--- language: - en license: mit tags: - mteb - sentence-transformers - transformers model-index: - name: e5-mistral-7b-instruct results: - task: type: STS dataset: name: MTEB AFQMC type: C-MTEB/AFQMC config: default split: validation revision: None metrics: - type: cos_sim_pearson value: 37.863226091673866 - type: cos_sim_spearman value: 38.98733013335281 - type: euclidean_pearson value: 37.51783380497874 - type: euclidean_spearman value: 38.98733012753365 - type: manhattan_pearson value: 37.26706888081721 - type: manhattan_spearman value: 38.709750161903834 - task: type: STS dataset: name: MTEB ATEC type: C-MTEB/ATEC config: default split: test revision: None metrics: - type: cos_sim_pearson value: 43.33924583134623 - type: cos_sim_spearman value: 42.84316155158754 - type: euclidean_pearson value: 45.62709879515238 - type: euclidean_spearman value: 42.843155921732404 - type: manhattan_pearson value: 45.4786950991229 - type: manhattan_spearman value: 42.657334751855984 - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en) type: mteb/amazon_counterfactual config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 78.68656716417911 - type: ap value: 41.71522322900398 - type: f1 value: 72.37207703532552 - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (de) type: mteb/amazon_counterfactual config: de split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 74.04710920770879 - type: ap value: 83.42622221864045 - type: f1 value: 72.14388257905772 - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (en-ext) type: mteb/amazon_counterfactual config: en-ext split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 77.93103448275862 - type: ap value: 26.039284760509513 - type: f1 value: 64.81092954450712 - task: type: Classification dataset: name: MTEB AmazonCounterfactualClassification (ja) type: mteb/amazon_counterfactual config: ja split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 77.21627408993577 - type: ap value: 24.876490553983036 - type: f1 value: 63.8773359684989 - task: type: Classification dataset: name: MTEB AmazonPolarityClassification type: mteb/amazon_polarity config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 95.90679999999999 - type: ap value: 94.32357863164454 - type: f1 value: 95.90485634708557 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (en) type: mteb/amazon_reviews_multi config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 55.786 - type: f1 value: 55.31211995815146 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (de) type: mteb/amazon_reviews_multi config: de split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 53.26 - type: f1 value: 52.156230111544986 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (es) type: mteb/amazon_reviews_multi config: es split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 50.33 - type: f1 value: 49.195023008878145 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (fr) type: mteb/amazon_reviews_multi config: fr split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 49.3 - type: f1 value: 48.434470184108 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (ja) type: mteb/amazon_reviews_multi config: ja split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 48.68599999999999 - type: f1 value: 47.62681775202072 - task: type: Classification dataset: name: MTEB AmazonReviewsClassification (zh) type: mteb/amazon_reviews_multi config: zh split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 46.238 - type: f1 value: 45.014030559653705 - task: type: Retrieval dataset: name: MTEB ArguAna type: arguana config: default split: test revision: None metrics: - type: map_at_1 value: 36.486000000000004 - type: map_at_10 value: 53.076 - type: map_at_100 value: 53.657999999999994 - type: map_at_1000 value: 53.659 - type: map_at_3 value: 48.234 - type: map_at_5 value: 51.121 - type: mrr_at_1 value: 37.269000000000005 - type: mrr_at_10 value: 53.335 - type: mrr_at_100 value: 53.916 - type: mrr_at_1000 value: 53.918 - type: mrr_at_3 value: 48.518 - type: mrr_at_5 value: 51.406 - type: ndcg_at_1 value: 36.486000000000004 - type: ndcg_at_10 value: 61.882000000000005 - type: ndcg_at_100 value: 64.165 - type: ndcg_at_1000 value: 64.203 - type: ndcg_at_3 value: 52.049 - type: ndcg_at_5 value: 57.199 - type: precision_at_1 value: 36.486000000000004 - type: precision_at_10 value: 8.982999999999999 - type: precision_at_100 value: 0.9939999999999999 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 21.029 - type: precision_at_5 value: 15.092 - type: recall_at_1 value: 36.486000000000004 - type: recall_at_10 value: 89.82900000000001 - type: recall_at_100 value: 99.36 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 63.087 - type: recall_at_5 value: 75.46199999999999 - task: type: Clustering dataset: name: MTEB ArxivClusteringP2P type: mteb/arxiv-clustering-p2p config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 50.45119266859667 - task: type: Clustering dataset: name: MTEB ArxivClusteringS2S type: mteb/arxiv-clustering-s2s config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 45.4958298992051 - task: type: Reranking dataset: name: MTEB AskUbuntuDupQuestions type: mteb/askubuntudupquestions-reranking config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 66.98177472838887 - type: mrr value: 79.91854636591478 - task: type: STS dataset: name: MTEB BIOSSES type: mteb/biosses-sts config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 87.67086498650698 - type: cos_sim_spearman value: 85.54773239564638 - type: euclidean_pearson value: 86.48229161588425 - type: euclidean_spearman value: 85.54773239564638 - type: manhattan_pearson value: 86.67533327742343 - type: manhattan_spearman value: 85.76099026691983 - task: type: STS dataset: name: MTEB BQ type: C-MTEB/BQ config: default split: test revision: None metrics: - type: cos_sim_pearson value: 50.31998888922809 - type: cos_sim_spearman value: 50.6369940530675 - type: euclidean_pearson value: 50.055544636296055 - type: euclidean_spearman value: 50.63699405154838 - type: manhattan_pearson value: 50.00739378036807 - type: manhattan_spearman value: 50.607237418676945 - task: type: BitextMining dataset: name: MTEB BUCC (de-en) type: mteb/bucc-bitext-mining config: de-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 99.5615866388309 - type: f1 value: 99.49895615866389 - type: precision value: 99.46764091858039 - type: recall value: 99.5615866388309 - task: type: BitextMining dataset: name: MTEB BUCC (fr-en) type: mteb/bucc-bitext-mining config: fr-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 99.19656614571869 - type: f1 value: 99.08650671362535 - type: precision value: 99.0314769975787 - type: recall value: 99.19656614571869 - task: type: BitextMining dataset: name: MTEB BUCC (ru-en) type: mteb/bucc-bitext-mining config: ru-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 98.0256321440942 - type: f1 value: 97.83743216718624 - type: precision value: 97.74390947927492 - type: recall value: 98.0256321440942 - task: type: BitextMining dataset: name: MTEB BUCC (zh-en) type: mteb/bucc-bitext-mining config: zh-en split: test revision: d51519689f32196a32af33b075a01d0e7c51e252 metrics: - type: accuracy value: 99.26276987888363 - type: f1 value: 99.22766368264 - type: precision value: 99.21011058451816 - type: recall value: 99.26276987888363 - task: type: Classification dataset: name: MTEB Banking77Classification type: mteb/banking77 config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 88.22727272727272 - type: f1 value: 88.17411732496673 - task: type: Clustering dataset: name: MTEB BiorxivClusteringP2P type: mteb/biorxiv-clustering-p2p config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 43.530637846246975 - task: type: Clustering dataset: name: MTEB BiorxivClusteringS2S type: mteb/biorxiv-clustering-s2s config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 40.23505728593893 - task: type: Clustering dataset: name: MTEB CLSClusteringP2P type: C-MTEB/CLSClusteringP2P config: default split: test revision: None metrics: - type: v_measure value: 44.419028279451275 - task: type: Clustering dataset: name: MTEB CLSClusteringS2S type: C-MTEB/CLSClusteringS2S config: default split: test revision: None metrics: - type: v_measure value: 42.5820277929776 - task: type: Reranking dataset: name: MTEB CMedQAv1 type: C-MTEB/CMedQAv1-reranking config: default split: test revision: None metrics: - type: map value: 77.67811726152972 - type: mrr value: 80.99003968253969 - task: type: Reranking dataset: name: MTEB CMedQAv2 type: C-MTEB/CMedQAv2-reranking config: default split: test revision: None metrics: - type: map value: 78.66055354534922 - type: mrr value: 81.66119047619047 - task: type: Retrieval dataset: name: MTEB CQADupstackRetrieval type: BeIR/cqadupstack config: default split: test revision: None metrics: - type: map_at_1 value: 27.162333333333333 - type: map_at_10 value: 37.22291666666667 - type: map_at_100 value: 38.56733333333333 - type: map_at_1000 value: 38.684250000000006 - type: map_at_3 value: 34.22858333333333 - type: map_at_5 value: 35.852500000000006 - type: mrr_at_1 value: 32.459833333333336 - type: mrr_at_10 value: 41.65358333333333 - type: mrr_at_100 value: 42.566916666666664 - type: mrr_at_1000 value: 42.61766666666667 - type: mrr_at_3 value: 39.210499999999996 - type: mrr_at_5 value: 40.582166666666666 - type: ndcg_at_1 value: 32.459833333333336 - type: ndcg_at_10 value: 42.96758333333333 - type: ndcg_at_100 value: 48.5065 - type: ndcg_at_1000 value: 50.556583333333336 - type: ndcg_at_3 value: 38.004416666666664 - type: ndcg_at_5 value: 40.25916666666667 - type: precision_at_1 value: 32.459833333333336 - type: precision_at_10 value: 7.664583333333333 - type: precision_at_100 value: 1.2349999999999999 - type: precision_at_1000 value: 0.15966666666666668 - type: precision_at_3 value: 17.731166666666663 - type: precision_at_5 value: 12.575333333333335 - type: recall_at_1 value: 27.162333333333333 - type: recall_at_10 value: 55.44158333333334 - type: recall_at_100 value: 79.56966666666666 - type: recall_at_1000 value: 93.45224999999999 - type: recall_at_3 value: 41.433083333333336 - type: recall_at_5 value: 47.31108333333333 - task: type: Retrieval dataset: name: MTEB ClimateFEVER type: climate-fever config: default split: test revision: None metrics: - type: map_at_1 value: 16.539 - type: map_at_10 value: 28.494999999999997 - type: map_at_100 value: 30.568 - type: map_at_1000 value: 30.741000000000003 - type: map_at_3 value: 23.846999999999998 - type: map_at_5 value: 26.275 - type: mrr_at_1 value: 37.394 - type: mrr_at_10 value: 50.068 - type: mrr_at_100 value: 50.727 - type: mrr_at_1000 value: 50.751000000000005 - type: mrr_at_3 value: 46.938 - type: mrr_at_5 value: 48.818 - type: ndcg_at_1 value: 37.394 - type: ndcg_at_10 value: 38.349 - type: ndcg_at_100 value: 45.512 - type: ndcg_at_1000 value: 48.321 - type: ndcg_at_3 value: 32.172 - type: ndcg_at_5 value: 34.265 - type: precision_at_1 value: 37.394 - type: precision_at_10 value: 11.927999999999999 - type: precision_at_100 value: 1.966 - type: precision_at_1000 value: 0.25 - type: precision_at_3 value: 24.126 - type: precision_at_5 value: 18.306 - type: recall_at_1 value: 16.539 - type: recall_at_10 value: 44.504 - type: recall_at_100 value: 68.605 - type: recall_at_1000 value: 84.1 - type: recall_at_3 value: 29.008 - type: recall_at_5 value: 35.58 - task: type: Retrieval dataset: name: MTEB CmedqaRetrieval type: C-MTEB/CmedqaRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 19.482 - type: map_at_10 value: 28.622999999999998 - type: map_at_100 value: 30.262 - type: map_at_1000 value: 30.432 - type: map_at_3 value: 25.647 - type: map_at_5 value: 27.128000000000004 - type: mrr_at_1 value: 30.408 - type: mrr_at_10 value: 37.188 - type: mrr_at_100 value: 38.196000000000005 - type: mrr_at_1000 value: 38.273 - type: mrr_at_3 value: 35.067 - type: mrr_at_5 value: 36.124 - type: ndcg_at_1 value: 30.408 - type: ndcg_at_10 value: 34.215 - type: ndcg_at_100 value: 41.349999999999994 - type: ndcg_at_1000 value: 44.689 - type: ndcg_at_3 value: 30.264999999999997 - type: ndcg_at_5 value: 31.572 - type: precision_at_1 value: 30.408 - type: precision_at_10 value: 7.6770000000000005 - type: precision_at_100 value: 1.352 - type: precision_at_1000 value: 0.178 - type: precision_at_3 value: 17.213 - type: precision_at_5 value: 12.198 - type: recall_at_1 value: 19.482 - type: recall_at_10 value: 42.368 - type: recall_at_100 value: 72.694 - type: recall_at_1000 value: 95.602 - type: recall_at_3 value: 30.101 - type: recall_at_5 value: 34.708 - task: type: PairClassification dataset: name: MTEB Cmnli type: C-MTEB/CMNLI config: default split: validation revision: None metrics: - type: cos_sim_accuracy value: 71.16055321707758 - type: cos_sim_ap value: 80.21073839711723 - type: cos_sim_f1 value: 72.9740932642487 - type: cos_sim_precision value: 65.53136050623488 - type: cos_sim_recall value: 82.3240589198036 - type: dot_accuracy value: 71.16055321707758 - type: dot_ap value: 80.212299264122 - type: dot_f1 value: 72.9740932642487 - type: dot_precision value: 65.53136050623488 - type: dot_recall value: 82.3240589198036 - type: euclidean_accuracy value: 71.16055321707758 - type: euclidean_ap value: 80.21076298680417 - type: euclidean_f1 value: 72.9740932642487 - type: euclidean_precision value: 65.53136050623488 - type: euclidean_recall value: 82.3240589198036 - type: manhattan_accuracy value: 70.71557426337944 - type: manhattan_ap value: 79.93448977199749 - type: manhattan_f1 value: 72.83962726826877 - type: manhattan_precision value: 62.7407908077053 - type: manhattan_recall value: 86.81318681318682 - type: max_accuracy value: 71.16055321707758 - type: max_ap value: 80.212299264122 - type: max_f1 value: 72.9740932642487 - task: type: Retrieval dataset: name: MTEB CovidRetrieval type: C-MTEB/CovidRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 60.643 - type: map_at_10 value: 69.011 - type: map_at_100 value: 69.533 - type: map_at_1000 value: 69.545 - type: map_at_3 value: 67.167 - type: map_at_5 value: 68.12700000000001 - type: mrr_at_1 value: 60.801 - type: mrr_at_10 value: 69.111 - type: mrr_at_100 value: 69.6 - type: mrr_at_1000 value: 69.611 - type: mrr_at_3 value: 67.229 - type: mrr_at_5 value: 68.214 - type: ndcg_at_1 value: 60.801 - type: ndcg_at_10 value: 73.128 - type: ndcg_at_100 value: 75.614 - type: ndcg_at_1000 value: 75.92 - type: ndcg_at_3 value: 69.261 - type: ndcg_at_5 value: 70.973 - type: precision_at_1 value: 60.801 - type: precision_at_10 value: 8.662 - type: precision_at_100 value: 0.9860000000000001 - type: precision_at_1000 value: 0.101 - type: precision_at_3 value: 25.149 - type: precision_at_5 value: 15.953999999999999 - type: recall_at_1 value: 60.643 - type: recall_at_10 value: 85.959 - type: recall_at_100 value: 97.576 - type: recall_at_1000 value: 100.0 - type: recall_at_3 value: 75.184 - type: recall_at_5 value: 79.32000000000001 - task: type: Retrieval dataset: name: MTEB DBPedia type: dbpedia-entity config: default split: test revision: None metrics: - type: map_at_1 value: 10.183 - type: map_at_10 value: 23.958 - type: map_at_100 value: 34.354 - type: map_at_1000 value: 36.442 - type: map_at_3 value: 16.345000000000002 - type: map_at_5 value: 19.647000000000002 - type: mrr_at_1 value: 74.25 - type: mrr_at_10 value: 80.976 - type: mrr_at_100 value: 81.256 - type: mrr_at_1000 value: 81.262 - type: mrr_at_3 value: 79.958 - type: mrr_at_5 value: 80.37100000000001 - type: ndcg_at_1 value: 62.0 - type: ndcg_at_10 value: 48.894999999999996 - type: ndcg_at_100 value: 53.867 - type: ndcg_at_1000 value: 61.304 - type: ndcg_at_3 value: 53.688 - type: ndcg_at_5 value: 50.900999999999996 - type: precision_at_1 value: 74.25 - type: precision_at_10 value: 39.525 - type: precision_at_100 value: 12.323 - type: precision_at_1000 value: 2.539 - type: precision_at_3 value: 57.49999999999999 - type: precision_at_5 value: 49.1 - type: recall_at_1 value: 10.183 - type: recall_at_10 value: 29.296 - type: recall_at_100 value: 60.394999999999996 - type: recall_at_1000 value: 83.12 - type: recall_at_3 value: 17.495 - type: recall_at_5 value: 22.235 - task: type: Retrieval dataset: name: MTEB DuRetrieval type: C-MTEB/DuRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 26.613999999999997 - type: map_at_10 value: 79.77300000000001 - type: map_at_100 value: 82.71 - type: map_at_1000 value: 82.75 - type: map_at_3 value: 55.92700000000001 - type: map_at_5 value: 70.085 - type: mrr_at_1 value: 90.7 - type: mrr_at_10 value: 93.438 - type: mrr_at_100 value: 93.504 - type: mrr_at_1000 value: 93.50699999999999 - type: mrr_at_3 value: 93.125 - type: mrr_at_5 value: 93.34 - type: ndcg_at_1 value: 90.7 - type: ndcg_at_10 value: 87.023 - type: ndcg_at_100 value: 90.068 - type: ndcg_at_1000 value: 90.43299999999999 - type: ndcg_at_3 value: 86.339 - type: ndcg_at_5 value: 85.013 - type: precision_at_1 value: 90.7 - type: precision_at_10 value: 41.339999999999996 - type: precision_at_100 value: 4.806 - type: precision_at_1000 value: 0.48900000000000005 - type: precision_at_3 value: 76.983 - type: precision_at_5 value: 64.69 - type: recall_at_1 value: 26.613999999999997 - type: recall_at_10 value: 87.681 - type: recall_at_100 value: 97.44699999999999 - type: recall_at_1000 value: 99.348 - type: recall_at_3 value: 57.809999999999995 - type: recall_at_5 value: 74.258 - task: type: Retrieval dataset: name: MTEB EcomRetrieval type: C-MTEB/EcomRetrieval config: default split: dev revision: None metrics: - type: map_at_1 value: 30.9 - type: map_at_10 value: 40.467 - type: map_at_100 value: 41.423 - type: map_at_1000 value: 41.463 - type: map_at_3 value: 37.25 - type: map_at_5 value: 39.31 - type: mrr_at_1 value: 30.9 - type: mrr_at_10 value: 40.467 - type: mrr_at_100 value: 41.423 - type: mrr_at_1000 value: 41.463 - type: mrr_at_3 value: 37.25 - type: mrr_at_5 value: 39.31 - type: ndcg_at_1 value: 30.9 - type: ndcg_at_10 value: 45.957 - type: ndcg_at_100 value: 50.735 - type: ndcg_at_1000 value: 51.861999999999995 - type: ndcg_at_3 value: 39.437 - type: ndcg_at_5 value: 43.146 - type: precision_at_1 value: 30.9 - type: precision_at_10 value: 6.35 - type: precision_at_100 value: 0.861 - type: precision_at_1000 value: 0.095 - type: precision_at_3 value: 15.267 - type: precision_at_5 value: 10.96 - type: recall_at_1 value: 30.9 - type: recall_at_10 value: 63.5 - type: recall_at_100 value: 86.1 - type: recall_at_1000 value: 95.1 - type: recall_at_3 value: 45.800000000000004 - type: recall_at_5 value: 54.800000000000004 - task: type: Classification dataset: name: MTEB EmotionClassification type: mteb/emotion config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 49.765 - type: f1 value: 45.93242203574485 - task: type: Retrieval dataset: name: MTEB FEVER type: fever config: default split: test revision: None metrics: - type: map_at_1 value: 75.138 - type: map_at_10 value: 84.21300000000001 - type: map_at_100 value: 84.43 - type: map_at_1000 value: 84.441 - type: map_at_3 value: 83.071 - type: map_at_5 value: 83.853 - type: mrr_at_1 value: 80.948 - type: mrr_at_10 value: 88.175 - type: mrr_at_100 value: 88.24 - type: mrr_at_1000 value: 88.241 - type: mrr_at_3 value: 87.516 - type: mrr_at_5 value: 87.997 - type: ndcg_at_1 value: 80.948 - type: ndcg_at_10 value: 87.84100000000001 - type: ndcg_at_100 value: 88.576 - type: ndcg_at_1000 value: 88.75699999999999 - type: ndcg_at_3 value: 86.176 - type: ndcg_at_5 value: 87.214 - type: precision_at_1 value: 80.948 - type: precision_at_10 value: 10.632 - type: precision_at_100 value: 1.123 - type: precision_at_1000 value: 0.11499999999999999 - type: precision_at_3 value: 33.193 - type: precision_at_5 value: 20.663 - type: recall_at_1 value: 75.138 - type: recall_at_10 value: 94.89699999999999 - type: recall_at_100 value: 97.751 - type: recall_at_1000 value: 98.833 - type: recall_at_3 value: 90.455 - type: recall_at_5 value: 93.085 - task: type: Retrieval dataset: name: MTEB FiQA2018 type: fiqa config: default split: test revision: None metrics: - type: map_at_1 value: 29.45 - type: map_at_10 value: 48.596000000000004 - type: map_at_100 value: 50.70400000000001 - type: map_at_1000 value: 50.83800000000001 - type: map_at_3 value: 42.795 - type: map_at_5 value: 46.085 - 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type: map_at_5 value: 54.2 - type: mrr_at_1 value: 44.351 - type: mrr_at_10 value: 58.567 - type: mrr_at_100 value: 59.099000000000004 - type: mrr_at_1000 value: 59.109 - type: mrr_at_3 value: 55.218999999999994 - type: mrr_at_5 value: 57.391999999999996 - type: ndcg_at_1 value: 44.322 - type: ndcg_at_10 value: 63.535 - type: ndcg_at_100 value: 66.654 - type: ndcg_at_1000 value: 66.991 - type: ndcg_at_3 value: 55.701 - type: ndcg_at_5 value: 60.06700000000001 - type: precision_at_1 value: 44.322 - type: precision_at_10 value: 10.026 - type: precision_at_100 value: 1.18 - type: precision_at_1000 value: 0.121 - type: precision_at_3 value: 24.865000000000002 - type: precision_at_5 value: 17.48 - type: recall_at_1 value: 39.499 - type: recall_at_10 value: 84.053 - type: recall_at_100 value: 97.11 - type: recall_at_1000 value: 99.493 - type: recall_at_3 value: 64.091 - type: recall_at_5 value: 74.063 - task: type: PairClassification dataset: name: MTEB Ocnli type: C-MTEB/OCNLI config: default split: validation revision: None metrics: - type: cos_sim_accuracy value: 61.18029236599891 - type: cos_sim_ap value: 64.18398769398412 - type: cos_sim_f1 value: 67.96347757046446 - type: cos_sim_precision value: 54.4529262086514 - type: cos_sim_recall value: 90.3907074973601 - type: dot_accuracy value: 61.18029236599891 - type: dot_ap value: 64.18393484706077 - type: dot_f1 value: 67.96347757046446 - type: dot_precision value: 54.4529262086514 - type: dot_recall value: 90.3907074973601 - type: euclidean_accuracy value: 61.18029236599891 - type: euclidean_ap value: 64.18395024821486 - type: euclidean_f1 value: 67.96347757046446 - type: euclidean_precision value: 54.4529262086514 - type: euclidean_recall value: 90.3907074973601 - type: manhattan_accuracy value: 61.451001624255554 - type: manhattan_ap value: 64.38232708763513 - type: manhattan_f1 value: 68.05860805860804 - type: manhattan_precision value: 52.10319685922602 - type: manhattan_recall value: 98.09926082365365 - type: max_accuracy value: 61.451001624255554 - type: max_ap value: 64.38232708763513 - type: max_f1 value: 68.05860805860804 - task: type: Classification dataset: name: MTEB OnlineShopping type: C-MTEB/OnlineShopping-classification config: default split: test revision: None metrics: - type: accuracy value: 92.19000000000001 - type: ap value: 89.73918431886767 - type: f1 value: 92.17175032574507 - task: type: STS dataset: name: MTEB PAWSX type: C-MTEB/PAWSX config: default split: test revision: None metrics: - type: cos_sim_pearson value: 15.079320253752224 - type: cos_sim_spearman value: 16.813772504404263 - type: euclidean_pearson value: 19.476541162041762 - type: euclidean_spearman value: 16.813772498098782 - type: manhattan_pearson value: 19.497429832915277 - type: manhattan_spearman value: 16.869600674180607 - task: type: STS dataset: name: MTEB QBQTC type: C-MTEB/QBQTC config: default split: test revision: None metrics: - type: cos_sim_pearson value: 30.36139599797913 - type: cos_sim_spearman value: 31.80296402851347 - type: euclidean_pearson value: 30.10387888252793 - type: euclidean_spearman value: 31.80297780103808 - type: manhattan_pearson value: 30.86720382849436 - type: manhattan_spearman value: 32.70491131366606 - task: type: Retrieval dataset: name: MTEB QuoraRetrieval type: quora config: default split: test revision: None metrics: - type: map_at_1 value: 71.911 - type: map_at_10 value: 86.087 - type: map_at_100 value: 86.701 - type: map_at_1000 value: 86.715 - type: map_at_3 value: 83.231 - type: map_at_5 value: 85.051 - type: mrr_at_1 value: 82.75 - type: mrr_at_10 value: 88.759 - type: mrr_at_100 value: 88.844 - type: mrr_at_1000 value: 88.844 - type: mrr_at_3 value: 87.935 - type: mrr_at_5 value: 88.504 - type: ndcg_at_1 value: 82.75 - type: ndcg_at_10 value: 89.605 - type: ndcg_at_100 value: 90.664 - type: ndcg_at_1000 value: 90.733 - type: ndcg_at_3 value: 87.03 - type: ndcg_at_5 value: 88.473 - type: precision_at_1 value: 82.75 - type: precision_at_10 value: 13.575000000000001 - type: precision_at_100 value: 1.539 - type: precision_at_1000 value: 0.157 - type: precision_at_3 value: 38.153 - type: precision_at_5 value: 25.008000000000003 - type: recall_at_1 value: 71.911 - type: recall_at_10 value: 96.261 - type: recall_at_100 value: 99.72800000000001 - type: recall_at_1000 value: 99.993 - type: recall_at_3 value: 88.762 - type: recall_at_5 value: 92.949 - task: type: Clustering dataset: name: MTEB RedditClustering type: mteb/reddit-clustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 57.711581165572376 - task: type: Clustering dataset: name: MTEB RedditClusteringP2P type: mteb/reddit-clustering-p2p config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 66.48938885750297 - task: type: Retrieval dataset: name: MTEB SCIDOCS type: scidocs config: default split: test revision: None metrics: - type: map_at_1 value: 3.7379999999999995 - type: map_at_10 value: 9.261 - type: map_at_100 value: 11.001 - type: map_at_1000 value: 11.262 - type: map_at_3 value: 6.816 - type: map_at_5 value: 8.0 - type: mrr_at_1 value: 18.4 - type: mrr_at_10 value: 28.755999999999997 - type: mrr_at_100 value: 29.892000000000003 - type: mrr_at_1000 value: 29.961 - type: mrr_at_3 value: 25.467000000000002 - type: mrr_at_5 value: 27.332 - type: ndcg_at_1 value: 18.4 - type: ndcg_at_10 value: 16.296 - type: ndcg_at_100 value: 23.52 - type: ndcg_at_1000 value: 28.504 - type: ndcg_at_3 value: 15.485 - type: ndcg_at_5 value: 13.471 - type: precision_at_1 value: 18.4 - type: precision_at_10 value: 8.469999999999999 - type: precision_at_100 value: 1.8950000000000002 - type: precision_at_1000 value: 0.309 - type: precision_at_3 value: 14.6 - type: precision_at_5 value: 11.84 - type: recall_at_1 value: 3.7379999999999995 - type: recall_at_10 value: 17.185 - type: recall_at_100 value: 38.397 - type: recall_at_1000 value: 62.798 - type: recall_at_3 value: 8.896999999999998 - type: recall_at_5 value: 12.021999999999998 - task: type: STS dataset: name: MTEB SICK-R type: mteb/sickr-sts config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - 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type: f1 value: 85.26264784928323 - task: type: text-generation name: Text Generation dataset: name: AI2 Reasoning Challenge (25-Shot) type: ai2_arc config: ARC-Challenge split: test args: num_few_shot: 25 metrics: - type: acc_norm value: 29.61 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=intfloat/e5-mistral-7b-instruct name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: HellaSwag (10-Shot) type: hellaswag split: validation args: num_few_shot: 10 metrics: - type: acc_norm value: 27.05 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=intfloat/e5-mistral-7b-instruct name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: MMLU (5-Shot) type: cais/mmlu config: all split: test args: num_few_shot: 5 metrics: - type: acc value: 23.12 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=intfloat/e5-mistral-7b-instruct name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: TruthfulQA (0-shot) type: truthful_qa config: multiple_choice split: validation args: num_few_shot: 0 metrics: - type: mc2 value: 47.53 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=intfloat/e5-mistral-7b-instruct name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: Winogrande (5-shot) type: winogrande config: winogrande_xl split: validation args: num_few_shot: 5 metrics: - type: acc value: 51.93 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=intfloat/e5-mistral-7b-instruct name: Open LLM Leaderboard - task: type: text-generation name: Text Generation dataset: name: GSM8k (5-shot) type: gsm8k config: main split: test args: num_few_shot: 5 metrics: - type: acc value: 0.0 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=intfloat/e5-mistral-7b-instruct name: Open LLM Leaderboard --- ## E5-mistral-7b-instruct [Improving Text Embeddings with Large Language Models](https://arxiv.org/pdf/2401.00368.pdf). Liang Wang, Nan Yang, Xiaolong Huang, Linjun Yang, Rangan Majumder, Furu Wei, arXiv 2024 This model has 32 layers and the embedding size is 4096. ## Usage Below is an example to encode queries and passages from the MS-MARCO passage ranking dataset. ### Sentence Transformers ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer("intfloat/e5-mistral-7b-instruct") # In case you want to reduce the maximum sequence length: model.max_seq_length = 4096 queries = [ "how much protein should a female eat", "summit define", ] documents = [ "As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.", "Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments." ] query_embeddings = model.encode(queries, prompt_name="web_search_query") document_embeddings = model.encode(documents) scores = (query_embeddings @ document_embeddings.T) * 100 print(scores.tolist()) ``` Have a look at [config_sentence_transformers.json](config_sentence_transformers.json) for the prompts that are pre-configured, such as `web_search_query`, `sts_query`, and `summarization_query`. Additionally, check out [unilm/e5/utils.py](https://github.com/microsoft/unilm/blob/9c0f1ff7ca53431fe47d2637dfe253643d94185b/e5/utils.py#L106) for prompts we used for evaluation. You can use these via e.g. `model.encode(queries, prompt="Instruct: Given a claim, find documents that refute the claim\nQuery: ")`. ### Transformers ```python import torch import torch.nn.functional as F from torch import Tensor from transformers import AutoTokenizer, AutoModel def last_token_pool(last_hidden_states: Tensor, attention_mask: Tensor) -> Tensor: left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0]) if left_padding: return last_hidden_states[:, -1] else: sequence_lengths = attention_mask.sum(dim=1) - 1 batch_size = last_hidden_states.shape[0] return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths] def get_detailed_instruct(task_description: str, query: str) -> str: return f'Instruct: {task_description}\nQuery: {query}' # Each query must come with a one-sentence instruction that describes the task task = 'Given a web search query, retrieve relevant passages that answer the query' queries = [ get_detailed_instruct(task, 'how much protein should a female eat'), get_detailed_instruct(task, 'summit define') ] # No need to add instruction for retrieval documents documents = [ "As a general guideline, the CDC's average requirement of protein for women ages 19 to 70 is 46 grams per day. But, as you can see from this chart, you'll need to increase that if you're expecting or training for a marathon. Check out the chart below to see how much protein you should be eating each day.", "Definition of summit for English Language Learners. : 1 the highest point of a mountain : the top of a mountain. : 2 the highest level. : 3 a meeting or series of meetings between the leaders of two or more governments." ] input_texts = queries + documents tokenizer = AutoTokenizer.from_pretrained('intfloat/e5-mistral-7b-instruct') model = AutoModel.from_pretrained('intfloat/e5-mistral-7b-instruct') max_length = 4096 # Tokenize the input texts batch_dict = tokenizer(input_texts, max_length=max_length, padding=True, truncation=True, return_tensors='pt') outputs = model(**batch_dict) embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask']) # normalize embeddings embeddings = F.normalize(embeddings, p=2, dim=1) scores = (embeddings[:2] @ embeddings[2:].T) * 100 print(scores.tolist()) ``` ## Supported Languages This model is initialized from [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) and fine-tuned on a mixture of multilingual datasets. As a result, it has some multilingual capability. However, since Mistral-7B-v0.1 is mainly trained on English data, we recommend using this model for English only. For multilingual use cases, please refer to [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large). ## MTEB Benchmark Evaluation Check out [unilm/e5](https://github.com/microsoft/unilm/tree/master/e5) to reproduce evaluation results on the [BEIR](https://arxiv.org/abs/2104.08663) and [MTEB benchmark](https://arxiv.org/abs/2210.07316). ## FAQ **1. Do I need to add instructions to the query?** Yes, this is how the model is trained, otherwise you will see a performance degradation. The task definition should be a one-sentence instruction that describes the task. This is a way to customize text embeddings for different scenarios through natural language instructions. Please check out [unilm/e5/utils.py](https://github.com/microsoft/unilm/blob/9c0f1ff7ca53431fe47d2637dfe253643d94185b/e5/utils.py#L106) for instructions we used for evaluation. On the other hand, there is no need to add instructions to the document side. **2. Why are my reproduced results slightly different from reported in the model card?** Different versions of `transformers` and `pytorch` could cause negligible but non-zero performance differences. **3. Where are the LoRA-only weights?** You can find the LoRA-only weights at [https://huggingface.co/intfloat/e5-mistral-7b-instruct/tree/main/lora](https://huggingface.co/intfloat/e5-mistral-7b-instruct/tree/main/lora). ## Citation If you find our paper or models helpful, please consider cite as follows: ```bibtex @article{wang2023improving, title={Improving Text Embeddings with Large Language Models}, author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Yang, Linjun and Majumder, Rangan and Wei, Furu}, journal={arXiv preprint arXiv:2401.00368}, year={2023} } @article{wang2022text, title={Text Embeddings by Weakly-Supervised Contrastive Pre-training}, author={Wang, Liang and Yang, Nan and Huang, Xiaolong and Jiao, Binxing and Yang, Linjun and Jiang, Daxin and Majumder, Rangan and Wei, Furu}, journal={arXiv preprint arXiv:2212.03533}, year={2022} } ``` ## Limitations Using this model for inputs longer than 4096 tokens is not recommended. This model's multilingual capability is still inferior to [multilingual-e5-large](https://huggingface.co/intfloat/multilingual-e5-large) for some cases. # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_intfloat__e5-mistral-7b-instruct) | Metric |Value| |---------------------------------|----:| |Avg. |29.87| |AI2 Reasoning Challenge (25-Shot)|29.61| |HellaSwag (10-Shot) |27.05| |MMLU (5-Shot) |23.12| |TruthfulQA (0-shot) |47.53| |Winogrande (5-shot) |51.93| |GSM8k (5-shot) | 0.00|