nomic-embed-text-v1_a14032665276706816973660/README.md

library_name	pipeline_tag	tags	model-index	license	language	new_version
sentence-transformers	sentence-similarity	feature-extraction sentence-similarity mteb transformers transformers.js	name results epoch_0_model task dataset metrics type Classification type name config split revision mteb/amazon_counterfactual MTEB AmazonCounterfactualClassification (en) en test e8379541af4e31359cca9fbcf4b00f2671dba205 type value accuracy 76.8507462686567 type value ap 40.592189159090495 type value f1 71.01634655512476 task dataset metrics type Classification type name config split revision mteb/amazon_polarity MTEB AmazonPolarityClassification default test e2d317d38cd51312af73b3d32a06d1a08b442046 type value accuracy 91.51892500000001 type value ap 88.50346762975335 type value f1 91.50342077459624 task dataset metrics type Classification type name config split revision mteb/amazon_reviews_multi MTEB AmazonReviewsClassification (en) en test 1399c76144fd37290681b995c656ef9b2e06e26d type value accuracy 47.364 type value f1 46.72708080922794 task dataset metrics type Retrieval type name config split revision arguana MTEB ArguAna default test None type value map_at_1 25.178 type value map_at_10 40.244 type value map_at_100 41.321999999999996 type value map_at_1000 41.331 type value map_at_3 35.016999999999996 type value map_at_5 37.99 type value mrr_at_1 25.605 type value mrr_at_10 40.422000000000004 type value mrr_at_100 41.507 type value mrr_at_1000 41.516 type value mrr_at_3 35.23 type value mrr_at_5 38.15 type value ndcg_at_1 25.178 type value ndcg_at_10 49.258 type value ndcg_at_100 53.776 type value ndcg_at_1000 53.995000000000005 type value ndcg_at_3 38.429 type value ndcg_at_5 43.803 type value precision_at_1 25.178 type value precision_at_10 7.831 type value precision_at_100 0.979 type value precision_at_1000 0.1 type value precision_at_3 16.121 type value precision_at_5 12.29 type value recall_at_1 25.178 type value recall_at_10 78.307 type value recall_at_100 97.866 type value recall_at_1000 99.57300000000001 type value recall_at_3 48.364000000000004 type value recall_at_5 61.451 task dataset metrics type Clustering type name config split revision mteb/arxiv-clustering-p2p MTEB ArxivClusteringP2P default test a122ad7f3f0291bf49cc6f4d32aa80929df69d5d type value v_measure 45.93034494751465 task dataset metrics type Clustering type name config split revision mteb/arxiv-clustering-s2s MTEB ArxivClusteringS2S default test f910caf1a6075f7329cdf8c1a6135696f37dbd53 type value v_measure 36.64579480054327 task dataset metrics type Reranking type name config split revision mteb/askubuntudupquestions-reranking MTEB AskUbuntuDupQuestions default test 2000358ca161889fa9c082cb41daa8dcfb161a54 type value map 60.601310529222054 type value mrr 75.04484896451656 task dataset metrics type STS type name config split revision mteb/biosses-sts MTEB BIOSSES default test d3fb88f8f02e40887cd149695127462bbcf29b4a type value cos_sim_pearson 88.57797718095814 type value cos_sim_spearman 86.47064499110101 type value euclidean_pearson 87.4559602783142 type value euclidean_spearman 86.47064499110101 type value manhattan_pearson 87.7232764230245 type value manhattan_spearman 86.91222131777742 task dataset metrics type Classification type name config split revision mteb/banking77 MTEB Banking77Classification default test 0fd18e25b25c072e09e0d92ab615fda904d66300 type value accuracy 84.5422077922078 type value f1 84.47657456950589 task dataset metrics type Clustering type name config split revision mteb/biorxiv-clustering-p2p MTEB BiorxivClusteringP2P default test 65b79d1d13f80053f67aca9498d9402c2d9f1f40 type value v_measure 38.48953561974464 task dataset metrics type Clustering type name config split revision mteb/biorxiv-clustering-s2s MTEB BiorxivClusteringS2S default test 258694dd0231531bc1fd9de6ceb52a0853c6d908 type value v_measure 32.75995857510105 task dataset metrics type Retrieval type name config split revision BeIR/cqadupstack MTEB CQADupstackAndroidRetrieval default test None type value map_at_1 30.008000000000003 type value map_at_10 39.51 type value map_at_100 40.841 type value map_at_1000 40.973 type value map_at_3 36.248999999999995 type value map_at_5 38.096999999999994 type value mrr_at_1 36.481 type value mrr_at_10 44.818000000000005 type value mrr_at_100 45.64 type value mrr_at_1000 45.687 type value mrr_at_3 42.036 type value mrr_at_5 43.782 type value ndcg_at_1 36.481 type value ndcg_at_10 45.152 type value ndcg_at_100 50.449 type value ndcg_at_1000 52.76499999999999 type value ndcg_at_3 40.161 type value ndcg_at_5 42.577999999999996 type value precision_at_1 36.481 type value precision_at_10 8.369 type value precision_at_100 1.373 type value precision_at_1000 0.186 type value precision_at_3 18.693 type value precision_at_5 13.533999999999999 type value recall_at_1 30.008000000000003 type value recall_at_10 56.108999999999995 type value recall_at_100 78.55499999999999 type value recall_at_1000 93.659 type value recall_at_3 41.754999999999995 type value recall_at_5 48.296 task dataset metrics type Retrieval type name config split revision BeIR/cqadupstack MTEB CQADupstackEnglishRetrieval default test None type value map_at_1 30.262 type value map_at_10 40.139 type value map_at_100 41.394 type value map_at_1000 41.526 type value map_at_3 37.155 type value map_at_5 38.785 type value mrr_at_1 38.153 type value mrr_at_10 46.369 type value mrr_at_100 47.072 type value mrr_at_1000 47.111999999999995 type value mrr_at_3 44.268 type value mrr_at_5 45.389 type value ndcg_at_1 38.153 type value ndcg_at_10 45.925 type value ndcg_at_100 50.394000000000005 type value ndcg_at_1000 52.37500000000001 type value ndcg_at_3 41.754000000000005 type value ndcg_at_5 43.574 type value precision_at_1 38.153 type value precision_at_10 8.796 type value precision_at_100 1.432 type value precision_at_1000 0.189 type value precision_at_3 20.318 type value precision_at_5 14.395 type value recall_at_1 30.262 type value recall_at_10 55.72200000000001 type value recall_at_100 74.97500000000001 type value recall_at_1000 87.342 type value recall_at_3 43.129 type value recall_at_5 48.336 task dataset metrics type Retrieval type name config split revision BeIR/cqadupstack MTEB CQADupstackGamingRetrieval default test None type value map_at_1 39.951 type value map_at_10 51.248000000000005 type value map_at_100 52.188 type value map_at_1000 52.247 type value map_at_3 48.211 type value map_at_5 49.797000000000004 type value mrr_at_1 45.329 type value mrr_at_10 54.749 type value mrr_at_100 55.367999999999995 type value mrr_at_1000 55.400000000000006 type value mrr_at_3 52.382 type value mrr_at_5 53.649 type value ndcg_at_1 45.329 type value ndcg_at_10 56.847 type value ndcg_at_100 60.738 type value ndcg_at_1000 61.976 type value ndcg_at_3 51.59 type value ndcg_at_5 53.915 type value precision_at_1 45.329 type value precision_at_10 8.959 type value precision_at_100 1.187 type value precision_at_1000 0.134 type value precision_at_3 22.612 type value precision_at_5 15.273 type value recall_at_1 39.951 type value recall_at_10 70.053 type value recall_at_100 86.996 type value recall_at_1000 95.707 type value recall_at_3 56.032000000000004 type value recall_at_5 61.629999999999995 task dataset metrics type Retrieval type name config split revision BeIR/cqadupstack MTEB CQADupstackGisRetrieval default test None type value map_at_1 25.566 type value map_at_10 33.207 type value map_at_100 34.166000000000004 type value map_at_1000 34.245 type value map_at_3 30.94 type value map_at_5 32.01 type value mrr_at_1 27.345000000000002 type value mrr_at_10 35.193000000000005 type value mrr_at_100 35.965 type value mrr_at_1000 36.028999999999996 type value mrr_at_3 32.806000000000004 type value mrr_at_5 34.021 type value ndcg_at_1 27.345000000000002 type value ndcg_at_10 37.891999999999996 type value ndcg_at_100 42.664 type value ndcg_at_1000 44.757000000000005 type value ndcg_at_3 33.123000000000005 type value ndcg_at_5 35.035 type value precision_at_1 27.345000000000002 type value precision_at_10 5.763 type value precision_at_100 0.859 type value precision_at_1000 0.108 type value precision_at_3 13.71 type value precision_at_5 9.401 type value recall_at_1 25.566 type value recall_at_10 50.563 type value recall_at_100 72.86399999999999 type value recall_at_1000 88.68599999999999 type value recall_at_3 37.43 type value recall_at_5 41.894999999999996 task dataset metrics type Retrieval type name config split revision BeIR/cqadupstack MTEB CQADupstackMathematicaRetrieval default test None type value map_at_1 16.663 type value map_at_10 23.552 type value map_at_100 24.538 type value map_at_1000 24.661 type value map_at_3 21.085 type value map_at_5 22.391 type value mrr_at_1 20.025000000000002 type value mrr_at_10 27.643 type value mrr_at_100 28.499999999999996 type value mrr_at_1000 28.582 type value mrr_at_3 25.083 type value mrr_at_5 26.544 type value ndcg_at_1 20.025000000000002 type value ndcg_at_10 28.272000000000002 type value ndcg_at_100 33.353 type value ndcg_at_1000 36.454 type value ndcg_at_3 23.579 type value ndcg_at_5 25.685000000000002 type value precision_at_1 20.025000000000002 type value precision_at_10 5.187 type value precision_at_100 0.897 type value precision_at_1000 0.13 type value precision_at_3 10.987 type value precision_at_5 8.06 type value recall_at_1 16.663 type value recall_at_10 38.808 type value recall_at_100 61.305 type value recall_at_1000 83.571 type value recall_at_3 25.907999999999998 type value recall_at_5 31.214 task dataset metrics type Retrieval type name config split revision BeIR/cqadupstack MTEB CQADupstackPhysicsRetrieval default test None type value map_at_1 27.695999999999998 type value map_at_10 37.018 type value map_at_100 38.263000000000005 type value map_at_1000 38.371 type value map_at_3 34.226 type value map_at_5 35.809999999999995 type value mrr_at_1 32.916000000000004 type value mrr_at_10 42.067 type value mrr_at_100 42.925000000000004 type value mrr_at_1000 42.978 type value mrr_at_3 39.637 type value mrr_at_5 41.134 type value ndcg_at_1 32.916000000000004 type value ndcg_at_10 42.539 type value ndcg_at_100 47.873 type value ndcg_at_1000 50.08200000000001 type value ndcg_at_3 37.852999999999994 type value ndcg_at_5 40.201 type value precision_at_1 32.916000000000004 type value precision_at_10 7.5840000000000005 type value precision_at_100 1.199 type value precision_at_1000 0.155 type value precision_at_3 17.485 type value precision_at_5 12.512 type value recall_at_1 27.695999999999998 type value recall_at_10 53.638 type value recall_at_100 76.116 type value recall_at_1000 91.069 type value recall_at_3 41.13 type value recall_at_5 46.872 task dataset metrics type Retrieval type name config split revision BeIR/cqadupstack MTEB CQADupstackProgrammersRetrieval default test None type value map_at_1 24.108 type value map_at_10 33.372 type value map_at_100 34.656 type value map_at_1000 34.768 type value map_at_3 30.830999999999996 type value map_at_5 32.204 type value mrr_at_1 29.110000000000003 type value mrr_at_10 37.979 type value mrr_at_100 38.933 type value mrr_at_1000 38.988 type value mrr_at_3 35.731 type value mrr_at_5 36.963 type value ndcg_at_1 29.110000000000003 type value ndcg_at_10 38.635000000000005 type value ndcg_at_100 44.324999999999996 type value ndcg_at_1000 46.747 type value ndcg_at_3 34.37 type value ndcg_at_5 36.228 type value precision_at_1 29.110000000000003 type value precision_at_10 6.963 type value precision_at_100 1.146 type value precision_at_1000 0.152 type value precision_at_3 16.400000000000002 type value precision_at_5 11.552999999999999 type value recall_at_1 24.108 type value recall_at_10 49.597 type value recall_at_100 73.88900000000001 type value recall_at_1000 90.62400000000001 type value recall_at_3 37.662 type value recall_at_5 42.565 task dataset metrics type Retrieval type name config split revision BeIR/cqadupstack MTEB CQADupstackRetrieval default test None type value map_at_1 25.00791666666667 type value map_at_10 33.287749999999996 type value map_at_100 34.41141666666667 type value map_at_1000 34.52583333333333 type value map_at_3 30.734416666666668 type value map_at_5 32.137166666666666 type value mrr_at_1 29.305666666666664 type value mrr_at_10 37.22966666666666 type value mrr_at_100 38.066583333333334 type value mrr_at_1000 38.12616666666667 type value mrr_at_3 34.92275 type value mrr_at_5 36.23333333333334 type value ndcg_at_1 29.305666666666664 type value ndcg_at_10 38.25533333333333 type value ndcg_at_100 43.25266666666666 type value ndcg_at_1000 45.63583333333334 type value ndcg_at_3 33.777166666666666 type value ndcg_at_5 35.85 type value precision_at_1 29.305666666666664 type value precision_at_10 6.596416666666667 type value precision_at_100 1.0784166666666668 type value precision_at_1000 0.14666666666666664 type value precision_at_3 15.31075 type value precision_at_5 10.830916666666667 type value recall_at_1 25.00791666666667 type value recall_at_10 49.10933333333333 type value recall_at_100 71.09216666666667 type value recall_at_1000 87.77725000000001 type value recall_at_3 36.660916666666665 type value recall_at_5 41.94149999999999 task dataset metrics type Retrieval type name config split revision BeIR/cqadupstack MTEB CQADupstackStatsRetrieval default test None type value map_at_1 23.521 type value map_at_10 30.043 type value map_at_100 30.936000000000003 type value map_at_1000 31.022 type value map_at_3 27.926000000000002 type value map_at_5 29.076999999999998 type value mrr_at_1 26.227 type value mrr_at_10 32.822 type value mrr_at_100 33.61 type value mrr_at_1000 33.672000000000004 type value mrr_at_3 30.776999999999997 type value mrr_at_5 31.866 type value ndcg_at_1 26.227 type value ndcg_at_10 34.041 type value ndcg_at_100 38.394 type value ndcg_at_1000 40.732 type value ndcg_at_3 30.037999999999997 type value ndcg_at_5 31.845000000000002 type value precision_at_1 26.227 type value precision_at_10 5.244999999999999 type value precision_at_100 0.808 type value precision_at_1000 0.107 type value precision_at_3 12.679000000000002 type value precision_at_5 8.773 type value recall_at_1 23.521 type value recall_at_10 43.633 type value recall_at_100 63.126000000000005 type value recall_at_1000 80.765 type value recall_at_3 32.614 type value recall_at_5 37.15 task dataset metrics type Retrieval type name config split revision BeIR/cqadupstack MTEB CQADupstackTexRetrieval default test None type value map_at_1 16.236 type value map_at_10 22.898 type value map_at_100 23.878 type value map_at_1000 24.009 type value map_at_3 20.87 type value map_at_5 22.025 type value mrr_at_1 19.339000000000002 type value mrr_at_10 26.382 type value mrr_at_100 27.245 type value mrr_at_1000 27.33 type value mrr_at_3 24.386 type value mrr_at_5 25.496000000000002 type value ndcg_at_1 19.339000000000002 type value ndcg_at_10 27.139999999999997 type value ndcg_at_100 31.944 type value ndcg_at_1000 35.077999999999996 type value ndcg_at_3 23.424 type value ndcg_at_5 25.188 type value precision_at_1 19.339000000000002 type value precision_at_10 4.8309999999999995 type value precision_at_100 0.845 type value precision_at_1000 0.128 type value precision_at_3 10.874 type value precision_at_5 7.825 type value recall_at_1 16.236 type value recall_at_10 36.513 type value recall_at_100 57.999 type value recall_at_1000 80.512 type value recall_at_3 26.179999999999996 type value recall_at_5 30.712 task dataset metrics type Retrieval type name config split revision BeIR/cqadupstack MTEB CQADupstackUnixRetrieval default test None type value map_at_1 24.11 type value map_at_10 31.566 type value map_at_100 32.647 type value map_at_1000 32.753 type value map_at_3 29.24 type value map_at_5 30.564999999999998 type value mrr_at_1 28.265 type value mrr_at_10 35.504000000000005 type value mrr_at_100 36.436 type value mrr_at_1000 36.503 type value mrr_at_3 33.349000000000004 type value mrr_at_5 34.622 type value ndcg_at_1 28.265 type value ndcg_at_10 36.192 type value ndcg_at_100 41.388000000000005 type value ndcg_at_1000 43.948 type value ndcg_at_3 31.959 type value ndcg_at_5 33.998 type value precision_at_1 28.265 type value precision_at_10 5.989 type value precision_at_100 0.9650000000000001 type value precision_at_1000 0.13 type value precision_at_3 14.335 type value precision_at_5 10.112 type value recall_at_1 24.11 type value recall_at_10 46.418 type value recall_at_100 69.314 type value recall_at_1000 87.397 type value recall_at_3 34.724 type value recall_at_5 39.925 task dataset metrics type Retrieval type name config split revision BeIR/cqadupstack MTEB CQADupstackWebmastersRetrieval default test None type value map_at_1 22.091 type value map_at_10 29.948999999999998 type value map_at_100 31.502000000000002 type value map_at_1000 31.713 type value map_at_3 27.464 type value map_at_5 28.968 type value mrr_at_1 26.482 type value mrr_at_10 34.009 type value mrr_at_100 35.081 type value mrr_at_1000 35.138000000000005 type value mrr_at_3 31.785000000000004 type value mrr_at_5 33.178999999999995 type value ndcg_at_1 26.482 type value ndcg_at_10 35.008 type value ndcg_at_100 41.272999999999996 type value ndcg_at_1000 43.972 type value ndcg_at_3 30.804 type value ndcg_at_5 33.046 type value precision_at_1 26.482 type value precision_at_10 6.462 type value precision_at_100 1.431 type value precision_at_1000 0.22899999999999998 type value precision_at_3 14.360999999999999 type value precision_at_5 10.474 type value recall_at_1 22.091 type value recall_at_10 45.125 type value recall_at_100 72.313 type value recall_at_1000 89.503 type value recall_at_3 33.158 type value recall_at_5 39.086999999999996 task dataset metrics type Retrieval type name config split revision BeIR/cqadupstack MTEB CQADupstackWordpressRetrieval default test None type value map_at_1 19.883 type value map_at_10 26.951000000000004 type value map_at_100 27.927999999999997 type value map_at_1000 28.022000000000002 type value map_at_3 24.616 type value map_at_5 25.917 type value mrr_at_1 21.996 type value mrr_at_10 29.221000000000004 type value mrr_at_100 30.024 type value mrr_at_1000 30.095 type value mrr_at_3 26.833000000000002 type value mrr_at_5 28.155 type value ndcg_at_1 21.996 type value ndcg_at_10 31.421 type value ndcg_at_100 36.237 type value ndcg_at_1000 38.744 type value ndcg_at_3 26.671 type value ndcg_at_5 28.907 type value precision_at_1 21.996 type value precision_at_10 5.009 type value precision_at_100 0.799 type value precision_at_1000 0.11199999999999999 type value precision_at_3 11.275 type value precision_at_5 8.059 type value recall_at_1 19.883 type value recall_at_10 43.132999999999996 type value recall_at_100 65.654 type value recall_at_1000 84.492 type value recall_at_3 30.209000000000003 type value recall_at_5 35.616 task dataset metrics type Retrieval type name config split revision climate-fever MTEB ClimateFEVER default test None type value map_at_1 17.756 type value map_at_10 30.378 type value map_at_100 32.537 type value map_at_1000 32.717 type value map_at_3 25.599 type value map_at_5 28.372999999999998 type value mrr_at_1 41.303 type value mrr_at_10 53.483999999999995 type value mrr_at_100 54.106 type value mrr_at_1000 54.127 type value mrr_at_3 50.315 type value mrr_at_5 52.396 type value ndcg_at_1 41.303 type value ndcg_at_10 40.503 type value ndcg_at_100 47.821000000000005 type value ndcg_at_1000 50.788 type value ndcg_at_3 34.364 type value ndcg_at_5 36.818 type value precision_at_1 41.303 type value precision_at_10 12.463000000000001 type value precision_at_100 2.037 type value precision_at_1000 0.26 type value precision_at_3 25.798 type value precision_at_5 19.896 type value recall_at_1 17.756 type value recall_at_10 46.102 type value recall_at_100 70.819 type value recall_at_1000 87.21799999999999 type value recall_at_3 30.646 type value recall_at_5 38.022 task dataset metrics type Retrieval type name config split revision dbpedia-entity MTEB DBPedia default test None type value map_at_1 9.033 type value map_at_10 20.584 type value map_at_100 29.518 type value map_at_1000 31.186000000000003 type value map_at_3 14.468 type value map_at_5 17.177 type value mrr_at_1 69.75 type value mrr_at_10 77.025 type value mrr_at_100 77.36699999999999 type value mrr_at_1000 77.373 type value mrr_at_3 75.583 type value mrr_at_5 76.396 type value ndcg_at_1 58.5 type value ndcg_at_10 45.033 type value ndcg_at_100 49.071 type value ndcg_at_1000 56.056 type value ndcg_at_3 49.936 type value ndcg_at_5 47.471999999999994 type value precision_at_1 69.75 type value precision_at_10 35.775 type value precision_at_100 11.594999999999999 type value precision_at_1000 2.062 type value precision_at_3 52.5 type value precision_at_5 45.300000000000004 type value recall_at_1 9.033 type value recall_at_10 26.596999999999998 type value recall_at_100 54.607000000000006 type value recall_at_1000 76.961 type value recall_at_3 15.754999999999999 type value recall_at_5 20.033 task dataset metrics type Classification type name config split revision mteb/emotion MTEB EmotionClassification default test 4f58c6b202a23cf9a4da393831edf4f9183cad37 type value accuracy 48.345000000000006 type value f1 43.4514918068706 task dataset metrics type Retrieval type name config split revision fever MTEB FEVER default test None type value map_at_1 71.29100000000001 type value map_at_10 81.059 type value map_at_100 81.341 type value map_at_1000 81.355 type value map_at_3 79.74799999999999 type value map_at_5 80.612 type value mrr_at_1 76.40299999999999 type value mrr_at_10 84.615 type value mrr_at_100 84.745 type value mrr_at_1000 84.748 type value mrr_at_3 83.776 type value mrr_at_5 84.343 type value ndcg_at_1 76.40299999999999 type value ndcg_at_10 84.981 type value ndcg_at_100 86.00999999999999 type value ndcg_at_1000 86.252 type value ndcg_at_3 82.97 type value ndcg_at_5 84.152 type value precision_at_1 76.40299999999999 type value precision_at_10 10.446 type value precision_at_100 1.1199999999999999 type value precision_at_1000 0.116 type value precision_at_3 32.147999999999996 type value precision_at_5 20.135 type value recall_at_1 71.29100000000001 type value recall_at_10 93.232 type value recall_at_100 97.363 type value recall_at_1000 98.905 type value recall_at_3 87.893 type value recall_at_5 90.804 task dataset metrics type Retrieval type name config split revision fiqa MTEB FiQA2018 default test None type value map_at_1 18.667 type value map_at_10 30.853 type value map_at_100 32.494 type value map_at_1000 32.677 type value map_at_3 26.91 type value map_at_5 29.099000000000004 type value mrr_at_1 37.191 type value mrr_at_10 46.171 type value mrr_at_100 47.056 type value mrr_at_1000 47.099000000000004 type value mrr_at_3 44.059 type value mrr_at_5 45.147 type value ndcg_at_1 37.191 type value ndcg_at_10 38.437 type value ndcg_at_100 44.62 type value ndcg_at_1000 47.795 type value ndcg_at_3 35.003 type value ndcg_at_5 36.006 type value precision_at_1 37.191 type value precision_at_10 10.586 type value precision_at_100 1.688 type value precision_at_1000 0.22699999999999998 type value precision_at_3 23.302 type value precision_at_5 17.006 type value recall_at_1 18.667 type value recall_at_10 45.367000000000004 type value recall_at_100 68.207 type value recall_at_1000 87.072 type value recall_at_3 32.129000000000005 type value recall_at_5 37.719 task dataset metrics type Retrieval type name config split revision hotpotqa MTEB HotpotQA default test None type value map_at_1 39.494 type value map_at_10 66.223 type value map_at_100 67.062 type value map_at_1000 67.11500000000001 type value map_at_3 62.867 type value map_at_5 64.994 type value mrr_at_1 78.987 type value mrr_at_10 84.585 type value mrr_at_100 84.773 type value mrr_at_1000 84.77900000000001 type value mrr_at_3 83.592 type value mrr_at_5 84.235 type value ndcg_at_1 78.987 type value ndcg_at_10 73.64 type value ndcg_at_100 76.519 type value ndcg_at_1000 77.51 type value ndcg_at_3 68.893 type value ndcg_at_5 71.585 type value precision_at_1 78.987 type value precision_at_10 15.529000000000002 type value precision_at_100 1.7770000000000001 type value precision_at_1000 0.191 type value precision_at_3 44.808 type value precision_at_5 29.006999999999998 type value recall_at_1 39.494 type value recall_at_10 77.643 type value recall_at_100 88.825 type value recall_at_1000 95.321 type value recall_at_3 67.211 type value recall_at_5 72.519 task dataset metrics type Classification type name config split revision mteb/imdb MTEB ImdbClassification default test 3d86128a09e091d6018b6d26cad27f2739fc2db7 type value accuracy 85.55959999999999 type value ap 80.7246500384617 type value f1 85.52336485065454 task dataset metrics type Retrieval type name config split revision msmarco MTEB MSMARCO default dev None type value map_at_1 23.631 type value map_at_10 36.264 type value map_at_100 37.428 type value map_at_1000 37.472 type value map_at_3 32.537 type value map_at_5 34.746 type value mrr_at_1 24.312 type value mrr_at_10 36.858000000000004 type value mrr_at_100 37.966 type value mrr_at_1000 38.004 type value mrr_at_3 33.188 type value mrr_at_5 35.367 type value ndcg_at_1 24.312 type value ndcg_at_10 43.126999999999995 type value ndcg_at_100 48.642 type value ndcg_at_1000 49.741 type value ndcg_at_3 35.589 type value ndcg_at_5 39.515 type value precision_at_1 24.312 type value precision_at_10 6.699 type value precision_at_100 0.9450000000000001 type value precision_at_1000 0.104 type value precision_at_3 15.153 type value precision_at_5 11.065999999999999 type value recall_at_1 23.631 type value recall_at_10 64.145 type value recall_at_100 89.41 type value recall_at_1000 97.83500000000001 type value recall_at_3 43.769000000000005 type value recall_at_5 53.169 task dataset metrics type Classification type name config split revision mteb/mtop_domain MTEB MTOPDomainClassification (en) en test d80d48c1eb48d3562165c59d59d0034df9fff0bf type value accuracy 93.4108527131783 type value f1 93.1415880261038 task dataset metrics type Classification type name config split revision mteb/mtop_intent MTEB MTOPIntentClassification (en) en test ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba type value accuracy 77.24806201550388 type value f1 60.531916308197175 task dataset metrics type Classification type name config split revision mteb/amazon_massive_intent MTEB MassiveIntentClassification (en) en test 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 type value accuracy 73.71553463349024 type value f1 71.70753174900791 task dataset metrics type Classification type name config split revision mteb/amazon_massive_scenario MTEB MassiveScenarioClassification (en) en test 7d571f92784cd94a019292a1f45445077d0ef634 type value accuracy 77.79757901815736 type value f1 77.83719850433258 task dataset metrics type Clustering type name config split revision mteb/medrxiv-clustering-p2p MTEB MedrxivClusteringP2P default test e7a26af6f3ae46b30dde8737f02c07b1505bcc73 type value v_measure 33.74193296622113 task dataset metrics type Clustering type name config split revision mteb/medrxiv-clustering-s2s MTEB MedrxivClusteringS2S default test 35191c8c0dca72d8ff3efcd72aa802307d469663 type value v_measure 30.64257594108566 task dataset metrics type Reranking type name config split revision mteb/mind_small MTEB MindSmallReranking default test 3bdac13927fdc888b903db93b2ffdbd90b295a69 type value map 30.811018518883625 type value mrr 31.910376577445003 task dataset metrics type Retrieval type name config split revision nfcorpus MTEB NFCorpus default test None type value map_at_1 5.409 type value map_at_10 13.093 type value map_at_100 16.256999999999998 type value map_at_1000 17.617 type value map_at_3 9.555 type value map_at_5 11.428 type value mrr_at_1 45.201 type value mrr_at_10 54.179 type value mrr_at_100 54.812000000000005 type value mrr_at_1000 54.840999999999994 type value mrr_at_3 51.909000000000006 type value mrr_at_5 53.519000000000005 type value ndcg_at_1 43.189 type value ndcg_at_10 35.028 type value ndcg_at_100 31.226 type value ndcg_at_1000 39.678000000000004 type value ndcg_at_3 40.596 type value ndcg_at_5 38.75 type value precision_at_1 44.582 type value precision_at_10 25.974999999999998 type value precision_at_100 7.793 type value precision_at_1000 2.036 type value precision_at_3 38.493 type value precision_at_5 33.994 type value recall_at_1 5.409 type value recall_at_10 16.875999999999998 type value recall_at_100 30.316 type value recall_at_1000 60.891 type value recall_at_3 10.688 type value recall_at_5 13.832 task dataset metrics type Retrieval type name config split revision nq MTEB NQ default test None type value map_at_1 36.375 type value map_at_10 51.991 type value map_at_100 52.91400000000001 type value map_at_1000 52.93600000000001 type value map_at_3 48.014 type value map_at_5 50.381 type value mrr_at_1 40.759 type value mrr_at_10 54.617000000000004 type value mrr_at_100 55.301 type value mrr_at_1000 55.315000000000005 type value mrr_at_3 51.516 type value mrr_at_5 53.435 type value ndcg_at_1 40.759 type value ndcg_at_10 59.384 type value ndcg_at_100 63.157 type value ndcg_at_1000 63.654999999999994 type value ndcg_at_3 52.114000000000004 type value ndcg_at_5 55.986000000000004 type value precision_at_1 40.759 type value precision_at_10 9.411999999999999 type value precision_at_100 1.153 type value precision_at_1000 0.12 type value precision_at_3 23.329 type value precision_at_5 16.256999999999998 type value recall_at_1 36.375 type value recall_at_10 79.053 type value recall_at_100 95.167 type value recall_at_1000 98.82 type value recall_at_3 60.475 type value recall_at_5 69.327 task dataset metrics type Retrieval type name config split revision quora MTEB QuoraRetrieval default test None type value map_at_1 70.256 type value map_at_10 83.8 type value map_at_100 84.425 type value map_at_1000 84.444 type value map_at_3 80.906 type value map_at_5 82.717 type value mrr_at_1 80.97999999999999 type value mrr_at_10 87.161 type value mrr_at_100 87.262 type value mrr_at_1000 87.263 type value mrr_at_3 86.175 type value mrr_at_5 86.848 type value ndcg_at_1 80.97999999999999 type value ndcg_at_10 87.697 type value ndcg_at_100 88.959 type value ndcg_at_1000 89.09899999999999 type value ndcg_at_3 84.83800000000001 type value ndcg_at_5 86.401 type value precision_at_1 80.97999999999999 type value precision_at_10 13.261000000000001 type value precision_at_100 1.5150000000000001 type value precision_at_1000 0.156 type value precision_at_3 37.01 type value precision_at_5 24.298000000000002 type value recall_at_1 70.256 type value recall_at_10 94.935 type value recall_at_100 99.274 type value recall_at_1000 99.928 type value recall_at_3 86.602 type value recall_at_5 91.133 task dataset metrics type Clustering type name config split revision mteb/reddit-clustering MTEB RedditClustering default test 24640382cdbf8abc73003fb0fa6d111a705499eb type value v_measure 56.322692497613104 task dataset metrics type Clustering type name config split revision mteb/reddit-clustering-p2p MTEB RedditClusteringP2P default test 282350215ef01743dc01b456c7f5241fa8937f16 type value v_measure 61.895813503775074 task dataset metrics type Retrieval type name config split revision scidocs MTEB SCIDOCS default test None type value map_at_1 4.338 type value map_at_10 10.767 type value map_at_100 12.537999999999998 type value map_at_1000 12.803999999999998 type value map_at_3 7.788 type value map_at_5 9.302000000000001 type value mrr_at_1 21.4 type value mrr_at_10 31.637999999999998 type value mrr_at_100 32.688 type value mrr_at_1000 32.756 type value mrr_at_3 28.433000000000003 type value mrr_at_5 30.178 type value ndcg_at_1 21.4 type value ndcg_at_10 18.293 type value ndcg_at_100 25.274 type value ndcg_at_1000 30.284 type value ndcg_at_3 17.391000000000002 type value ndcg_at_5 15.146999999999998 type value precision_at_1 21.4 type value precision_at_10 9.48 type value precision_at_100 1.949 type value precision_at_1000 0.316 type value precision_at_3 16.167 type value precision_at_5 13.22 type value recall_at_1 4.338 type value recall_at_10 19.213 type value recall_at_100 39.562999999999995 type value recall_at_1000 64.08 type value recall_at_3 9.828000000000001 type value recall_at_5 13.383000000000001 task dataset metrics type STS type name config split revision mteb/sickr-sts MTEB SICK-R default test a6ea5a8cab320b040a23452cc28066d9beae2cee type value cos_sim_pearson 82.42568163642142 type value cos_sim_spearman 78.5797159641342 type value euclidean_pearson 80.22151260811604 type value euclidean_spearman 78.5797151953878 type value manhattan_pearson 80.21224215864788 type value manhattan_spearman 78.55641478381344 task dataset metrics type STS type name config split revision mteb/sts12-sts MTEB STS12 default test a0d554a64d88156834ff5ae9920b964011b16384 type value cos_sim_pearson 85.44020710812569 type value cos_sim_spearman 78.91631735081286 type value euclidean_pearson 81.64188964182102 type value euclidean_spearman 78.91633286881678 type value manhattan_pearson 81.69294748512496 type value manhattan_spearman 78.93438558002656 task dataset metrics type STS type name config split revision mteb/sts13-sts MTEB STS13 default test 7e90230a92c190f1bf69ae9002b8cea547a64cca type value cos_sim_pearson 84.27165426412311 type value cos_sim_spearman 85.40429140249618 type value euclidean_pearson 84.7509580724893 type value euclidean_spearman 85.40429140249618 type value manhattan_pearson 84.76488289321308 type value manhattan_spearman 85.4256793698708 task dataset metrics type STS type name config split revision mteb/sts14-sts MTEB STS14 default test 6031580fec1f6af667f0bd2da0a551cf4f0b2375 type value cos_sim_pearson 83.138851760732 type value cos_sim_spearman 81.64101363896586 type value euclidean_pearson 82.55165038934942 type value euclidean_spearman 81.64105257080502 type value manhattan_pearson 82.52802949883335 type value manhattan_spearman 81.61255430718158 task dataset metrics type STS type name config split revision mteb/sts15-sts MTEB STS15 default test ae752c7c21bf194d8b67fd573edf7ae58183cbe3 type value cos_sim_pearson 86.0654695484029 type value cos_sim_spearman 87.20408521902229 type value euclidean_pearson 86.8110651362115 type value euclidean_spearman 87.20408521902229 type value manhattan_pearson 86.77984656478691 type value manhattan_spearman 87.1719947099227 task dataset metrics type STS type name config split revision mteb/sts16-sts MTEB STS16 default test 4d8694f8f0e0100860b497b999b3dbed754a0513 type value cos_sim_pearson 83.77823915496512 type value cos_sim_spearman 85.43566325729779 type value euclidean_pearson 84.5396956658821 type value euclidean_spearman 85.43566325729779 type value manhattan_pearson 84.5665398848169 type value manhattan_spearman 85.44375870303232 task dataset metrics type STS type name config split revision mteb/sts17-crosslingual-sts MTEB STS17 (en-en) en-en test af5e6fb845001ecf41f4c1e033ce921939a2a68d type value cos_sim_pearson 87.20030208471798 type value cos_sim_spearman 87.20485505076539 type value euclidean_pearson 88.10588324368722 type value euclidean_spearman 87.20485505076539 type value manhattan_pearson 87.92324770415183 type value manhattan_spearman 87.0571314561877 task dataset metrics type STS type name config split revision mteb/sts22-crosslingual-sts MTEB STS22 (en) en test 6d1ba47164174a496b7fa5d3569dae26a6813b80 type value cos_sim_pearson 63.06093161604453 type value cos_sim_spearman 64.2163140357722 type value euclidean_pearson 65.27589680994006 type value euclidean_spearman 64.2163140357722 type value manhattan_pearson 65.45904383711101 type value manhattan_spearman 64.55404716679305 task dataset metrics type STS type name config split revision mteb/stsbenchmark-sts MTEB STSBenchmark default test b0fddb56ed78048fa8b90373c8a3cfc37b684831 type value cos_sim_pearson 84.32976164578706 type value cos_sim_spearman 85.54302197678368 type value euclidean_pearson 85.26307149193056 type value euclidean_spearman 85.54302197678368 type value manhattan_pearson 85.26647282029371 type value manhattan_spearman 85.5316135265568 task dataset metrics type Reranking type name config split revision mteb/scidocs-reranking MTEB SciDocsRR default test d3c5e1fc0b855ab6097bf1cda04dd73947d7caab type value map 81.44675968318754 type value mrr 94.92741826075158 task dataset metrics type Retrieval type name config split revision scifact MTEB SciFact default test None type value map_at_1 56.34400000000001 type value map_at_10 65.927 type value map_at_100 66.431 type value map_at_1000 66.461 type value map_at_3 63.529 type value map_at_5 64.818 type value mrr_at_1 59.333000000000006 type value mrr_at_10 67.54599999999999 type value mrr_at_100 67.892 type value mrr_at_1000 67.917 type value mrr_at_3 65.778 type value mrr_at_5 66.794 type value ndcg_at_1 59.333000000000006 type value ndcg_at_10 70.5 type value ndcg_at_100 72.688 type value ndcg_at_1000 73.483 type value ndcg_at_3 66.338 type value ndcg_at_5 68.265 type value precision_at_1 59.333000000000006 type value precision_at_10 9.3 type value precision_at_100 1.053 type value precision_at_1000 0.11199999999999999 type value precision_at_3 25.889 type value precision_at_5 16.866999999999997 type value recall_at_1 56.34400000000001 type value recall_at_10 82.789 type value recall_at_100 92.767 type value recall_at_1000 99 type value recall_at_3 71.64399999999999 type value recall_at_5 76.322 task dataset metrics type PairClassification type name config split revision mteb/sprintduplicatequestions-pairclassification MTEB SprintDuplicateQuestions default test d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 type value cos_sim_accuracy 99.75742574257426 type value cos_sim_ap 93.52081548447406 type value cos_sim_f1 87.33850129198966 type value cos_sim_precision 90.37433155080214 type value cos_sim_recall 84.5 type value dot_accuracy 99.75742574257426 type value dot_ap 93.52081548447406 type value dot_f1 87.33850129198966 type value dot_precision 90.37433155080214 type value dot_recall 84.5 type value euclidean_accuracy 99.75742574257426 type value euclidean_ap 93.52081548447406 type value euclidean_f1 87.33850129198966 type value euclidean_precision 90.37433155080214 type value euclidean_recall 84.5 type value manhattan_accuracy 99.75841584158415 type value manhattan_ap 93.4975678585854 type value manhattan_f1 87.26708074534162 type value manhattan_precision 90.45064377682404 type value manhattan_recall 84.3 type value max_accuracy 99.75841584158415 type value max_ap 93.52081548447406 type value max_f1 87.33850129198966 task dataset metrics type Clustering type name config split revision mteb/stackexchange-clustering MTEB StackExchangeClustering default test 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 type value v_measure 64.31437036686651 task dataset metrics type Clustering type name config split revision mteb/stackexchange-clustering-p2p MTEB StackExchangeClusteringP2P default test 815ca46b2622cec33ccafc3735d572c266efdb44 type value v_measure 33.25569319007206 task dataset metrics type Reranking type name config split revision mteb/stackoverflowdupquestions-reranking MTEB StackOverflowDupQuestions default test e185fbe320c72810689fc5848eb6114e1ef5ec69 type value map 49.90474939720706 type value mrr 50.568115503777264 task dataset metrics type Summarization type name config split revision mteb/summeval MTEB SummEval default test cda12ad7615edc362dbf25a00fdd61d3b1eaf93c type value cos_sim_pearson 29.866828641244712 type value cos_sim_spearman 30.077555055873866 type value dot_pearson 29.866832988572266 type value dot_spearman 30.077555055873866 task dataset metrics type Retrieval type name config split revision trec-covid MTEB TRECCOVID default test None type value map_at_1 0.232 type value map_at_10 2.094 type value map_at_100 11.971 type value map_at_1000 28.158 type value map_at_3 0.688 type value map_at_5 1.114 type value mrr_at_1 88 type value mrr_at_10 93.4 type value mrr_at_100 93.4 type value mrr_at_1000 93.4 type value mrr_at_3 93 type value mrr_at_5 93.4 type value ndcg_at_1 84 type value ndcg_at_10 79.923 type value ndcg_at_100 61.17 type value ndcg_at_1000 53.03 type value ndcg_at_3 84.592 type value ndcg_at_5 82.821 type value precision_at_1 88 type value precision_at_10 85 type value precision_at_100 63.019999999999996 type value precision_at_1000 23.554 type value precision_at_3 89.333 type value precision_at_5 87.2 type value recall_at_1 0.232 type value recall_at_10 2.255 type value recall_at_100 14.823 type value recall_at_1000 49.456 type value recall_at_3 0.718 type value recall_at_5 1.175 task dataset metrics type Retrieval type name config split revision webis-touche2020 MTEB Touche2020 default test None type value map_at_1 2.547 type value map_at_10 11.375 type value map_at_100 18.194 type value map_at_1000 19.749 type value map_at_3 5.825 type value map_at_5 8.581 type value mrr_at_1 32.653 type value mrr_at_10 51.32 type value mrr_at_100 51.747 type value mrr_at_1000 51.747 type value mrr_at_3 47.278999999999996 type value mrr_at_5 48.605 type value ndcg_at_1 29.592000000000002 type value ndcg_at_10 28.151 type value ndcg_at_100 39.438 type value ndcg_at_1000 50.769 type value ndcg_at_3 30.758999999999997 type value ndcg_at_5 30.366 type value precision_at_1 32.653 type value precision_at_10 25.714 type value precision_at_100 8.041 type value precision_at_1000 1.555 type value precision_at_3 33.333 type value precision_at_5 31.837 type value recall_at_1 2.547 type value recall_at_10 18.19 type value recall_at_100 49.538 type value recall_at_1000 83.86 type value recall_at_3 7.329 type value recall_at_5 11.532 task dataset metrics type Classification type name config split revision mteb/toxic_conversations_50k MTEB ToxicConversationsClassification default test d7c0de2777da35d6aae2200a62c6e0e5af397c4c type value accuracy 71.4952 type value ap 14.793362635531409 type value f1 55.204635551516915 task dataset metrics type Classification type name config split revision mteb/tweet_sentiment_extraction MTEB TweetSentimentExtractionClassification default test d604517c81ca91fe16a244d1248fc021f9ecee7a type value accuracy 61.5365025466893 type value f1 61.81742556334845 task dataset metrics type Clustering type name config split revision mteb/twentynewsgroups-clustering MTEB TwentyNewsgroupsClustering default test 6125ec4e24fa026cec8a478383ee943acfbd5449 type value v_measure 49.05531070301185 task dataset metrics type PairClassification type name config split revision mteb/twittersemeval2015-pairclassification MTEB TwitterSemEval2015 default test 70970daeab8776df92f5ea462b6173c0b46fd2d1 type value cos_sim_accuracy 86.51725576682364 type value cos_sim_ap 75.2292304265163 type value cos_sim_f1 69.54022988505749 type value cos_sim_precision 63.65629110039457 type value cos_sim_recall 76.62269129287598 type value dot_accuracy 86.51725576682364 type value dot_ap 75.22922386081054 type value dot_f1 69.54022988505749 type value dot_precision 63.65629110039457 type value dot_recall 76.62269129287598 type value euclidean_accuracy 86.51725576682364 type value euclidean_ap 75.22925730473472 type value euclidean_f1 69.54022988505749 type value euclidean_precision 63.65629110039457 type value euclidean_recall 76.62269129287598 type value manhattan_accuracy 86.52321630804077 type value manhattan_ap 75.20608115037336 type value manhattan_f1 69.60000000000001 type value manhattan_precision 64.37219730941705 type value manhattan_recall 75.75197889182058 type value max_accuracy 86.52321630804077 type value max_ap 75.22925730473472 type value max_f1 69.60000000000001 task dataset metrics type PairClassification type name config split revision mteb/twitterurlcorpus-pairclassification MTEB TwitterURLCorpus default test 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf type value cos_sim_accuracy 89.34877944657896 type value cos_sim_ap 86.71257569277373 type value cos_sim_f1 79.10386355986088 type value cos_sim_precision 76.91468470434214 type value cos_sim_recall 81.4213119802895 type value dot_accuracy 89.34877944657896 type value dot_ap 86.71257133133368 type value dot_f1 79.10386355986088 type value dot_precision 76.91468470434214 type value dot_recall 81.4213119802895 type value euclidean_accuracy 89.34877944657896 type value euclidean_ap 86.71257651501476 type value euclidean_f1 79.10386355986088 type value euclidean_precision 76.91468470434214 type value euclidean_recall 81.4213119802895 type value manhattan_accuracy 89.35848177901967 type value manhattan_ap 86.69330615469126 type value manhattan_f1 79.13867741453949 type value manhattan_precision 76.78881807647741 type value manhattan_recall 81.63689559593472 type value max_accuracy 89.35848177901967 type value max_ap 86.71257651501476 type value max_f1 79.13867741453949	apache-2.0	en	nomic-ai/nomic-embed-text-v1.5

nomic-embed-text-v1: A Reproducible Long Context (8192) Text Embedder

nomic-embed-text-v1 is 8192 context length text encoder that surpasses OpenAI text-embedding-ada-002 and text-embedding-3-small performance on short and long context tasks.

Performance Benchmarks

Name	SeqLen	MTEB	LoCo	Jina Long Context	Open Weights	Open Training Code	Open Data
nomic-embed-text-v1	8192	62.39	85.53	54.16	✅	✅	✅
jina-embeddings-v2-base-en	8192	60.39	85.45	51.90	✅	❌	❌
text-embedding-3-small	8191	62.26	82.40	58.20	❌	❌	❌
text-embedding-ada-002	8191	60.99	52.7	55.25	❌	❌	❌

Exciting Update!: nomic-embed-text-v1 is now multimodal! nomic-embed-vision-v1 is aligned to the embedding space of nomic-embed-text-v1, meaning any text embedding is multimodal!

Usage

Important: the text prompt must include a task instruction prefix, instructing the model which task is being performed.

For example, if you are implementing a RAG application, you embed your documents as search_document: <text here> and embed your user queries as search_query: <text here>.

Task instruction prefixes

search_document

Purpose: embed texts as documents from a dataset

This prefix is used for embedding texts as documents, for example as documents for a RAG index.

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/nomic-embed-text-v1", trust_remote_code=True)
sentences = ['search_document: TSNE is a dimensionality reduction algorithm created by Laurens van Der Maaten']
embeddings = model.encode(sentences)
print(embeddings)

search_query

Purpose: embed texts as questions to answer

This prefix is used for embedding texts as questions that documents from a dataset could resolve, for example as queries to be answered by a RAG application.

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/nomic-embed-text-v1", trust_remote_code=True)
sentences = ['search_query: Who is Laurens van Der Maaten?']
embeddings = model.encode(sentences)
print(embeddings)

clustering

Purpose: embed texts to group them into clusters

This prefix is used for embedding texts in order to group them into clusters, discover common topics, or remove semantic duplicates.

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/nomic-embed-text-v1", trust_remote_code=True)
sentences = ['clustering: the quick brown fox']
embeddings = model.encode(sentences)
print(embeddings)

classification

Purpose: embed texts to classify them

This prefix is used for embedding texts into vectors that will be used as features for a classification model

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/nomic-embed-text-v1", trust_remote_code=True)
sentences = ['classification: the quick brown fox']
embeddings = model.encode(sentences)
print(embeddings)

Sentence Transformers

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("nomic-ai/nomic-embed-text-v1", trust_remote_code=True)
sentences = ['search_query: What is TSNE?', 'search_query: Who is Laurens van der Maaten?']
embeddings = model.encode(sentences)
print(embeddings)

Transformers

import torch
import torch.nn.functional as F
from transformers import AutoTokenizer, AutoModel

def mean_pooling(model_output, attention_mask):
    token_embeddings = model_output[0]
    input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
    return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)

sentences = ['search_query: What is TSNE?', 'search_query: Who is Laurens van der Maaten?']

tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1', trust_remote_code=True)
model.eval()

encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

with torch.no_grad():
    model_output = model(**encoded_input)

embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
embeddings = F.normalize(embeddings, p=2, dim=1)
print(embeddings)

The model natively supports scaling of the sequence length past 2048 tokens. To do so,

- tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
+ tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', model_max_length=8192)


- model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1', trust_remote_code=True)
+ model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1', trust_remote_code=True, rotary_scaling_factor=2)

Transformers.js

import { pipeline } from '@xenova/transformers';

// Create a feature extraction pipeline
const extractor = await pipeline('feature-extraction', 'nomic-ai/nomic-embed-text-v1', {
    quantized: false, // Comment out this line to use the quantized version
});

// Compute sentence embeddings
const texts = ['search_query: What is TSNE?', 'search_query: Who is Laurens van der Maaten?'];
const embeddings = await extractor(texts, { pooling: 'mean', normalize: true });
console.log(embeddings);

Nomic API

The easiest way to get started with Nomic Embed is through the Nomic Embedding API.

Generating embeddings with the nomic Python client is as easy as

from nomic import embed

output = embed.text(
    texts=['Nomic Embedding API', '#keepAIOpen'],
    model='nomic-embed-text-v1',
    task_type='search_document'
)

print(output)

For more information, see the API reference

Training

Click the Nomic Atlas map below to visualize a 5M sample of our contrastive pretraining data!

We train our embedder using a multi-stage training pipeline. Starting from a long-context BERT model, the first unsupervised contrastive stage trains on a dataset generated from weakly related text pairs, such as question-answer pairs from forums like StackExchange and Quora, title-body pairs from Amazon reviews, and summarizations from news articles.

In the second finetuning stage, higher quality labeled datasets such as search queries and answers from web searches are leveraged. Data curation and hard-example mining is crucial in this stage.

For more details, see the Nomic Embed Technical Report and corresponding blog post.

Training data to train the models is released in its entirety. For more details, see the contrastors repository

Join the Nomic Community

• Nomic: https://nomic.ai
• Discord: https://discord.gg/myY5YDR8z8
• Twitter: https://twitter.com/nomic_ai

Citation

If you find the model, dataset, or training code useful, please cite our work

@misc{nussbaum2024nomic,
      title={Nomic Embed: Training a Reproducible Long Context Text Embedder}, 
      author={Zach Nussbaum and John X. Morris and Brandon Duderstadt and Andriy Mulyar},
      year={2024},
      eprint={2402.01613},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}