value: 13.142000000000001 |
| - type: map_at_100 |
| value: 19.763 |
| - type: map_at_1000 |
| value: 21.319 |
| - type: map_at_3 |
| value: 6.805999999999999 |
| - type: map_at_5 |
| value: 8.952 |
| - type: mrr_at_1 |
| value: 46.939 |
| - type: mrr_at_10 |
| value: 61.082 |
| - type: mrr_at_100 |
| value: 61.45 |
| - type: mrr_at_1000 |
| value: 61.468999999999994 |
| - type: mrr_at_3 |
| value: 57.483 |
| - type: mrr_at_5 |
| value: 59.931999999999995 |
| - type: ndcg_at_1 |
| value: 44.897999999999996 |
| - type: ndcg_at_10 |
| value: 32.35 |
| - type: ndcg_at_100 |
| value: 42.719 |
| - type: ndcg_at_1000 |
| value: 53.30200000000001 |
| - type: ndcg_at_3 |
| value: 37.724999999999994 |
| - type: ndcg_at_5 |
| value: 34.79 |
| - type: precision_at_1 |
| value: 46.939 |
| - type: precision_at_10 |
| value: 28.366999999999997 |
| - type: precision_at_100 |
| value: 8.429 |
| - type: precision_at_1000 |
| value: 1.557 |
| - type: precision_at_3 |
| value: 38.095 |
| - type: precision_at_5 |
| value: 33.469 |
| - type: recall_at_1 |
| value: 3.376 |
| - type: recall_at_10 |
| value: 20.164 |
| - type: recall_at_100 |
| value: 50.668 |
| - type: recall_at_1000 |
| value: 83.159 |
| - type: recall_at_3 |
| value: 8.155 |
| - type: recall_at_5 |
| value: 11.872 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/toxic_conversations_50k |
| name: MTEB ToxicConversationsClassification |
| config: default |
| split: test |
| revision: edfaf9da55d3dd50d43143d90c1ac476895ae6de |
| metrics: |
| - type: accuracy |
| value: 66.739 |
| - type: ap |
| value: 12.17931839228834 |
| - type: f1 |
| value: 51.05383188624636 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/tweet_sentiment_extraction |
| name: MTEB TweetSentimentExtractionClassification |
| config: default |
| split: test |
| revision: d604517c81ca91fe16a244d1248fc021f9ecee7a |
| metrics: |
| - type: accuracy |
| value: 56.72891907187323 |
| - type: f1 |
| value: 56.997614557150946 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/twentynewsgroups-clustering |
| name: MTEB TwentyNewsgroupsClustering |
| config: default |
| split: test |
| revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 |
| metrics: |
| - type: v_measure |
| value: 39.825318429345224 |
| - task: |
| type: PairClassification |
| dataset: |
| type: mteb/twittersemeval2015-pairclassification |
| name: MTEB TwitterSemEval2015 |
| config: default |
| split: test |
| revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 |
| metrics: |
| - type: cos_sim_accuracy |
| value: 83.65619598259522 |
| - type: cos_sim_ap |
| value: 66.17412885183877 |
| - type: cos_sim_f1 |
| value: 63.09125656951745 |
| - type: cos_sim_precision |
| value: 57.63858577040594 |
| - type: cos_sim_recall |
| value: 69.68337730870712 |
| - type: dot_accuracy |
| value: 83.65619598259522 |
| - type: dot_ap |
| value: 66.17413621964548 |
| - type: dot_f1 |
| value: 63.09125656951745 |
| - type: dot_precision |
| value: 57.63858577040594 |
| - type: dot_recall |
| value: 69.68337730870712 |
| - type: euclidean_accuracy |
| value: 83.65619598259522 |
| - type: euclidean_ap |
| value: 66.17412836413126 |
| - type: euclidean_f1 |
| value: 63.09125656951745 |
| - type: euclidean_precision |
| value: 57.63858577040594 |
| - type: euclidean_recall |
| value: 69.68337730870712 |
| - type: manhattan_accuracy |
| value: 83.5548667819038 |
| - type: manhattan_ap |
| value: 66.07998834521334 |
| - type: manhattan_f1 |
| value: 62.96433419721092 |
| - type: manhattan_precision |
| value: 59.14676559239509 |
| - type: manhattan_recall |
| value: 67.30870712401055 |
| - type: max_accuracy |
| value: 83.65619598259522 |
| - type: max_ap |
| value: 66.17413621964548 |
| - type: max_f1 |
| value: 63.09125656951745 |
| - task: |
| type: PairClassification |
| dataset: |
| type: mteb/twitterurlcorpus-pairclassification |
| name: MTEB TwitterURLCorpus |
| config: default |
| split: test |
| revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf |
| metrics: |
| - type: cos_sim_accuracy |
| value: 88.55706911941631 |
| - type: cos_sim_ap |
| value: 85.20971331546805 |
| - type: cos_sim_f1 |
| value: 77.28446050593702 |
| - type: cos_sim_precision |
| value: 74.16135881104033 |
| - type: cos_sim_recall |
| value: 80.6821681552202 |
| - type: dot_accuracy |
| value: 88.55706911941631 |
| - type: dot_ap |
| value: 85.2097154112633 |
| - type: dot_f1 |
| value: 77.28446050593702 |
| - type: dot_precision |
| value: 74.16135881104033 |
| - type: dot_recall |
| value: 80.6821681552202 |
| - type: euclidean_accuracy |
| value: 88.55706911941631 |
| - type: euclidean_ap |
| value: 85.20971719214488 |
| - type: euclidean_f1 |
| value: 77.28446050593702 |
| - type: euclidean_precision |
| value: 74.16135881104033 |
| - type: euclidean_recall |
| value: 80.6821681552202 |
| - type: manhattan_accuracy |
| value: 88.52020025614158 |
| - type: manhattan_ap |
| value: 85.17569799117058 |
| - type: manhattan_f1 |
| value: 77.27157773040933 |
| - type: manhattan_precision |
| value: 72.79286638077734 |
| - type: manhattan_recall |
| value: 82.33754234678165 |
| - type: max_accuracy |
| value: 88.55706911941631 |
| - type: max_ap |
| value: 85.20971719214488 |
| - type: max_f1 |
| value: 77.28446050593702 |
| - task: |
| type: Clustering |
| dataset: |
| type: jinaai/cities_wiki_clustering |
| name: MTEB WikiCitiesClustering |
| config: default |
| split: test |
| revision: ddc9ee9242fa65332597f70e967ecc38b9d734fa |
| metrics: |
| - type: v_measure |
| value: 85.63474850264893 |
| --- |
| <h1 align="center">Snowflake's Arctic-embed-m-long</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-m-long", trust_remote_code=True) |
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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? |
| 0.46484852 The Data Cloud! |
| 0.3758855 Mexico City of Course! |
| Query: Where can I get the best tacos? |
| 0.42407742 Mexico City of Course! |
| 0.36740506 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-m-long') |
| model = AutoModel.from_pretrained('Snowflake/snowflake-arctic-embed-m-long', trust_remote_code=True, add_pooling_layer=False, safe_serialization=True) |
| 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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| If you use the long context model with more than 2048 tokens, ensure that you initialize the model like below instead. This will use [RPE](https://arxiv.org/abs/2104.09864) to allow up to 8192 tokens. |
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| ``` py |
| model = AutoModel.from_pretrained('Snowflake/snowflake-arctic-embed-m-long', trust_remote_code=True, safe_serialization=True, rotary_scaling_factor=2) |
| ``` |
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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-m-long', { |
| 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); // [0.36740492125676116, 0.42407774292046635] |
| ``` |
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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=f528b5b4-2ba6-4fc6-8eed-259968d45577" /> |
