--- tags: - mteb - sentence-transformers - feature-extraction - sentence-similarity - transformers language: en license: mit model-index: - name: ember_v1 results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 76.05970149253731 - type: ap value: 38.76045348512767 - type: f1 value: 69.8824007294685 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 91.977 - type: ap value: 88.63507587170176 - type: f1 value: 91.9524133311038 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 47.938 - type: f1 value: 47.58273047536129 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 41.252 - type: map_at_10 value: 56.567 - type: map_at_100 value: 57.07600000000001 - type: map_at_1000 value: 57.08 - type: map_at_3 value: 52.394 - type: map_at_5 value: 55.055 - type: mrr_at_1 value: 42.39 - type: mrr_at_10 value: 57.001999999999995 - type: mrr_at_100 value: 57.531 - type: mrr_at_1000 value: 57.535000000000004 - type: mrr_at_3 value: 52.845 - type: mrr_at_5 value: 55.47299999999999 - type: ndcg_at_1 value: 41.252 - type: ndcg_at_10 value: 64.563 - type: ndcg_at_100 value: 66.667 - type: ndcg_at_1000 value: 66.77 - type: ndcg_at_3 value: 56.120000000000005 - type: ndcg_at_5 value: 60.889 - type: precision_at_1 value: 41.252 - type: precision_at_10 value: 8.982999999999999 - type: precision_at_100 value: 0.989 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 22.309 - type: precision_at_5 value: 15.690000000000001 - type: recall_at_1 value: 41.252 - type: recall_at_10 value: 89.82900000000001 - type: recall_at_100 value: 98.86200000000001 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 66.927 - type: recall_at_5 value: 78.45 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 48.5799968717232 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 43.142844164856136 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 64.45997990276463 - type: mrr value: 77.85560392208592 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 86.38299310075898 - type: cos_sim_spearman value: 85.81038898286454 - type: euclidean_pearson value: 84.28002556389774 - type: euclidean_spearman value: 85.80315990248238 - type: manhattan_pearson value: 83.9755390675032 - type: manhattan_spearman value: 85.30435335611396 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 87.89935064935065 - type: f1 value: 87.87886687103833 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 38.84335510371379 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 36.377963093857005 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 32.557 - type: map_at_10 value: 44.501000000000005 - type: map_at_100 value: 46.11 - type: map_at_1000 value: 46.232 - type: map_at_3 value: 40.711000000000006 - type: map_at_5 value: 42.937 - type: mrr_at_1 value: 40.916000000000004 - type: mrr_at_10 value: 51.317 - type: mrr_at_100 value: 52.003 - type: mrr_at_1000 value: 52.044999999999995 - type: mrr_at_3 value: 48.569 - type: mrr_at_5 value: 50.322 - type: ndcg_at_1 value: 40.916000000000004 - type: ndcg_at_10 value: 51.353 - type: ndcg_at_100 value: 56.762 - type: ndcg_at_1000 value: 58.555 - type: ndcg_at_3 value: 46.064 - type: ndcg_at_5 value: 48.677 - type: precision_at_1 value: 40.916000000000004 - type: precision_at_10 value: 9.927999999999999 - type: precision_at_100 value: 1.592 - type: precision_at_1000 value: 0.20600000000000002 - type: precision_at_3 value: 22.078999999999997 - type: precision_at_5 value: 16.08 - type: recall_at_1 value: 32.557 - type: recall_at_10 value: 63.942 - type: recall_at_100 value: 86.436 - type: recall_at_1000 value: 97.547 - type: recall_at_3 value: 48.367 - type: recall_at_5 value: 55.818 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 32.106 - type: map_at_10 value: 42.55 - type: map_at_100 value: 43.818 - type: map_at_1000 value: 43.952999999999996 - type: map_at_3 value: 39.421 - type: map_at_5 value: 41.276 - type: mrr_at_1 value: 39.936 - type: mrr_at_10 value: 48.484 - type: mrr_at_100 value: 49.123 - type: mrr_at_1000 value: 49.163000000000004 - type: mrr_at_3 value: 46.221000000000004 - type: mrr_at_5 value: 47.603 - type: ndcg_at_1 value: 39.936 - type: ndcg_at_10 value: 48.25 - type: ndcg_at_100 value: 52.674 - type: ndcg_at_1000 value: 54.638 - type: ndcg_at_3 value: 44.05 - type: ndcg_at_5 value: 46.125 - type: precision_at_1 value: 39.936 - type: precision_at_10 value: 9.096 - type: precision_at_100 value: 1.473 - type: precision_at_1000 value: 0.19499999999999998 - type: precision_at_3 value: 21.295 - type: precision_at_5 value: 15.121 - type: recall_at_1 value: 32.106 - type: recall_at_10 value: 58.107 - type: recall_at_100 value: 76.873 - type: recall_at_1000 value: 89.079 - type: recall_at_3 value: 45.505 - type: recall_at_5 value: 51.479 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 41.513 - type: map_at_10 value: 54.571999999999996 - type: map_at_100 value: 55.579 - type: map_at_1000 value: 55.626 - type: map_at_3 value: 51.127 - type: map_at_5 value: 53.151 - type: mrr_at_1 value: 47.398 - type: mrr_at_10 value: 57.82000000000001 - type: mrr_at_100 value: 58.457 - type: mrr_at_1000 value: 58.479000000000006 - type: mrr_at_3 value: 55.32899999999999 - type: mrr_at_5 value: 56.89999999999999 - type: ndcg_at_1 value: 47.398 - type: ndcg_at_10 value: 60.599000000000004 - type: ndcg_at_100 value: 64.366 - type: ndcg_at_1000 value: 65.333 - type: ndcg_at_3 value: 54.98 - type: ndcg_at_5 value: 57.874 - type: precision_at_1 value: 47.398 - type: precision_at_10 value: 9.806 - type: precision_at_100 value: 1.2590000000000001 - type: precision_at_1000 value: 0.13799999999999998 - type: precision_at_3 value: 24.619 - type: precision_at_5 value: 16.878 - type: recall_at_1 value: 41.513 - type: recall_at_10 value: 74.91799999999999 - type: recall_at_100 value: 90.96 - type: recall_at_1000 value: 97.923 - type: recall_at_3 value: 60.013000000000005 - type: recall_at_5 value: 67.245 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.319 - type: map_at_10 value: 35.766999999999996 - type: map_at_100 value: 36.765 - type: map_at_1000 value: 36.829 - type: map_at_3 value: 32.888 - type: map_at_5 value: 34.538999999999994 - type: mrr_at_1 value: 28.249000000000002 - type: mrr_at_10 value: 37.766 - type: mrr_at_100 value: 38.62 - type: mrr_at_1000 value: 38.667 - type: mrr_at_3 value: 35.009 - type: mrr_at_5 value: 36.608000000000004 - type: ndcg_at_1 value: 28.249000000000002 - type: ndcg_at_10 value: 41.215 - type: ndcg_at_100 value: 46.274 - type: ndcg_at_1000 value: 48.007 - type: ndcg_at_3 value: 35.557 - type: ndcg_at_5 value: 38.344 - type: precision_at_1 value: 28.249000000000002 - type: precision_at_10 value: 6.429 - type: precision_at_100 value: 0.9480000000000001 - type: precision_at_1000 value: 0.11399999999999999 - type: precision_at_3 value: 15.179 - type: precision_at_5 value: 10.734 - type: recall_at_1 value: 26.319 - type: recall_at_10 value: 56.157999999999994 - type: recall_at_100 value: 79.65 - type: recall_at_1000 value: 92.73 - type: recall_at_3 value: 40.738 - type: recall_at_5 value: 47.418 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 18.485 - type: map_at_10 value: 27.400999999999996 - type: map_at_100 value: 28.665000000000003 - type: map_at_1000 value: 28.79 - type: map_at_3 value: 24.634 - type: map_at_5 value: 26.313 - type: mrr_at_1 value: 23.134 - type: mrr_at_10 value: 32.332 - type: mrr_at_100 value: 33.318 - type: mrr_at_1000 value: 33.384 - type: mrr_at_3 value: 29.664 - type: mrr_at_5 value: 31.262 - type: ndcg_at_1 value: 23.134 - type: ndcg_at_10 value: 33.016 - type: ndcg_at_100 value: 38.763 - type: ndcg_at_1000 value: 41.619 - type: ndcg_at_3 value: 28.017999999999997 - type: ndcg_at_5 value: 30.576999999999998 - type: precision_at_1 value: 23.134 - type: precision_at_10 value: 6.069999999999999 - type: precision_at_100 value: 1.027 - type: precision_at_1000 value: 0.14200000000000002 - type: precision_at_3 value: 13.599 - type: precision_at_5 value: 9.975000000000001 - type: recall_at_1 value: 18.485 - type: recall_at_10 value: 45.39 - type: recall_at_100 value: 69.876 - type: recall_at_1000 value: 90.023 - type: recall_at_3 value: 31.587 - type: recall_at_5 value: 38.164 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 30.676 - type: map_at_10 value: 41.785 - type: map_at_100 value: 43.169000000000004 - type: map_at_1000 value: 43.272 - type: map_at_3 value: 38.462 - type: map_at_5 value: 40.32 - type: mrr_at_1 value: 37.729 - type: mrr_at_10 value: 47.433 - type: mrr_at_100 value: 48.303000000000004 - type: mrr_at_1000 value: 48.337 - type: mrr_at_3 value: 45.011 - type: mrr_at_5 value: 46.455 - type: ndcg_at_1 value: 37.729 - type: ndcg_at_10 value: 47.921 - type: ndcg_at_100 value: 53.477 - type: ndcg_at_1000 value: 55.300000000000004 - type: ndcg_at_3 value: 42.695 - type: ndcg_at_5 value: 45.175 - type: precision_at_1 value: 37.729 - type: precision_at_10 value: 8.652999999999999 - type: precision_at_100 value: 1.336 - type: precision_at_1000 value: 0.168 - type: precision_at_3 value: 20.18 - type: precision_at_5 value: 14.302000000000001 - type: recall_at_1 value: 30.676 - type: recall_at_10 value: 60.441 - type: recall_at_100 value: 83.37 - type: recall_at_1000 value: 95.092 - type: recall_at_3 value: 45.964 - type: recall_at_5 value: 52.319 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.978 - type: map_at_10 value: 35.926 - type: map_at_100 value: 37.341 - type: map_at_1000 value: 37.445 - type: map_at_3 value: 32.748 - type: map_at_5 value: 34.207 - type: mrr_at_1 value: 31.163999999999998 - type: mrr_at_10 value: 41.394 - type: mrr_at_100 value: 42.321 - type: mrr_at_1000 value: 42.368 - type: mrr_at_3 value: 38.964999999999996 - type: mrr_at_5 value: 40.135 - type: ndcg_at_1 value: 31.163999999999998 - type: ndcg_at_10 value: 42.191 - type: ndcg_at_100 value: 48.083999999999996 - type: ndcg_at_1000 value: 50.21 - type: ndcg_at_3 value: 36.979 - type: ndcg_at_5 value: 38.823 - type: precision_at_1 value: 31.163999999999998 - type: precision_at_10 value: 7.968 - type: precision_at_100 value: 1.2550000000000001 - type: precision_at_1000 value: 0.16199999999999998 - type: precision_at_3 value: 18.075 - type: precision_at_5 value: 12.626000000000001 - type: recall_at_1 value: 24.978 - type: recall_at_10 value: 55.410000000000004 - type: recall_at_100 value: 80.562 - type: recall_at_1000 value: 94.77600000000001 - type: recall_at_3 value: 40.359 - type: recall_at_5 value: 45.577 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.812166666666666 - type: map_at_10 value: 36.706916666666665 - type: map_at_100 value: 37.94016666666666 - type: map_at_1000 value: 38.05358333333333 - type: map_at_3 value: 33.72408333333334 - type: map_at_5 value: 35.36508333333333 - type: mrr_at_1 value: 31.91516666666667 - type: mrr_at_10 value: 41.09716666666666 - type: mrr_at_100 value: 41.931916666666666 - type: mrr_at_1000 value: 41.98458333333333 - type: mrr_at_3 value: 38.60183333333333 - type: mrr_at_5 value: 40.031916666666675 - type: ndcg_at_1 value: 31.91516666666667 - type: ndcg_at_10 value: 42.38725 - type: ndcg_at_100 value: 47.56291666666667 - type: ndcg_at_1000 value: 49.716499999999996 - type: ndcg_at_3 value: 37.36491666666667 - type: ndcg_at_5 value: 39.692166666666665 - type: precision_at_1 value: 31.91516666666667 - type: precision_at_10 value: 7.476749999999999 - type: precision_at_100 value: 1.1869166666666668 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 17.275249999999996 - type: precision_at_5 value: 12.25825 - type: recall_at_1 value: 26.812166666666666 - type: recall_at_10 value: 54.82933333333333 - type: recall_at_100 value: 77.36508333333333 - type: recall_at_1000 value: 92.13366666666667 - type: recall_at_3 value: 40.83508333333334 - type: recall_at_5 value: 46.85083333333334 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 25.352999999999998 - type: map_at_10 value: 33.025999999999996 - type: map_at_100 value: 33.882 - type: map_at_1000 value: 33.983999999999995 - type: map_at_3 value: 30.995 - type: map_at_5 value: 32.113 - type: mrr_at_1 value: 28.834 - type: mrr_at_10 value: 36.14 - type: mrr_at_100 value: 36.815 - type: mrr_at_1000 value: 36.893 - type: mrr_at_3 value: 34.305 - type: mrr_at_5 value: 35.263 - type: ndcg_at_1 value: 28.834 - type: ndcg_at_10 value: 37.26 - type: ndcg_at_100 value: 41.723 - type: ndcg_at_1000 value: 44.314 - type: ndcg_at_3 value: 33.584 - type: ndcg_at_5 value: 35.302 - type: precision_at_1 value: 28.834 - type: precision_at_10 value: 5.736 - type: precision_at_100 value: 0.876 - type: precision_at_1000 value: 0.117 - type: precision_at_3 value: 14.468 - type: precision_at_5 value: 9.847 - type: recall_at_1 value: 25.352999999999998 - type: recall_at_10 value: 47.155 - type: recall_at_100 value: 68.024 - type: recall_at_1000 value: 87.26899999999999 - type: recall_at_3 value: 37.074 - type: recall_at_5 value: 41.352 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 17.845 - type: map_at_10 value: 25.556 - type: map_at_100 value: 26.787 - type: map_at_1000 value: 26.913999999999998 - type: map_at_3 value: 23.075000000000003 - type: map_at_5 value: 24.308 - type: mrr_at_1 value: 21.714 - type: mrr_at_10 value: 29.543999999999997 - type: mrr_at_100 value: 30.543 - type: mrr_at_1000 value: 30.618000000000002 - type: mrr_at_3 value: 27.174 - type: mrr_at_5 value: 28.409000000000002 - type: ndcg_at_1 value: 21.714 - type: ndcg_at_10 value: 30.562 - type: ndcg_at_100 value: 36.27 - type: ndcg_at_1000 value: 39.033 - type: ndcg_at_3 value: 26.006 - type: ndcg_at_5 value: 27.843 - type: precision_at_1 value: 21.714 - type: precision_at_10 value: 5.657 - type: precision_at_100 value: 1 - type: precision_at_1000 value: 0.14100000000000001 - type: precision_at_3 value: 12.4 - type: precision_at_5 value: 8.863999999999999 - type: recall_at_1 value: 17.845 - type: recall_at_10 value: 41.72 - type: recall_at_100 value: 67.06400000000001 - type: recall_at_1000 value: 86.515 - type: recall_at_3 value: 28.78 - type: recall_at_5 value: 33.629999999999995 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.695 - type: map_at_10 value: 36.205999999999996 - type: map_at_100 value: 37.346000000000004 - type: map_at_1000 value: 37.447 - type: map_at_3 value: 32.84 - type: map_at_5 value: 34.733000000000004 - type: mrr_at_1 value: 31.343 - type: mrr_at_10 value: 40.335 - type: mrr_at_100 value: 41.162 - type: mrr_at_1000 value: 41.221000000000004 - type: mrr_at_3 value: 37.329 - type: mrr_at_5 value: 39.068999999999996 - type: ndcg_at_1 value: 31.343 - type: ndcg_at_10 value: 41.996 - type: ndcg_at_100 value: 47.096 - type: ndcg_at_1000 value: 49.4 - type: ndcg_at_3 value: 35.902 - type: ndcg_at_5 value: 38.848 - type: precision_at_1 value: 31.343 - type: precision_at_10 value: 7.146 - type: precision_at_100 value: 1.098 - type: precision_at_1000 value: 0.14100000000000001 - type: precision_at_3 value: 16.014 - type: precision_at_5 value: 11.735 - type: recall_at_1 value: 26.695 - type: recall_at_10 value: 55.525000000000006 - type: recall_at_100 value: 77.376 - type: recall_at_1000 value: 93.476 - type: recall_at_3 value: 39.439 - type: recall_at_5 value: 46.501 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 24.196 - type: map_at_10 value: 33.516 - type: map_at_100 value: 35.202 - type: map_at_1000 value: 35.426 - type: map_at_3 value: 30.561 - type: map_at_5 value: 31.961000000000002 - type: mrr_at_1 value: 29.644 - type: mrr_at_10 value: 38.769 - type: mrr_at_100 value: 39.843 - type: mrr_at_1000 value: 39.888 - type: mrr_at_3 value: 36.132999999999996 - type: mrr_at_5 value: 37.467 - type: ndcg_at_1 value: 29.644 - type: ndcg_at_10 value: 39.584 - type: ndcg_at_100 value: 45.964 - type: ndcg_at_1000 value: 48.27 - type: ndcg_at_3 value: 34.577999999999996 - type: ndcg_at_5 value: 36.498000000000005 - type: precision_at_1 value: 29.644 - type: precision_at_10 value: 7.668 - type: precision_at_100 value: 1.545 - type: precision_at_1000 value: 0.242 - type: precision_at_3 value: 16.271 - type: precision_at_5 value: 11.620999999999999 - type: recall_at_1 value: 24.196 - type: recall_at_10 value: 51.171 - type: recall_at_100 value: 79.212 - type: recall_at_1000 value: 92.976 - type: recall_at_3 value: 36.797999999999995 - type: recall_at_5 value: 42.006 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 21.023 - type: map_at_10 value: 29.677 - type: map_at_100 value: 30.618000000000002 - type: map_at_1000 value: 30.725 - type: map_at_3 value: 27.227 - type: map_at_5 value: 28.523 - type: mrr_at_1 value: 22.921 - type: mrr_at_10 value: 31.832 - type: mrr_at_100 value: 32.675 - type: mrr_at_1000 value: 32.751999999999995 - type: mrr_at_3 value: 29.513 - type: mrr_at_5 value: 30.89 - type: ndcg_at_1 value: 22.921 - type: ndcg_at_10 value: 34.699999999999996 - type: ndcg_at_100 value: 39.302 - type: ndcg_at_1000 value: 41.919000000000004 - type: ndcg_at_3 value: 29.965999999999998 - type: ndcg_at_5 value: 32.22 - type: precision_at_1 value: 22.921 - type: precision_at_10 value: 5.564 - type: precision_at_100 value: 0.8340000000000001 - type: precision_at_1000 value: 0.11800000000000001 - type: precision_at_3 value: 13.123999999999999 - type: precision_at_5 value: 9.316 - type: recall_at_1 value: 21.023 - type: recall_at_10 value: 48.015 - type: recall_at_100 value: 68.978 - type: recall_at_1000 value: 88.198 - type: recall_at_3 value: 35.397 - type: recall_at_5 value: 40.701 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 11.198 - type: map_at_10 value: 19.336000000000002 - type: map_at_100 value: 21.382 - type: map_at_1000 value: 21.581 - type: map_at_3 value: 15.992 - type: map_at_5 value: 17.613 - type: mrr_at_1 value: 25.080999999999996 - type: mrr_at_10 value: 36.032 - type: mrr_at_100 value: 37.1 - type: mrr_at_1000 value: 37.145 - type: mrr_at_3 value: 32.595 - type: mrr_at_5 value: 34.553 - type: ndcg_at_1 value: 25.080999999999996 - type: ndcg_at_10 value: 27.290999999999997 - type: ndcg_at_100 value: 35.31 - type: ndcg_at_1000 value: 38.885 - type: ndcg_at_3 value: 21.895999999999997 - type: ndcg_at_5 value: 23.669999999999998 - type: precision_at_1 value: 25.080999999999996 - type: precision_at_10 value: 8.645 - type: precision_at_100 value: 1.7209999999999999 - type: precision_at_1000 value: 0.23900000000000002 - type: precision_at_3 value: 16.287 - type: precision_at_5 value: 12.625 - type: recall_at_1 value: 11.198 - type: recall_at_10 value: 33.355000000000004 - type: recall_at_100 value: 60.912 - type: recall_at_1000 value: 80.89 - type: recall_at_3 value: 20.055 - type: recall_at_5 value: 25.14 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 9.228 - type: map_at_10 value: 20.018 - type: map_at_100 value: 28.388999999999996 - type: map_at_1000 value: 30.073 - type: map_at_3 value: 14.366999999999999 - type: map_at_5 value: 16.705000000000002 - type: mrr_at_1 value: 69 - type: mrr_at_10 value: 77.058 - type: mrr_at_100 value: 77.374 - type: mrr_at_1000 value: 77.384 - type: mrr_at_3 value: 75.708 - type: mrr_at_5 value: 76.608 - type: ndcg_at_1 value: 57.49999999999999 - type: ndcg_at_10 value: 41.792 - type: ndcg_at_100 value: 47.374 - type: ndcg_at_1000 value: 55.13 - type: ndcg_at_3 value: 46.353 - type: ndcg_at_5 value: 43.702000000000005 - type: precision_at_1 value: 69 - type: precision_at_10 value: 32.85 - type: precision_at_100 value: 10.708 - type: precision_at_1000 value: 2.024 - type: precision_at_3 value: 49.5 - type: precision_at_5 value: 42.05 - type: recall_at_1 value: 9.228 - type: recall_at_10 value: 25.635 - type: recall_at_100 value: 54.894 - type: recall_at_1000 value: 79.38 - type: recall_at_3 value: 15.68 - type: recall_at_5 value: 19.142 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 52.035 - type: f1 value: 46.85325505614071 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 70.132 - type: map_at_10 value: 79.527 - type: map_at_100 value: 79.81200000000001 - type: map_at_1000 value: 79.828 - type: map_at_3 value: 78.191 - type: map_at_5 value: 79.092 - type: mrr_at_1 value: 75.563 - type: mrr_at_10 value: 83.80199999999999 - type: mrr_at_100 value: 83.93 - type: mrr_at_1000 value: 83.933 - type: mrr_at_3 value: 82.818 - type: mrr_at_5 value: 83.505 - type: ndcg_at_1 value: 75.563 - type: ndcg_at_10 value: 83.692 - type: ndcg_at_100 value: 84.706 - type: ndcg_at_1000 value: 85.001 - type: ndcg_at_3 value: 81.51 - type: ndcg_at_5 value: 82.832 - type: precision_at_1 value: 75.563 - type: precision_at_10 value: 10.245 - type: precision_at_100 value: 1.0959999999999999 - type: precision_at_1000 value: 0.11399999999999999 - type: precision_at_3 value: 31.518 - type: precision_at_5 value: 19.772000000000002 - type: recall_at_1 value: 70.132 - type: recall_at_10 value: 92.204 - type: recall_at_100 value: 96.261 - type: recall_at_1000 value: 98.17399999999999 - type: recall_at_3 value: 86.288 - type: recall_at_5 value: 89.63799999999999 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 22.269 - type: map_at_10 value: 36.042 - type: map_at_100 value: 37.988 - type: map_at_1000 value: 38.162 - type: map_at_3 value: 31.691000000000003 - type: map_at_5 value: 33.988 - type: mrr_at_1 value: 44.907000000000004 - type: mrr_at_10 value: 53.348 - type: mrr_at_100 value: 54.033 - type: mrr_at_1000 value: 54.064 - type: mrr_at_3 value: 50.977 - type: mrr_at_5 value: 52.112 - type: ndcg_at_1 value: 44.907000000000004 - type: ndcg_at_10 value: 44.302 - type: ndcg_at_100 value: 51.054 - type: ndcg_at_1000 value: 53.822 - type: ndcg_at_3 value: 40.615 - type: ndcg_at_5 value: 41.455999999999996 - type: precision_at_1 value: 44.907000000000004 - type: precision_at_10 value: 12.176 - type: precision_at_100 value: 1.931 - type: precision_at_1000 value: 0.243 - type: precision_at_3 value: 27.16 - type: precision_at_5 value: 19.567999999999998 - type: recall_at_1 value: 22.269 - type: recall_at_10 value: 51.188 - type: recall_at_100 value: 75.924 - type: recall_at_1000 value: 92.525 - type: recall_at_3 value: 36.643 - type: recall_at_5 value: 42.27 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 40.412 - type: map_at_10 value: 66.376 - type: map_at_100 value: 67.217 - type: map_at_1000 value: 67.271 - type: map_at_3 value: 62.741 - type: map_at_5 value: 65.069 - type: mrr_at_1 value: 80.824 - type: mrr_at_10 value: 86.53 - type: mrr_at_100 value: 86.67399999999999 - type: mrr_at_1000 value: 86.678 - type: mrr_at_3 value: 85.676 - type: mrr_at_5 value: 86.256 - type: ndcg_at_1 value: 80.824 - type: ndcg_at_10 value: 74.332 - type: ndcg_at_100 value: 77.154 - type: ndcg_at_1000 value: 78.12400000000001 - type: ndcg_at_3 value: 69.353 - type: ndcg_at_5 value: 72.234 - type: precision_at_1 value: 80.824 - type: precision_at_10 value: 15.652 - type: precision_at_100 value: 1.7840000000000003 - type: precision_at_1000 value: 0.191 - type: precision_at_3 value: 44.911 - type: precision_at_5 value: 29.221000000000004 - type: recall_at_1 value: 40.412 - type: recall_at_10 value: 78.25800000000001 - type: recall_at_100 value: 89.196 - type: recall_at_1000 value: 95.544 - type: recall_at_3 value: 67.367 - type: recall_at_5 value: 73.05199999999999 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 92.78880000000001 - type: ap value: 89.39251741048801 - type: f1 value: 92.78019950076781 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 22.888 - type: map_at_10 value: 35.146 - type: map_at_100 value: 36.325 - type: map_at_1000 value: 36.372 - type: map_at_3 value: 31.3 - type: map_at_5 value: 33.533 - type: mrr_at_1 value: 23.480999999999998 - type: mrr_at_10 value: 35.777 - type: mrr_at_100 value: 36.887 - type: mrr_at_1000 value: 36.928 - type: mrr_at_3 value: 31.989 - type: mrr_at_5 value: 34.202 - type: ndcg_at_1 value: 23.496 - type: ndcg_at_10 value: 42.028999999999996 - type: ndcg_at_100 value: 47.629 - type: ndcg_at_1000 value: 48.785000000000004 - type: ndcg_at_3 value: 34.227000000000004 - type: ndcg_at_5 value: 38.207 - type: precision_at_1 value: 23.496 - type: precision_at_10 value: 6.596 - type: precision_at_100 value: 0.9400000000000001 - type: precision_at_1000 value: 0.104 - type: precision_at_3 value: 14.513000000000002 - type: precision_at_5 value: 10.711 - type: recall_at_1 value: 22.888 - type: recall_at_10 value: 63.129999999999995 - type: recall_at_100 value: 88.90299999999999 - type: recall_at_1000 value: 97.69 - type: recall_at_3 value: 42.014 - type: recall_at_5 value: 51.554 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 94.59188326493388 - type: f1 value: 94.36568950290486 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 79.25672594619242 - type: f1 value: 59.52405059722216 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 77.4142568930733 - type: f1 value: 75.23044196543388 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 80.44720914593141 - type: f1 value: 80.41049641537015 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 31.960921474993775 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 30.88042240204361 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 32.27071371606404 - type: mrr value: 33.541450459533856 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 6.551 - type: map_at_10 value: 14.359 - type: map_at_100 value: 18.157 - type: map_at_1000 value: 19.659 - type: map_at_3 value: 10.613999999999999 - type: map_at_5 value: 12.296 - type: mrr_at_1 value: 47.368 - type: mrr_at_10 value: 56.689 - type: mrr_at_100 value: 57.24399999999999 - type: mrr_at_1000 value: 57.284 - type: mrr_at_3 value: 54.489 - type: mrr_at_5 value: 55.928999999999995 - type: ndcg_at_1 value: 45.511 - type: ndcg_at_10 value: 36.911 - type: ndcg_at_100 value: 34.241 - type: ndcg_at_1000 value: 43.064 - type: ndcg_at_3 value: 42.348 - type: ndcg_at_5 value: 39.884 - type: precision_at_1 value: 46.749 - type: precision_at_10 value: 27.028000000000002 - type: precision_at_100 value: 8.52 - type: precision_at_1000 value: 2.154 - type: precision_at_3 value: 39.525 - type: precision_at_5 value: 34.18 - type: recall_at_1 value: 6.551 - type: recall_at_10 value: 18.602 - type: recall_at_100 value: 34.882999999999996 - type: recall_at_1000 value: 66.049 - type: recall_at_3 value: 11.872 - type: recall_at_5 value: 14.74 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 27.828999999999997 - type: map_at_10 value: 43.606 - type: map_at_100 value: 44.656 - type: map_at_1000 value: 44.690000000000005 - type: map_at_3 value: 39.015 - type: map_at_5 value: 41.625 - type: mrr_at_1 value: 31.518 - type: mrr_at_10 value: 46.047 - type: mrr_at_100 value: 46.846 - type: mrr_at_1000 value: 46.867999999999995 - type: mrr_at_3 value: 42.154 - type: mrr_at_5 value: 44.468999999999994 - type: ndcg_at_1 value: 31.518 - type: ndcg_at_10 value: 51.768 - type: ndcg_at_100 value: 56.184999999999995 - type: ndcg_at_1000 value: 56.92 - type: ndcg_at_3 value: 43.059999999999995 - type: ndcg_at_5 value: 47.481 - type: precision_at_1 value: 31.518 - type: precision_at_10 value: 8.824 - type: precision_at_100 value: 1.131 - type: precision_at_1000 value: 0.12 - type: precision_at_3 value: 19.969 - type: precision_at_5 value: 14.502 - type: recall_at_1 value: 27.828999999999997 - type: recall_at_10 value: 74.244 - type: recall_at_100 value: 93.325 - type: recall_at_1000 value: 98.71799999999999 - type: recall_at_3 value: 51.601 - type: recall_at_5 value: 61.841 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 71.54 - type: map_at_10 value: 85.509 - type: map_at_100 value: 86.137 - type: map_at_1000 value: 86.151 - type: map_at_3 value: 82.624 - type: map_at_5 value: 84.425 - type: mrr_at_1 value: 82.45 - type: mrr_at_10 value: 88.344 - type: mrr_at_100 value: 88.437 - type: mrr_at_1000 value: 88.437 - type: mrr_at_3 value: 87.417 - type: mrr_at_5 value: 88.066 - type: ndcg_at_1 value: 82.45 - type: ndcg_at_10 value: 89.092 - type: ndcg_at_100 value: 90.252 - type: ndcg_at_1000 value: 90.321 - type: ndcg_at_3 value: 86.404 - type: ndcg_at_5 value: 87.883 - type: precision_at_1 value: 82.45 - type: precision_at_10 value: 13.496 - type: precision_at_100 value: 1.536 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 37.833 - type: precision_at_5 value: 24.79 - type: recall_at_1 value: 71.54 - type: recall_at_10 value: 95.846 - type: recall_at_100 value: 99.715 - type: recall_at_1000 value: 99.979 - type: recall_at_3 value: 88.01299999999999 - type: recall_at_5 value: 92.32000000000001 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 57.60557586253866 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 64.0287172242051 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 3.9849999999999994 - type: map_at_10 value: 11.397 - type: map_at_100 value: 13.985 - type: map_at_1000 value: 14.391000000000002 - type: map_at_3 value: 7.66 - type: map_at_5 value: 9.46 - type: mrr_at_1 value: 19.8 - type: mrr_at_10 value: 31.958 - type: mrr_at_100 value: 33.373999999999995 - type: mrr_at_1000 value: 33.411 - type: mrr_at_3 value: 28.316999999999997 - type: mrr_at_5 value: 30.297 - type: ndcg_at_1 value: 19.8 - type: ndcg_at_10 value: 19.580000000000002 - type: ndcg_at_100 value: 29.555999999999997 - type: ndcg_at_1000 value: 35.882 - type: ndcg_at_3 value: 17.544 - type: ndcg_at_5 value: 15.815999999999999 - type: precision_at_1 value: 19.8 - type: precision_at_10 value: 10.61 - type: precision_at_100 value: 2.501 - type: precision_at_1000 value: 0.40099999999999997 - type: precision_at_3 value: 16.900000000000002 - type: precision_at_5 value: 14.44 - type: recall_at_1 value: 3.9849999999999994 - type: recall_at_10 value: 21.497 - type: recall_at_100 value: 50.727999999999994 - type: recall_at_1000 value: 81.27499999999999 - type: recall_at_3 value: 10.263 - type: recall_at_5 value: 14.643 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 85.0087509585503 - type: cos_sim_spearman value: 81.74697270664319 - type: euclidean_pearson value: 81.80424382731947 - type: euclidean_spearman value: 81.29794251968431 - type: manhattan_pearson value: 81.81524666226125 - type: manhattan_spearman value: 81.29475370198963 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 86.44442736429552 - type: cos_sim_spearman value: 78.51011398910948 - type: euclidean_pearson value: 83.36181801196723 - type: euclidean_spearman value: 79.47272621331535 - type: manhattan_pearson value: 83.3660113483837 - type: manhattan_spearman value: 79.47695922566032 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 85.82923943323635 - type: cos_sim_spearman value: 86.62037823380983 - type: euclidean_pearson value: 83.56369548403958 - type: euclidean_spearman value: 84.2176755481191 - type: manhattan_pearson value: 83.55460702084464 - type: manhattan_spearman value: 84.18617930921467 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 84.09071068110103 - type: cos_sim_spearman value: 83.05697553913335 - type: euclidean_pearson value: 81.1377457216497 - type: euclidean_spearman value: 81.74714169016676 - type: manhattan_pearson value: 81.0893424142723 - type: manhattan_spearman value: 81.7058918219677 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 87.61132157220429 - type: cos_sim_spearman value: 88.38581627185445 - type: euclidean_pearson value: 86.14904510913374 - type: euclidean_spearman value: 86.5452758925542 - type: manhattan_pearson value: 86.1484025377679 - type: manhattan_spearman value: 86.55483841566252 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 85.46195145161064 - type: cos_sim_spearman value: 86.82409112251158 - type: euclidean_pearson value: 84.75479672288957 - type: euclidean_spearman value: 85.41144307151548 - type: manhattan_pearson value: 84.70914329694165 - type: manhattan_spearman value: 85.38477943384089 - 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: 88.06351289930238 - type: cos_sim_spearman value: 87.90311138579116 - type: euclidean_pearson value: 86.17651467063077 - type: euclidean_spearman value: 84.89447802019073 - type: manhattan_pearson value: 86.3267677479595 - type: manhattan_spearman value: 85.00472295103874 - task: type: STS dataset: type: mteb/sts22-crosslingual-sts name: MTEB STS22 (en) config: en split: test revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80 metrics: - type: cos_sim_pearson value: 67.78311975978767 - type: cos_sim_spearman value: 66.76465685245887 - type: euclidean_pearson value: 67.21687806595443 - type: euclidean_spearman value: 65.05776733534435 - type: manhattan_pearson value: 67.14008143635883 - type: manhattan_spearman value: 65.25247076149701 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 86.7403488889418 - type: cos_sim_spearman value: 87.76870289783061 - type: euclidean_pearson value: 84.83171077794671 - type: euclidean_spearman value: 85.50579695091902 - type: manhattan_pearson value: 84.83074260180555 - type: manhattan_spearman value: 85.47589026938667 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 87.56234016237356 - type: mrr value: 96.26124238869338 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: map_at_1 value: 59.660999999999994 - type: map_at_10 value: 69.105 - type: map_at_100 value: 69.78 - type: map_at_1000 value: 69.80199999999999 - type: map_at_3 value: 65.991 - 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ember-v1

This model has been trained on an extensive corpus of text pairs that encompass a broad spectrum of domains, including finance, science, medicine, law, and various others. During the training process, we incorporated techniques derived from the [RetroMAE](https://arxiv.org/abs/2205.12035) and [SetFit](https://arxiv.org/abs/2209.11055) research papers. ### Plans - The research paper will be published soon. - The v2 of the model is currently in development and will feature an extended maximum sequence length of 4,000 tokens. ## Usage Use with transformers: ```python import torch.nn.functional as F from torch import Tensor from transformers import AutoTokenizer, AutoModel def average_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor: last_hidden = last_hidden_states.masked_fill(~attention_mask[..., None].bool(), 0.0) return last_hidden.sum(dim=1) / attention_mask.sum(dim=1)[..., None] input_texts = [ "This is an example sentence", "Each sentence is converted" ] tokenizer = AutoTokenizer.from_pretrained("llmrails/ember-v1") model = AutoModel.from_pretrained("llmrails/ember-v1") # Tokenize the input texts batch_dict = tokenizer(input_texts, max_length=512, padding=True, truncation=True, return_tensors='pt') outputs = model(**batch_dict) embeddings = average_pool(outputs.last_hidden_state, batch_dict['attention_mask']) # (Optionally) normalize embeddings embeddings = F.normalize(embeddings, p=2, dim=1) scores = (embeddings[:1] @ embeddings[1:].T) * 100 print(scores.tolist()) ``` Use with sentence-transformers: ```python from sentence_transformers import SentenceTransformer from sentence_transformers.util import cos_sim sentences = [ "This is an example sentence", "Each sentence is converted" ] model = SentenceTransformer('llmrails/ember-v1') embeddings = model.encode(sentences) print(cos_sim(embeddings[0], embeddings[1])) ``` ## Massive Text Embedding Benchmark (MTEB) Evaluation Our model achieve state-of-the-art performance on [MTEB leaderboard](https://huggingface.co/spaces/mteb/leaderboard) | Model Name | Dimension | Sequence Length | Average (56) | |:-----------------------------------------------------------------------:|:---------:|:---:|:------------:| | [ember-v1](https://huggingface.co/llmrails/ember-v1) | 1024 | 512 | **63.54** | | [bge-large-en-v1.5](https://huggingface.co/BAAI/bge-large-en-v1.5) | 1024 | 512 | 63.23 | | [bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) | 768 | 512 | 63.05 | | [text-embedding-ada-002](https://platform.openai.com/docs/guides/embeddings/types-of-embedding-models) | 1536 | 8191 | 60.99 | ### Limitation This model exclusively caters to English texts, and any lengthy texts will be truncated to a maximum of 512 tokens. ## License MIT ## Citation ```bibtex @misc{nur2024emberv1, title={ember-v1: SOTA embedding model}, author={Enrike Nur and Anar Aliyev}, year={2023}, } ```