--- license: apache-2.0 pipeline_tag: sentence-similarity inference: false tags: - sentence-transformers - feature-extraction - sentence-similarity - mteb language: en datasets: - s2orc - flax-sentence-embeddings/stackexchange_title_body_jsonl - flax-sentence-embeddings/stackexchange_titlebody_best_voted_answer_jsonl - flax-sentence-embeddings/stackexchange_title_best_voted_answer_jsonl - flax-sentence-embeddings/stackexchange_titlebody_best_and_down_voted_answer_jsonl - sentence-transformers/reddit-title-body - msmarco - gooaq - yahoo_answers_topics - code_search_net - search_qa - eli5 - snli - multi_nli - wikihow - natural_questions - trivia_qa - embedding-data/sentence-compression - embedding-data/flickr30k-captions - embedding-data/altlex - embedding-data/simple-wiki - embedding-data/QQP - embedding-data/SPECTER - embedding-data/PAQ_pairs - embedding-data/WikiAnswers - sentence-transformers/embedding-training-data model-index: - name: lodestone-base-4096-v1 results: - task: type: Classification dataset: type: mteb/amazon_counterfactual name: MTEB AmazonCounterfactualClassification (en) config: en split: test revision: e8379541af4e31359cca9fbcf4b00f2671dba205 metrics: - type: accuracy value: 69.7313432835821 - type: ap value: 31.618259511417733 - type: f1 value: 63.30313825394228 - task: type: Classification dataset: type: mteb/amazon_polarity name: MTEB AmazonPolarityClassification config: default split: test revision: e2d317d38cd51312af73b3d32a06d1a08b442046 metrics: - type: accuracy value: 86.89837499999999 - type: ap value: 82.39500885672128 - type: f1 value: 86.87317947399657 - task: type: Classification dataset: type: mteb/amazon_reviews_multi name: MTEB AmazonReviewsClassification (en) config: en split: test revision: 1399c76144fd37290681b995c656ef9b2e06e26d metrics: - type: accuracy value: 44.05 - type: f1 value: 42.67624383248947 - task: type: Retrieval dataset: type: arguana name: MTEB ArguAna config: default split: test revision: None metrics: - type: map_at_1 value: 26.173999999999996 - type: map_at_10 value: 40.976 - type: map_at_100 value: 42.067 - type: map_at_1000 value: 42.075 - type: map_at_3 value: 35.917 - type: map_at_5 value: 38.656 - type: mrr_at_1 value: 26.814 - type: mrr_at_10 value: 41.252 - type: mrr_at_100 value: 42.337 - type: mrr_at_1000 value: 42.345 - type: mrr_at_3 value: 36.226 - type: mrr_at_5 value: 38.914 - type: ndcg_at_1 value: 26.173999999999996 - type: ndcg_at_10 value: 49.819 - type: ndcg_at_100 value: 54.403999999999996 - type: ndcg_at_1000 value: 54.59 - type: ndcg_at_3 value: 39.231 - type: ndcg_at_5 value: 44.189 - type: precision_at_1 value: 26.173999999999996 - type: precision_at_10 value: 7.838000000000001 - type: precision_at_100 value: 0.9820000000000001 - type: precision_at_1000 value: 0.1 - type: precision_at_3 value: 16.287 - type: precision_at_5 value: 12.191 - type: recall_at_1 value: 26.173999999999996 - type: recall_at_10 value: 78.378 - type: recall_at_100 value: 98.222 - type: recall_at_1000 value: 99.644 - type: recall_at_3 value: 48.862 - type: recall_at_5 value: 60.953 - task: type: Clustering dataset: type: mteb/arxiv-clustering-p2p name: MTEB ArxivClusteringP2P config: default split: test revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d metrics: - type: v_measure value: 42.31689035788179 - task: type: Clustering dataset: type: mteb/arxiv-clustering-s2s name: MTEB ArxivClusteringS2S config: default split: test revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 metrics: - type: v_measure value: 31.280245136660984 - task: type: Reranking dataset: type: mteb/askubuntudupquestions-reranking name: MTEB AskUbuntuDupQuestions config: default split: test revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 metrics: - type: map value: 58.79109720839415 - type: mrr value: 71.79615705931495 - task: type: STS dataset: type: mteb/biosses-sts name: MTEB BIOSSES config: default split: test revision: d3fb88f8f02e40887cd149695127462bbcf29b4a metrics: - type: cos_sim_pearson value: 76.44918756608115 - type: cos_sim_spearman value: 70.86607256286257 - type: euclidean_pearson value: 74.12154678100815 - type: euclidean_spearman value: 70.86607256286257 - type: manhattan_pearson value: 74.0078626964417 - type: manhattan_spearman value: 70.68353828321327 - task: type: Classification dataset: type: mteb/banking77 name: MTEB Banking77Classification config: default split: test revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 metrics: - type: accuracy value: 75.40584415584415 - type: f1 value: 74.29514617572676 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-p2p name: MTEB BiorxivClusteringP2P config: default split: test revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 metrics: - type: v_measure value: 37.41860080664014 - task: type: Clustering dataset: type: mteb/biorxiv-clustering-s2s name: MTEB BiorxivClusteringS2S config: default split: test revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 metrics: - type: v_measure value: 29.319217023090705 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackAndroidRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 26.595000000000002 - type: map_at_10 value: 36.556 - type: map_at_100 value: 37.984 - type: map_at_1000 value: 38.134 - type: map_at_3 value: 33.417 - type: map_at_5 value: 35.160000000000004 - type: mrr_at_1 value: 32.761 - type: mrr_at_10 value: 41.799 - type: mrr_at_100 value: 42.526 - type: mrr_at_1000 value: 42.582 - type: mrr_at_3 value: 39.39 - type: mrr_at_5 value: 40.727000000000004 - type: ndcg_at_1 value: 32.761 - type: ndcg_at_10 value: 42.549 - type: ndcg_at_100 value: 47.915 - type: ndcg_at_1000 value: 50.475 - type: ndcg_at_3 value: 37.93 - type: ndcg_at_5 value: 39.939 - type: precision_at_1 value: 32.761 - type: precision_at_10 value: 8.312 - type: precision_at_100 value: 1.403 - type: precision_at_1000 value: 0.197 - type: precision_at_3 value: 18.741 - type: precision_at_5 value: 13.447999999999999 - type: recall_at_1 value: 26.595000000000002 - type: recall_at_10 value: 54.332 - type: recall_at_100 value: 76.936 - type: recall_at_1000 value: 93.914 - type: recall_at_3 value: 40.666000000000004 - type: recall_at_5 value: 46.513 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackEnglishRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 22.528000000000002 - type: map_at_10 value: 30.751 - type: map_at_100 value: 31.855 - type: map_at_1000 value: 31.972 - type: map_at_3 value: 28.465 - type: map_at_5 value: 29.738 - type: mrr_at_1 value: 28.662 - type: mrr_at_10 value: 35.912 - type: mrr_at_100 value: 36.726 - type: mrr_at_1000 value: 36.777 - type: mrr_at_3 value: 34.013 - type: mrr_at_5 value: 35.156 - type: ndcg_at_1 value: 28.662 - type: ndcg_at_10 value: 35.452 - type: ndcg_at_100 value: 40.1 - type: ndcg_at_1000 value: 42.323 - type: ndcg_at_3 value: 32.112 - type: ndcg_at_5 value: 33.638 - type: precision_at_1 value: 28.662 - type: precision_at_10 value: 6.688 - type: precision_at_100 value: 1.13 - type: precision_at_1000 value: 0.16 - type: precision_at_3 value: 15.562999999999999 - type: precision_at_5 value: 11.019 - type: recall_at_1 value: 22.528000000000002 - type: recall_at_10 value: 43.748 - type: recall_at_100 value: 64.235 - type: recall_at_1000 value: 78.609 - type: recall_at_3 value: 33.937 - type: recall_at_5 value: 38.234 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGamingRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 33.117999999999995 - type: map_at_10 value: 44.339 - type: map_at_100 value: 45.367000000000004 - type: map_at_1000 value: 45.437 - type: map_at_3 value: 41.195 - type: map_at_5 value: 42.922 - type: mrr_at_1 value: 38.37 - type: mrr_at_10 value: 47.786 - type: mrr_at_100 value: 48.522 - type: mrr_at_1000 value: 48.567 - type: mrr_at_3 value: 45.371 - type: mrr_at_5 value: 46.857 - type: ndcg_at_1 value: 38.37 - type: ndcg_at_10 value: 50.019999999999996 - type: ndcg_at_100 value: 54.36299999999999 - type: ndcg_at_1000 value: 55.897 - type: ndcg_at_3 value: 44.733000000000004 - type: ndcg_at_5 value: 47.292 - type: precision_at_1 value: 38.37 - type: precision_at_10 value: 8.288 - type: precision_at_100 value: 1.139 - type: precision_at_1000 value: 0.132 - type: precision_at_3 value: 20.293 - type: precision_at_5 value: 14.107 - type: recall_at_1 value: 33.117999999999995 - type: recall_at_10 value: 63.451 - type: recall_at_100 value: 82.767 - type: recall_at_1000 value: 93.786 - type: recall_at_3 value: 48.964999999999996 - type: recall_at_5 value: 55.358 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackGisRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 16.028000000000002 - type: map_at_10 value: 23.186999999999998 - type: map_at_100 value: 24.236 - type: map_at_1000 value: 24.337 - type: map_at_3 value: 20.816000000000003 - type: map_at_5 value: 22.311 - type: mrr_at_1 value: 17.514 - type: mrr_at_10 value: 24.84 - type: mrr_at_100 value: 25.838 - type: mrr_at_1000 value: 25.924999999999997 - type: mrr_at_3 value: 22.542 - type: mrr_at_5 value: 24.04 - type: ndcg_at_1 value: 17.514 - type: ndcg_at_10 value: 27.391 - type: ndcg_at_100 value: 32.684999999999995 - type: ndcg_at_1000 value: 35.367 - type: ndcg_at_3 value: 22.820999999999998 - type: ndcg_at_5 value: 25.380999999999997 - type: precision_at_1 value: 17.514 - type: precision_at_10 value: 4.463 - type: precision_at_100 value: 0.745 - type: precision_at_1000 value: 0.101 - type: precision_at_3 value: 10.019 - type: precision_at_5 value: 7.457999999999999 - type: recall_at_1 value: 16.028000000000002 - type: recall_at_10 value: 38.81 - type: recall_at_100 value: 63.295 - type: recall_at_1000 value: 83.762 - type: recall_at_3 value: 26.604 - type: recall_at_5 value: 32.727000000000004 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackMathematicaRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 11.962 - type: map_at_10 value: 17.218 - type: map_at_100 value: 18.321 - type: map_at_1000 value: 18.455 - type: map_at_3 value: 15.287999999999998 - type: map_at_5 value: 16.417 - type: mrr_at_1 value: 14.677000000000001 - type: mrr_at_10 value: 20.381 - type: mrr_at_100 value: 21.471999999999998 - type: mrr_at_1000 value: 21.566 - type: mrr_at_3 value: 18.448999999999998 - type: mrr_at_5 value: 19.587 - type: ndcg_at_1 value: 14.677000000000001 - type: ndcg_at_10 value: 20.86 - type: ndcg_at_100 value: 26.519 - type: ndcg_at_1000 value: 30.020000000000003 - type: ndcg_at_3 value: 17.208000000000002 - type: ndcg_at_5 value: 19.037000000000003 - type: precision_at_1 value: 14.677000000000001 - type: precision_at_10 value: 3.856 - type: precision_at_100 value: 0.7889999999999999 - type: precision_at_1000 value: 0.124 - type: precision_at_3 value: 8.043 - type: precision_at_5 value: 6.069999999999999 - type: recall_at_1 value: 11.962 - type: recall_at_10 value: 28.994999999999997 - type: recall_at_100 value: 54.071999999999996 - type: recall_at_1000 value: 79.309 - type: recall_at_3 value: 19.134999999999998 - type: recall_at_5 value: 23.727999999999998 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackPhysicsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 22.764 - type: map_at_10 value: 31.744 - type: map_at_100 value: 33.037 - type: map_at_1000 value: 33.156 - type: map_at_3 value: 29.015 - type: map_at_5 value: 30.434 - type: mrr_at_1 value: 28.296 - type: mrr_at_10 value: 37.03 - type: mrr_at_100 value: 37.902 - type: mrr_at_1000 value: 37.966 - type: mrr_at_3 value: 34.568 - type: mrr_at_5 value: 35.786 - type: ndcg_at_1 value: 28.296 - type: ndcg_at_10 value: 37.289 - type: ndcg_at_100 value: 42.787 - type: ndcg_at_1000 value: 45.382 - type: ndcg_at_3 value: 32.598 - type: ndcg_at_5 value: 34.521 - type: precision_at_1 value: 28.296 - type: precision_at_10 value: 6.901 - type: precision_at_100 value: 1.135 - type: precision_at_1000 value: 0.152 - type: precision_at_3 value: 15.367 - type: precision_at_5 value: 11.03 - type: recall_at_1 value: 22.764 - type: recall_at_10 value: 48.807 - type: recall_at_100 value: 71.859 - type: recall_at_1000 value: 89.606 - type: recall_at_3 value: 35.594 - type: recall_at_5 value: 40.541 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackProgrammersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 19.742 - type: map_at_10 value: 27.741 - type: map_at_100 value: 29.323 - type: map_at_1000 value: 29.438 - type: map_at_3 value: 25.217 - type: map_at_5 value: 26.583000000000002 - type: mrr_at_1 value: 24.657999999999998 - type: mrr_at_10 value: 32.407000000000004 - type: mrr_at_100 value: 33.631 - type: mrr_at_1000 value: 33.686 - type: mrr_at_3 value: 30.194 - type: mrr_at_5 value: 31.444 - type: ndcg_at_1 value: 24.657999999999998 - type: ndcg_at_10 value: 32.614 - type: ndcg_at_100 value: 39.61 - type: ndcg_at_1000 value: 42.114000000000004 - type: ndcg_at_3 value: 28.516000000000002 - type: ndcg_at_5 value: 30.274 - type: precision_at_1 value: 24.657999999999998 - type: precision_at_10 value: 6.176 - type: precision_at_100 value: 1.1400000000000001 - type: precision_at_1000 value: 0.155 - type: precision_at_3 value: 13.927 - type: precision_at_5 value: 9.954 - type: recall_at_1 value: 19.742 - type: recall_at_10 value: 42.427 - type: recall_at_100 value: 72.687 - type: recall_at_1000 value: 89.89 - type: recall_at_3 value: 30.781 - type: recall_at_5 value: 35.606 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 19.72608333333333 - type: map_at_10 value: 27.165333333333336 - type: map_at_100 value: 28.292499999999997 - type: map_at_1000 value: 28.416333333333327 - type: map_at_3 value: 24.783833333333334 - type: map_at_5 value: 26.101750000000003 - type: mrr_at_1 value: 23.721500000000002 - type: mrr_at_10 value: 30.853333333333328 - type: mrr_at_100 value: 31.741750000000003 - type: mrr_at_1000 value: 31.812999999999995 - type: mrr_at_3 value: 28.732249999999997 - type: mrr_at_5 value: 29.945166666666665 - type: ndcg_at_1 value: 23.721500000000002 - type: ndcg_at_10 value: 31.74883333333333 - type: ndcg_at_100 value: 36.883583333333334 - type: ndcg_at_1000 value: 39.6145 - type: ndcg_at_3 value: 27.639583333333334 - type: ndcg_at_5 value: 29.543666666666667 - type: precision_at_1 value: 23.721500000000002 - type: precision_at_10 value: 5.709083333333333 - type: precision_at_100 value: 0.9859166666666666 - type: precision_at_1000 value: 0.1413333333333333 - type: precision_at_3 value: 12.85683333333333 - type: precision_at_5 value: 9.258166666666668 - type: recall_at_1 value: 19.72608333333333 - type: recall_at_10 value: 41.73583333333334 - type: recall_at_100 value: 64.66566666666668 - type: recall_at_1000 value: 84.09833333333336 - type: recall_at_3 value: 30.223083333333328 - type: recall_at_5 value: 35.153083333333335 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackStatsRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 17.582 - type: map_at_10 value: 22.803 - type: map_at_100 value: 23.503 - type: map_at_1000 value: 23.599999999999998 - type: map_at_3 value: 21.375 - type: map_at_5 value: 22.052 - type: mrr_at_1 value: 20.399 - type: mrr_at_10 value: 25.369999999999997 - type: mrr_at_100 value: 26.016000000000002 - type: mrr_at_1000 value: 26.090999999999998 - type: mrr_at_3 value: 23.952 - type: mrr_at_5 value: 24.619 - type: ndcg_at_1 value: 20.399 - type: ndcg_at_10 value: 25.964 - type: ndcg_at_100 value: 29.607 - type: ndcg_at_1000 value: 32.349 - type: ndcg_at_3 value: 23.177 - type: ndcg_at_5 value: 24.276 - type: precision_at_1 value: 20.399 - type: precision_at_10 value: 4.018 - type: precision_at_100 value: 0.629 - type: precision_at_1000 value: 0.093 - type: precision_at_3 value: 9.969 - type: precision_at_5 value: 6.748 - type: recall_at_1 value: 17.582 - type: recall_at_10 value: 33.35 - type: recall_at_100 value: 50.219 - type: recall_at_1000 value: 71.06099999999999 - type: recall_at_3 value: 25.619999999999997 - type: recall_at_5 value: 28.291 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackTexRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 11.071 - type: map_at_10 value: 16.201999999999998 - type: map_at_100 value: 17.112 - type: map_at_1000 value: 17.238 - type: map_at_3 value: 14.508 - type: map_at_5 value: 15.440999999999999 - type: mrr_at_1 value: 13.833 - type: mrr_at_10 value: 19.235 - type: mrr_at_100 value: 20.108999999999998 - type: mrr_at_1000 value: 20.196 - type: mrr_at_3 value: 17.515 - type: mrr_at_5 value: 18.505 - type: ndcg_at_1 value: 13.833 - type: ndcg_at_10 value: 19.643 - type: ndcg_at_100 value: 24.298000000000002 - type: ndcg_at_1000 value: 27.614 - type: ndcg_at_3 value: 16.528000000000002 - type: ndcg_at_5 value: 17.991 - type: precision_at_1 value: 13.833 - type: precision_at_10 value: 3.6990000000000003 - type: precision_at_100 value: 0.713 - type: precision_at_1000 value: 0.116 - type: precision_at_3 value: 7.9030000000000005 - type: precision_at_5 value: 5.891 - type: recall_at_1 value: 11.071 - type: recall_at_10 value: 27.019 - type: recall_at_100 value: 48.404 - type: recall_at_1000 value: 72.641 - type: recall_at_3 value: 18.336 - type: recall_at_5 value: 21.991 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackUnixRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 18.573 - type: map_at_10 value: 25.008999999999997 - type: map_at_100 value: 26.015 - type: map_at_1000 value: 26.137 - type: map_at_3 value: 22.798 - type: map_at_5 value: 24.092 - type: mrr_at_1 value: 22.108 - type: mrr_at_10 value: 28.646 - type: mrr_at_100 value: 29.477999999999998 - type: mrr_at_1000 value: 29.57 - type: mrr_at_3 value: 26.415 - type: mrr_at_5 value: 27.693 - type: ndcg_at_1 value: 22.108 - type: ndcg_at_10 value: 29.42 - type: ndcg_at_100 value: 34.385 - type: ndcg_at_1000 value: 37.572 - type: ndcg_at_3 value: 25.274 - type: ndcg_at_5 value: 27.315 - type: precision_at_1 value: 22.108 - type: precision_at_10 value: 5.093 - type: precision_at_100 value: 0.859 - type: precision_at_1000 value: 0.124 - type: precision_at_3 value: 11.474 - type: precision_at_5 value: 8.321000000000002 - type: recall_at_1 value: 18.573 - type: recall_at_10 value: 39.433 - type: recall_at_100 value: 61.597 - type: recall_at_1000 value: 84.69 - type: recall_at_3 value: 27.849 - type: recall_at_5 value: 33.202999999999996 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWebmastersRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 22.807 - type: map_at_10 value: 30.014000000000003 - type: map_at_100 value: 31.422 - type: map_at_1000 value: 31.652 - type: map_at_3 value: 27.447 - type: map_at_5 value: 28.711 - type: mrr_at_1 value: 27.668 - type: mrr_at_10 value: 34.489 - type: mrr_at_100 value: 35.453 - type: mrr_at_1000 value: 35.526 - type: mrr_at_3 value: 32.477000000000004 - type: mrr_at_5 value: 33.603 - type: ndcg_at_1 value: 27.668 - type: ndcg_at_10 value: 34.983 - type: ndcg_at_100 value: 40.535 - type: ndcg_at_1000 value: 43.747 - type: ndcg_at_3 value: 31.026999999999997 - type: ndcg_at_5 value: 32.608 - type: precision_at_1 value: 27.668 - type: precision_at_10 value: 6.837999999999999 - type: precision_at_100 value: 1.411 - type: precision_at_1000 value: 0.23600000000000002 - type: precision_at_3 value: 14.295 - type: precision_at_5 value: 10.435 - type: recall_at_1 value: 22.807 - type: recall_at_10 value: 43.545 - type: recall_at_100 value: 69.39800000000001 - type: recall_at_1000 value: 90.706 - type: recall_at_3 value: 32.183 - type: recall_at_5 value: 36.563 - task: type: Retrieval dataset: type: BeIR/cqadupstack name: MTEB CQADupstackWordpressRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 13.943 - type: map_at_10 value: 20.419999999999998 - type: map_at_100 value: 21.335 - type: map_at_1000 value: 21.44 - type: map_at_3 value: 17.865000000000002 - type: map_at_5 value: 19.36 - type: mrr_at_1 value: 15.712000000000002 - type: mrr_at_10 value: 22.345000000000002 - type: mrr_at_100 value: 23.227999999999998 - type: mrr_at_1000 value: 23.304 - type: mrr_at_3 value: 19.901 - type: mrr_at_5 value: 21.325 - type: ndcg_at_1 value: 15.712000000000002 - type: ndcg_at_10 value: 24.801000000000002 - type: ndcg_at_100 value: 29.799 - type: ndcg_at_1000 value: 32.513999999999996 - type: ndcg_at_3 value: 19.750999999999998 - type: ndcg_at_5 value: 22.252 - type: precision_at_1 value: 15.712000000000002 - type: precision_at_10 value: 4.1770000000000005 - type: precision_at_100 value: 0.738 - type: precision_at_1000 value: 0.106 - type: precision_at_3 value: 8.688 - type: precision_at_5 value: 6.617000000000001 - type: recall_at_1 value: 13.943 - type: recall_at_10 value: 36.913000000000004 - type: recall_at_100 value: 60.519 - type: recall_at_1000 value: 81.206 - type: recall_at_3 value: 23.006999999999998 - type: recall_at_5 value: 29.082 - task: type: Retrieval dataset: type: climate-fever name: MTEB ClimateFEVER config: default split: test revision: None metrics: - type: map_at_1 value: 9.468 - type: map_at_10 value: 16.029 - type: map_at_100 value: 17.693 - type: map_at_1000 value: 17.886 - type: map_at_3 value: 13.15 - type: map_at_5 value: 14.568 - type: mrr_at_1 value: 21.173000000000002 - type: mrr_at_10 value: 31.028 - type: mrr_at_100 value: 32.061 - type: mrr_at_1000 value: 32.119 - type: mrr_at_3 value: 27.534999999999997 - type: mrr_at_5 value: 29.431 - type: ndcg_at_1 value: 21.173000000000002 - type: ndcg_at_10 value: 23.224 - type: ndcg_at_100 value: 30.225 - type: ndcg_at_1000 value: 33.961000000000006 - type: ndcg_at_3 value: 18.174 - type: ndcg_at_5 value: 19.897000000000002 - type: precision_at_1 value: 21.173000000000002 - type: precision_at_10 value: 7.4719999999999995 - type: precision_at_100 value: 1.5010000000000001 - type: precision_at_1000 value: 0.219 - type: precision_at_3 value: 13.312 - type: precision_at_5 value: 10.619 - type: recall_at_1 value: 9.468 - type: recall_at_10 value: 28.823 - type: recall_at_100 value: 53.26499999999999 - type: recall_at_1000 value: 74.536 - type: recall_at_3 value: 16.672 - type: recall_at_5 value: 21.302 - task: type: Retrieval dataset: type: dbpedia-entity name: MTEB DBPedia config: default split: test revision: None metrics: - type: map_at_1 value: 6.343 - type: map_at_10 value: 12.717 - type: map_at_100 value: 16.48 - type: map_at_1000 value: 17.381 - type: map_at_3 value: 9.568999999999999 - type: map_at_5 value: 11.125 - type: mrr_at_1 value: 48.75 - type: mrr_at_10 value: 58.425000000000004 - type: mrr_at_100 value: 59.075 - type: mrr_at_1000 value: 59.095 - type: mrr_at_3 value: 56.291999999999994 - type: mrr_at_5 value: 57.679 - type: ndcg_at_1 value: 37.875 - type: ndcg_at_10 value: 27.77 - type: ndcg_at_100 value: 30.288999999999998 - type: ndcg_at_1000 value: 36.187999999999995 - type: ndcg_at_3 value: 31.385999999999996 - type: ndcg_at_5 value: 29.923 - type: precision_at_1 value: 48.75 - type: precision_at_10 value: 22.375 - type: precision_at_100 value: 6.3420000000000005 - type: precision_at_1000 value: 1.4489999999999998 - type: precision_at_3 value: 35.5 - type: precision_at_5 value: 30.55 - type: recall_at_1 value: 6.343 - type: recall_at_10 value: 16.936 - type: recall_at_100 value: 35.955999999999996 - type: recall_at_1000 value: 55.787 - type: recall_at_3 value: 10.771 - type: recall_at_5 value: 13.669999999999998 - task: type: Classification dataset: type: mteb/emotion name: MTEB EmotionClassification config: default split: test revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 metrics: - type: accuracy value: 41.99 - type: f1 value: 36.823402174564954 - task: type: Retrieval dataset: type: fever name: MTEB FEVER config: default split: test revision: None metrics: - type: map_at_1 value: 40.088 - type: map_at_10 value: 52.69200000000001 - type: map_at_100 value: 53.296 - type: map_at_1000 value: 53.325 - type: map_at_3 value: 49.905 - type: map_at_5 value: 51.617000000000004 - type: mrr_at_1 value: 43.009 - type: mrr_at_10 value: 56.203 - type: mrr_at_100 value: 56.75 - type: mrr_at_1000 value: 56.769000000000005 - type: mrr_at_3 value: 53.400000000000006 - type: mrr_at_5 value: 55.163 - type: ndcg_at_1 value: 43.009 - type: ndcg_at_10 value: 59.39 - type: ndcg_at_100 value: 62.129999999999995 - type: ndcg_at_1000 value: 62.793 - type: ndcg_at_3 value: 53.878 - type: ndcg_at_5 value: 56.887 - type: precision_at_1 value: 43.009 - type: precision_at_10 value: 8.366 - type: precision_at_100 value: 0.983 - type: precision_at_1000 value: 0.105 - type: precision_at_3 value: 22.377 - type: precision_at_5 value: 15.035000000000002 - type: recall_at_1 value: 40.088 - type: recall_at_10 value: 76.68700000000001 - type: recall_at_100 value: 88.91 - type: recall_at_1000 value: 93.782 - type: recall_at_3 value: 61.809999999999995 - type: recall_at_5 value: 69.131 - task: type: Retrieval dataset: type: fiqa name: MTEB FiQA2018 config: default split: test revision: None metrics: - type: map_at_1 value: 10.817 - type: map_at_10 value: 18.9 - type: map_at_100 value: 20.448 - type: map_at_1000 value: 20.660999999999998 - type: map_at_3 value: 15.979 - type: map_at_5 value: 17.415 - type: mrr_at_1 value: 23.148 - type: mrr_at_10 value: 31.208000000000002 - type: mrr_at_100 value: 32.167 - type: mrr_at_1000 value: 32.242 - type: mrr_at_3 value: 28.498 - type: mrr_at_5 value: 29.964000000000002 - type: ndcg_at_1 value: 23.148 - type: ndcg_at_10 value: 25.325999999999997 - type: ndcg_at_100 value: 31.927 - type: ndcg_at_1000 value: 36.081 - type: ndcg_at_3 value: 21.647 - type: ndcg_at_5 value: 22.762999999999998 - type: precision_at_1 value: 23.148 - type: precision_at_10 value: 7.546 - type: precision_at_100 value: 1.415 - type: precision_at_1000 value: 0.216 - type: precision_at_3 value: 14.969 - type: precision_at_5 value: 11.327 - type: recall_at_1 value: 10.817 - type: recall_at_10 value: 32.164 - type: recall_at_100 value: 57.655 - type: recall_at_1000 value: 82.797 - type: recall_at_3 value: 19.709 - type: recall_at_5 value: 24.333 - task: type: Retrieval dataset: type: hotpotqa name: MTEB HotpotQA config: default split: test revision: None metrics: - type: map_at_1 value: 25.380999999999997 - type: map_at_10 value: 33.14 - type: map_at_100 value: 33.948 - type: map_at_1000 value: 34.028000000000006 - type: map_at_3 value: 31.019999999999996 - type: map_at_5 value: 32.23 - type: mrr_at_1 value: 50.763000000000005 - type: mrr_at_10 value: 57.899 - type: mrr_at_100 value: 58.426 - type: mrr_at_1000 value: 58.457 - type: mrr_at_3 value: 56.093 - type: mrr_at_5 value: 57.116 - type: ndcg_at_1 value: 50.763000000000005 - type: ndcg_at_10 value: 41.656 - type: ndcg_at_100 value: 45.079 - type: ndcg_at_1000 value: 46.916999999999994 - type: ndcg_at_3 value: 37.834 - type: ndcg_at_5 value: 39.732 - type: precision_at_1 value: 50.763000000000005 - type: precision_at_10 value: 8.648 - type: precision_at_100 value: 1.135 - type: precision_at_1000 value: 0.13799999999999998 - type: precision_at_3 value: 23.105999999999998 - type: precision_at_5 value: 15.363 - type: recall_at_1 value: 25.380999999999997 - type: recall_at_10 value: 43.241 - type: recall_at_100 value: 56.745000000000005 - type: recall_at_1000 value: 69.048 - type: recall_at_3 value: 34.659 - type: recall_at_5 value: 38.406 - task: type: Classification dataset: type: mteb/imdb name: MTEB ImdbClassification config: default split: test revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 metrics: - type: accuracy value: 79.544 - type: ap value: 73.82920133396664 - type: f1 value: 79.51048124883265 - task: type: Retrieval dataset: type: msmarco name: MTEB MSMARCO config: default split: dev revision: None metrics: - type: map_at_1 value: 11.174000000000001 - type: map_at_10 value: 19.451999999999998 - type: map_at_100 value: 20.612 - type: map_at_1000 value: 20.703 - type: map_at_3 value: 16.444 - type: map_at_5 value: 18.083 - type: mrr_at_1 value: 11.447000000000001 - type: mrr_at_10 value: 19.808 - type: mrr_at_100 value: 20.958 - type: mrr_at_1000 value: 21.041999999999998 - type: mrr_at_3 value: 16.791 - type: mrr_at_5 value: 18.459 - type: ndcg_at_1 value: 11.447000000000001 - type: ndcg_at_10 value: 24.556 - type: ndcg_at_100 value: 30.637999999999998 - type: ndcg_at_1000 value: 33.14 - type: ndcg_at_3 value: 18.325 - type: ndcg_at_5 value: 21.278 - type: precision_at_1 value: 11.447000000000001 - type: precision_at_10 value: 4.215 - type: precision_at_100 value: 0.732 - type: precision_at_1000 value: 0.095 - type: precision_at_3 value: 8.052 - type: precision_at_5 value: 6.318 - type: recall_at_1 value: 11.174000000000001 - type: recall_at_10 value: 40.543 - type: recall_at_100 value: 69.699 - type: recall_at_1000 value: 89.403 - type: recall_at_3 value: 23.442 - type: recall_at_5 value: 30.536 - task: type: Classification dataset: type: mteb/mtop_domain name: MTEB MTOPDomainClassification (en) config: en split: test revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf metrics: - type: accuracy value: 89.6671226630187 - type: f1 value: 89.57660424361246 - task: type: Classification dataset: type: mteb/mtop_intent name: MTEB MTOPIntentClassification (en) config: en split: test revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba metrics: - type: accuracy value: 60.284997720018254 - type: f1 value: 40.30637400152823 - task: type: Classification dataset: type: mteb/amazon_massive_intent name: MTEB MassiveIntentClassification (en) config: en split: test revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 metrics: - type: accuracy value: 63.33557498318763 - type: f1 value: 60.24039910680179 - task: type: Classification dataset: type: mteb/amazon_massive_scenario name: MTEB MassiveScenarioClassification (en) config: en split: test revision: 7d571f92784cd94a019292a1f45445077d0ef634 metrics: - type: accuracy value: 72.37390719569603 - type: f1 value: 72.33097333477316 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-p2p name: MTEB MedrxivClusteringP2P config: default split: test revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 metrics: - type: v_measure value: 34.68158939060552 - task: type: Clustering dataset: type: mteb/medrxiv-clustering-s2s name: MTEB MedrxivClusteringS2S config: default split: test revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 metrics: - type: v_measure value: 30.340061711905236 - task: type: Reranking dataset: type: mteb/mind_small name: MTEB MindSmallReranking config: default split: test revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 metrics: - type: map value: 32.01814326295803 - type: mrr value: 33.20555240055367 - task: type: Retrieval dataset: type: nfcorpus name: MTEB NFCorpus config: default split: test revision: None metrics: - type: map_at_1 value: 3.3910000000000005 - type: map_at_10 value: 7.7219999999999995 - type: map_at_100 value: 10.286 - type: map_at_1000 value: 11.668000000000001 - type: map_at_3 value: 5.552 - type: map_at_5 value: 6.468 - type: mrr_at_1 value: 34.365 - type: mrr_at_10 value: 42.555 - type: mrr_at_100 value: 43.295 - type: mrr_at_1000 value: 43.357 - type: mrr_at_3 value: 40.299 - type: mrr_at_5 value: 41.182 - type: ndcg_at_1 value: 31.424000000000003 - type: ndcg_at_10 value: 24.758 - type: ndcg_at_100 value: 23.677999999999997 - type: ndcg_at_1000 value: 33.377 - type: ndcg_at_3 value: 28.302 - type: ndcg_at_5 value: 26.342 - type: precision_at_1 value: 33.437 - type: precision_at_10 value: 19.256999999999998 - type: precision_at_100 value: 6.662999999999999 - type: precision_at_1000 value: 1.9900000000000002 - type: precision_at_3 value: 27.761000000000003 - type: precision_at_5 value: 23.715 - type: recall_at_1 value: 3.3910000000000005 - type: recall_at_10 value: 11.068 - type: recall_at_100 value: 25.878 - type: recall_at_1000 value: 60.19 - type: recall_at_3 value: 6.1690000000000005 - type: recall_at_5 value: 7.767 - task: type: Retrieval dataset: type: nq name: MTEB NQ config: default split: test revision: None metrics: - type: map_at_1 value: 15.168000000000001 - type: map_at_10 value: 26.177 - type: map_at_100 value: 27.564 - type: map_at_1000 value: 27.628999999999998 - type: map_at_3 value: 22.03 - type: map_at_5 value: 24.276 - type: mrr_at_1 value: 17.439 - type: mrr_at_10 value: 28.205000000000002 - type: mrr_at_100 value: 29.357 - type: mrr_at_1000 value: 29.408 - type: mrr_at_3 value: 24.377 - type: mrr_at_5 value: 26.540000000000003 - type: ndcg_at_1 value: 17.41 - type: ndcg_at_10 value: 32.936 - type: ndcg_at_100 value: 39.196999999999996 - type: ndcg_at_1000 value: 40.892 - type: ndcg_at_3 value: 24.721 - type: ndcg_at_5 value: 28.615000000000002 - type: precision_at_1 value: 17.41 - type: precision_at_10 value: 6.199000000000001 - type: precision_at_100 value: 0.9690000000000001 - type: precision_at_1000 value: 0.11299999999999999 - type: precision_at_3 value: 11.790000000000001 - type: precision_at_5 value: 9.264 - type: recall_at_1 value: 15.168000000000001 - type: recall_at_10 value: 51.914 - type: recall_at_100 value: 79.804 - type: recall_at_1000 value: 92.75999999999999 - type: recall_at_3 value: 30.212 - type: recall_at_5 value: 39.204 - task: type: Retrieval dataset: type: quora name: MTEB QuoraRetrieval config: default split: test revision: None metrics: - type: map_at_1 value: 67.306 - type: map_at_10 value: 80.634 - type: map_at_100 value: 81.349 - type: map_at_1000 value: 81.37299999999999 - type: map_at_3 value: 77.691 - type: map_at_5 value: 79.512 - type: mrr_at_1 value: 77.56 - type: mrr_at_10 value: 84.177 - type: mrr_at_100 value: 84.35000000000001 - type: mrr_at_1000 value: 84.353 - type: mrr_at_3 value: 83.003 - type: mrr_at_5 value: 83.799 - type: ndcg_at_1 value: 77.58 - type: ndcg_at_10 value: 84.782 - type: ndcg_at_100 value: 86.443 - type: ndcg_at_1000 value: 86.654 - type: ndcg_at_3 value: 81.67 - type: ndcg_at_5 value: 83.356 - type: precision_at_1 value: 77.58 - type: precision_at_10 value: 12.875 - type: precision_at_100 value: 1.503 - type: precision_at_1000 value: 0.156 - type: precision_at_3 value: 35.63 - type: precision_at_5 value: 23.483999999999998 - type: recall_at_1 value: 67.306 - type: recall_at_10 value: 92.64 - type: recall_at_100 value: 98.681 - type: recall_at_1000 value: 99.79 - type: recall_at_3 value: 83.682 - type: recall_at_5 value: 88.424 - task: type: Clustering dataset: type: mteb/reddit-clustering name: MTEB RedditClustering config: default split: test revision: 24640382cdbf8abc73003fb0fa6d111a705499eb metrics: - type: v_measure value: 50.76319866126382 - task: type: Clustering dataset: type: mteb/reddit-clustering-p2p name: MTEB RedditClusteringP2P config: default split: test revision: 282350215ef01743dc01b456c7f5241fa8937f16 metrics: - type: v_measure value: 55.024711941648995 - task: type: Retrieval dataset: type: scidocs name: MTEB SCIDOCS config: default split: test revision: None metrics: - type: map_at_1 value: 3.9379999999999997 - type: map_at_10 value: 8.817 - type: map_at_100 value: 10.546999999999999 - type: map_at_1000 value: 10.852 - type: map_at_3 value: 6.351999999999999 - type: map_at_5 value: 7.453 - type: mrr_at_1 value: 19.400000000000002 - type: mrr_at_10 value: 27.371000000000002 - type: mrr_at_100 value: 28.671999999999997 - type: mrr_at_1000 value: 28.747 - type: mrr_at_3 value: 24.583 - type: mrr_at_5 value: 26.143 - type: ndcg_at_1 value: 19.400000000000002 - type: ndcg_at_10 value: 15.264 - type: ndcg_at_100 value: 22.63 - type: ndcg_at_1000 value: 28.559 - type: ndcg_at_3 value: 14.424999999999999 - type: ndcg_at_5 value: 12.520000000000001 - type: precision_at_1 value: 19.400000000000002 - type: precision_at_10 value: 7.8100000000000005 - type: precision_at_100 value: 1.854 - type: precision_at_1000 value: 0.329 - type: precision_at_3 value: 13.100000000000001 - type: precision_at_5 value: 10.68 - type: recall_at_1 value: 3.9379999999999997 - type: recall_at_10 value: 15.903 - type: recall_at_100 value: 37.645 - type: recall_at_1000 value: 66.86 - type: recall_at_3 value: 7.993 - type: recall_at_5 value: 10.885 - task: type: STS dataset: type: mteb/sickr-sts name: MTEB SICK-R config: default split: test revision: a6ea5a8cab320b040a23452cc28066d9beae2cee metrics: - type: cos_sim_pearson value: 80.12689060151425 - type: cos_sim_spearman value: 70.46515535094771 - type: euclidean_pearson value: 77.17160003557223 - type: euclidean_spearman value: 70.4651757047438 - type: manhattan_pearson value: 77.18129609281937 - type: manhattan_spearman value: 70.46610403752913 - task: type: STS dataset: type: mteb/sts12-sts name: MTEB STS12 config: default split: test revision: a0d554a64d88156834ff5ae9920b964011b16384 metrics: - type: cos_sim_pearson value: 70.451157033355 - type: cos_sim_spearman value: 63.99899601697852 - type: euclidean_pearson value: 67.46985359967678 - type: euclidean_spearman value: 64.00001637764805 - type: manhattan_pearson value: 67.56534741780037 - type: manhattan_spearman value: 64.06533893575366 - task: type: STS dataset: type: mteb/sts13-sts name: MTEB STS13 config: default split: test revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca metrics: - type: cos_sim_pearson value: 77.65086614464292 - type: cos_sim_spearman value: 78.20169706921848 - type: euclidean_pearson value: 77.77758172155283 - type: euclidean_spearman value: 78.20169706921848 - type: manhattan_pearson value: 77.75077884860052 - type: manhattan_spearman value: 78.16875216484164 - task: type: STS dataset: type: mteb/sts14-sts name: MTEB STS14 config: default split: test revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 metrics: - type: cos_sim_pearson value: 76.26381598259717 - type: cos_sim_spearman value: 70.78377709313477 - type: euclidean_pearson value: 74.82646556532096 - type: euclidean_spearman value: 70.78377658155212 - type: manhattan_pearson value: 74.81784766108225 - type: manhattan_spearman value: 70.79351454692176 - task: type: STS dataset: type: mteb/sts15-sts name: MTEB STS15 config: default split: test revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 metrics: - type: cos_sim_pearson value: 79.00532026789739 - type: cos_sim_spearman value: 80.02708383244838 - type: euclidean_pearson value: 79.48345422610525 - type: euclidean_spearman value: 80.02708383244838 - type: manhattan_pearson value: 79.44519739854803 - type: manhattan_spearman value: 79.98344094559687 - task: type: STS dataset: type: mteb/sts16-sts name: MTEB STS16 config: default split: test revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 metrics: - type: cos_sim_pearson value: 77.32783048164805 - type: cos_sim_spearman value: 78.79729961288045 - type: euclidean_pearson value: 78.72111945793154 - type: euclidean_spearman value: 78.79729904606872 - type: manhattan_pearson value: 78.72464311117116 - type: manhattan_spearman value: 78.822591248334 - 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: 82.04318630630854 - type: cos_sim_spearman value: 83.87886389259836 - type: euclidean_pearson value: 83.40385877895086 - type: euclidean_spearman value: 83.87886389259836 - type: manhattan_pearson value: 83.46337128901547 - type: manhattan_spearman value: 83.9723106941644 - 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: 63.003511169944595 - type: cos_sim_spearman value: 64.39318805580227 - type: euclidean_pearson value: 65.4797990735967 - type: euclidean_spearman value: 64.39318805580227 - type: manhattan_pearson value: 65.44604544280844 - type: manhattan_spearman value: 64.38742899984233 - task: type: STS dataset: type: mteb/stsbenchmark-sts name: MTEB STSBenchmark config: default split: test revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 metrics: - type: cos_sim_pearson value: 76.63101237585029 - type: cos_sim_spearman value: 75.57446967644269 - type: euclidean_pearson value: 76.93491768734478 - type: euclidean_spearman value: 75.57446967644269 - type: manhattan_pearson value: 76.92187567800636 - type: manhattan_spearman value: 75.57239337194585 - task: type: Reranking dataset: type: mteb/scidocs-reranking name: MTEB SciDocsRR config: default split: test revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab metrics: - type: map value: 78.5376604868993 - type: mrr value: 92.94422897364073 - task: type: Retrieval dataset: type: scifact name: MTEB SciFact config: default split: test revision: None metrics: - type: map_at_1 value: 38.872 - type: map_at_10 value: 50.417 - type: map_at_100 value: 51.202000000000005 - type: map_at_1000 value: 51.25999999999999 - type: map_at_3 value: 47.02 - type: map_at_5 value: 49.326 - type: mrr_at_1 value: 41.0 - type: mrr_at_10 value: 51.674 - type: mrr_at_100 value: 52.32599999999999 - type: mrr_at_1000 value: 52.376999999999995 - type: mrr_at_3 value: 48.778 - type: mrr_at_5 value: 50.744 - type: ndcg_at_1 value: 41.0 - type: ndcg_at_10 value: 56.027 - type: ndcg_at_100 value: 59.362 - type: ndcg_at_1000 value: 60.839 - type: ndcg_at_3 value: 50.019999999999996 - type: ndcg_at_5 value: 53.644999999999996 - type: precision_at_1 value: 41.0 - type: precision_at_10 value: 8.1 - type: precision_at_100 value: 0.987 - type: precision_at_1000 value: 0.11100000000000002 - type: precision_at_3 value: 20.444000000000003 - type: precision_at_5 value: 14.466999999999999 - type: recall_at_1 value: 38.872 - type: recall_at_10 value: 71.906 - type: recall_at_100 value: 86.367 - type: recall_at_1000 value: 98.0 - type: recall_at_3 value: 56.206 - type: recall_at_5 value: 65.05 - task: type: PairClassification dataset: type: mteb/sprintduplicatequestions-pairclassification name: MTEB SprintDuplicateQuestions config: default split: test revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 metrics: - type: cos_sim_accuracy value: 99.7039603960396 - 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type: v_measure value: 59.663210666678715 - task: type: Clustering dataset: type: mteb/stackexchange-clustering-p2p name: MTEB StackExchangeClusteringP2P config: default split: test revision: 815ca46b2622cec33ccafc3735d572c266efdb44 metrics: - type: v_measure value: 32.107791216468776 - task: type: Reranking dataset: type: mteb/stackoverflowdupquestions-reranking name: MTEB StackOverflowDupQuestions config: default split: test revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 metrics: - type: map value: 46.440691925067604 - type: mrr value: 47.03390257618199 - task: type: Summarization dataset: type: mteb/summeval name: MTEB SummEval config: default split: test revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c metrics: - type: cos_sim_pearson value: 31.067177519784074 - type: cos_sim_spearman value: 31.234728424648967 - type: dot_pearson value: 31.06717083018107 - type: dot_spearman value: 31.234728424648967 - task: type: Retrieval dataset: type: trec-covid name: MTEB TRECCOVID config: default split: test revision: None metrics: - type: map_at_1 value: 0.136 - type: map_at_10 value: 0.767 - type: map_at_100 value: 3.3689999999999998 - type: map_at_1000 value: 8.613999999999999 - type: map_at_3 value: 0.369 - type: map_at_5 value: 0.514 - type: mrr_at_1 value: 48.0 - type: mrr_at_10 value: 63.908 - type: mrr_at_100 value: 64.615 - type: mrr_at_1000 value: 64.615 - type: mrr_at_3 value: 62.0 - type: mrr_at_5 value: 63.4 - type: ndcg_at_1 value: 44.0 - type: ndcg_at_10 value: 38.579 - type: ndcg_at_100 value: 26.409 - type: ndcg_at_1000 value: 26.858999999999998 - type: ndcg_at_3 value: 47.134 - type: ndcg_at_5 value: 43.287 - type: precision_at_1 value: 48.0 - type: precision_at_10 value: 40.400000000000006 - type: precision_at_100 value: 26.640000000000004 - type: precision_at_1000 value: 12.04 - type: precision_at_3 value: 52.666999999999994 - type: precision_at_5 value: 46.800000000000004 - type: recall_at_1 value: 0.136 - type: recall_at_10 value: 1.0070000000000001 - type: recall_at_100 value: 6.318 - type: recall_at_1000 value: 26.522000000000002 - type: recall_at_3 value: 0.41700000000000004 - type: recall_at_5 value: 0.606 - task: type: Retrieval dataset: type: webis-touche2020 name: MTEB Touche2020 config: default split: test revision: None metrics: - type: map_at_1 value: 1.9949999999999999 - type: map_at_10 value: 8.304 - type: map_at_100 value: 13.644 - type: map_at_1000 value: 15.43 - type: map_at_3 value: 4.788 - type: map_at_5 value: 6.22 - type: mrr_at_1 value: 22.448999999999998 - type: mrr_at_10 value: 37.658 - type: mrr_at_100 value: 38.491 - type: mrr_at_1000 value: 38.503 - type: mrr_at_3 value: 32.312999999999995 - type: mrr_at_5 value: 35.68 - type: ndcg_at_1 value: 21.429000000000002 - type: ndcg_at_10 value: 18.995 - type: ndcg_at_100 value: 32.029999999999994 - type: ndcg_at_1000 value: 44.852 - type: ndcg_at_3 value: 19.464000000000002 - type: ndcg_at_5 value: 19.172 - type: precision_at_1 value: 22.448999999999998 - type: precision_at_10 value: 17.143 - type: precision_at_100 value: 6.877999999999999 - type: precision_at_1000 value: 1.524 - type: precision_at_3 value: 21.769 - type: precision_at_5 value: 20.0 - type: recall_at_1 value: 1.9949999999999999 - type: recall_at_10 value: 13.395999999999999 - type: recall_at_100 value: 44.348 - type: recall_at_1000 value: 82.622 - type: recall_at_3 value: 5.896 - type: recall_at_5 value: 8.554 - task: type: Classification dataset: type: mteb/toxic_conversations_50k name: MTEB ToxicConversationsClassification config: default split: test revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c metrics: - type: accuracy value: 67.9394 - type: ap value: 12.943337263423334 - type: f1 value: 52.28243093094156 - task: type: Classification dataset: type: mteb/tweet_sentiment_extraction name: MTEB TweetSentimentExtractionClassification config: default split: test revision: d604517c81ca91fe16a244d1248fc021f9ecee7a metrics: - type: accuracy value: 56.414827391058296 - type: f1 value: 56.666412409573105 - task: type: Clustering dataset: type: mteb/twentynewsgroups-clustering name: MTEB TwentyNewsgroupsClustering config: default split: test revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 metrics: - type: v_measure value: 47.009746255495465 - task: type: PairClassification dataset: type: mteb/twittersemeval2015-pairclassification name: MTEB TwitterSemEval2015 config: default split: test revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 metrics: - type: cos_sim_accuracy value: 84.02574953805807 - type: cos_sim_ap value: 67.66599910763128 - type: cos_sim_f1 value: 63.491277990844985 - type: cos_sim_precision value: 59.77172140694154 - type: cos_sim_recall value: 67.70448548812665 - type: dot_accuracy value: 84.02574953805807 - type: dot_ap value: 67.66600090945406 - type: dot_f1 value: 63.491277990844985 - type: dot_precision value: 59.77172140694154 - type: dot_recall value: 67.70448548812665 - type: euclidean_accuracy value: 84.02574953805807 - type: euclidean_ap value: 67.6659842364448 - type: euclidean_f1 value: 63.491277990844985 - type: euclidean_precision value: 59.77172140694154 - type: euclidean_recall value: 67.70448548812665 - type: manhattan_accuracy value: 84.0317100792752 - type: manhattan_ap value: 67.66351692448987 - type: manhattan_f1 value: 63.48610948306178 - type: manhattan_precision value: 57.11875131828729 - type: manhattan_recall value: 71.45118733509234 - type: max_accuracy value: 84.0317100792752 - type: max_ap value: 67.66600090945406 - type: max_f1 value: 63.491277990844985 - task: type: PairClassification dataset: type: mteb/twitterurlcorpus-pairclassification name: MTEB TwitterURLCorpus config: default split: test revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf metrics: - type: cos_sim_accuracy value: 87.53832421314084 - type: cos_sim_ap value: 83.11416594316626 - type: cos_sim_f1 value: 75.41118114347518 - type: cos_sim_precision value: 73.12839059674504 - type: cos_sim_recall value: 77.8410840776101 - type: dot_accuracy value: 87.53832421314084 - type: dot_ap value: 83.11416226342155 - type: dot_f1 value: 75.41118114347518 - type: dot_precision value: 73.12839059674504 - type: dot_recall value: 77.8410840776101 - type: euclidean_accuracy value: 87.53832421314084 - type: euclidean_ap value: 83.11416284455395 - type: euclidean_f1 value: 75.41118114347518 - type: euclidean_precision value: 73.12839059674504 - type: euclidean_recall value: 77.8410840776101 - type: manhattan_accuracy value: 87.49369348391353 - type: manhattan_ap value: 83.08066812574694 - type: manhattan_f1 value: 75.36561228603892 - type: manhattan_precision value: 71.9202518363064 - type: manhattan_recall value: 79.15768401601478 - type: max_accuracy value: 87.53832421314084 - type: max_ap value: 83.11416594316626 - type: max_f1 value: 75.41118114347518 --- # lodestone-base-4096-v1 [Hum-Works/lodestone-base-4096-v1](https://huggingface.co/Hum-Works/lodestone-base-4096-v1). [Griffin McCauley](https://huggingface.co/gmccaul1), [Will Fortin](https://huggingface.co/willathum), [Dylan DiGioia](https://huggingface.co/dylanAtHum) 2023 This new [sentence-transformers](https://www.SBERT.net) model from [Hum](https://www.hum.works/) maps long sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. ## Abstract In the hopes of furthering Hum's overarching mission of increasing the accessibility and interconnectivity of human knowledge, this model was developed as part of a project intending to boost the maximum input sequence length of sentence embedding models by leveraging recent architectural advances in the design of transformer models such as the incorporation of FlashAttention, Attention with Linear Biases (ALiBi), and Gated Linear Units (GLU). These modifications and enhancements were implemented by the team at MosaicML who designed and constructed the pre-trained [`mosaic-bert-base-seqlen-2048`](https://huggingface.co/mosaicml/mosaic-bert-base-seqlen-2048) model, and more information regarding the details of their development and testing specifications can be found on the model card. While the fine-tuning procedure followed during the course of this project loosely mirrors that of the of the original [Flax-sentence-embeddings](https://huggingface.co/flax-sentence-embeddings) team responsible for the creation of many other popular sentence-transformers models (e.g. [all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2), [all-distilroberta-v1](https://huggingface.co/sentence-transformers/all-distilroberta-v1), and [all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2)), our methodology includes novel techniques for data loading, batch sampling, and model checkpointing intended to improve training efficiency with regards to memory allocation and data storage. Through combining these well-established and proven fine-tuning practices with novel advances in transformer architectural elements, our `lodestone-base-4096-v1` model is able to achieve comparable performance metrics on standard text embedding evaluation benchmarks while also supporting a longer and more robust input sequence length of 4096 while retaining a smaller, more manageable size capable of being run on either a GPU or CPU. ## Usage Using this model becomes relatively easy when you have [sentence-transformers](https://www.SBERT.net) installed. *At the time of publishing, sentence-transformers does not support remote code which is required for flash-attention used by the model. A fork of the sentence-transformers repository that allows remote code execution is provided for convenience. It can be installed using the following command:* ``` pip install git+https://github.com/Hum-Works/sentence-transformers.git pip install einops ``` Then you can use the model like this: ```python from sentence_transformers import SentenceTransformer model = SentenceTransformer('Hum-Works/lodestone-base-4096-v1', trust_remote_code=True, revision='v1.0.0') sentences = ["This is an example sentence", "Each sentence is converted"] embeddings = model.encode(sentences) print(embeddings) ``` *Note: The model will use the openAI/Triton implementation of FlashAttention if installed. This is more performant than the fallback, torch implementation. Some platforms and GPUs may not be supported by Triton - up to date compatibility can be found on [Triton’s github page](https://github.com/openai/triton#compatibility).* ------ ## Background The project aims to train sentence embedding models on very large sentence level datasets using a self-supervised contrastive learning objective. We used the pretrained [`mosaic-bert-base-seqlen-2048`](https://huggingface.co/mosaicml/mosaic-bert-base-seqlen-2048) model and fine-tuned it on a nearly 1.5B sentence pairs dataset. We use a contrastive learning objective: given a sentence from the pair, the model should predict which out of a set of randomly sampled other sentences, was actually paired with it in our dataset. ## Intended uses Our model is intended to be used as a long sentence and paragraph encoder. Given an input text, it outputs a vector containing the semantic information. The sentence vector may be used for information retrieval, clustering, or sentence similarity tasks. ## Training procedure ### Pre-training We use the pretrained [`mosaic-bert-base-seqlen-2048`](https://huggingface.co/mosaicml/mosaic-bert-base-seqlen-2048). Please refer to the model card for more detailed information about the pre-training procedure. ### Fine-tuning We fine-tune the model using a contrastive objective. Formally, we compute the dot product of each possible sentence pairing in the batch. We then apply the cross entropy loss by comparing with true pairs. #### Hyperparameters We trained our model on an ml.g5.4xlarge EC2 instance with 1 NVIDIA A10G Tensor Core GPU. We train the model during 1.4 million steps using a batch size of 16. We use a learning rate warm up of 500. The sequence length during training was limited to 2048 tokens. We used the AdamW optimizer with a 2e-5 learning rate and weight decay of 0.01 (i.e. the default parameter values for SentenceTransformer.fit()). The full training script is accessible in this current repository: `Training.py`. ## Model Architecture By incorporating FlashAttention, [Attention with Linear Biases (ALiBi)](https://arxiv.org/abs/2108.12409), and Gated Linear Units (GLU), this model is able to handle input sequences of 4096, 8x longer than that supported by most comparable sentence embedding models. The model was trained using a sequence length maximum of 2048, but the final model has a maximum sequence length of 4096. This is accomplished by taking advantage of ALiBi’s positional attention extrapolation which has been shown to allow sequence lengths of 2x the initial trained length. ## Full Model Architecture ``` SentenceTransformer( (0): Transformer({'max_seq_length': 4096, 'do_lower_case': False}) with Transformer model: BertModel (1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False}) (2): Normalize() ) ``` #### Training data We use the concatenation from multiple datasets to fine-tune our model. The total number of sentence pairs is nearly 1.5 billion sentences. We sampled each dataset given a weighted probability proportional to its relative contribution to the entire dataset. The breakdown of the dataset can be seen below, and the entire dataset can be publicly accessed and uploaded via the `Dataloading.ipynb` located within this repository. | Dataset | Paper | Number of training tuples | |--------------------------------------------------------|:----------------------------------------:|:--------------------------:| | [Reddit comments (2015-2018)](https://github.com/PolyAI-LDN/conversational-datasets/tree/master/reddit) | [paper](https://arxiv.org/abs/1904.06472) | 726,484,430 | | **[S2ORC](https://github.com/allenai/s2orc) Citation pairs (Abstracts)** | [paper](https://aclanthology.org/2020.acl-main.447/) | 252,102,397 | | **[Reddit posts](https://huggingface.co/datasets/sentence-transformers/reddit-title-body) (Title, Body) pairs** | - | 127,445,911 | | **[Amazon reviews (2018)](https://huggingface.co/datasets/sentence-transformers/embedding-training-data) (Title, Review) pairs** | - | 87,877,725 | | [WikiAnswers](https://github.com/afader/oqa#wikianswers-corpus) Duplicate question pairs | [paper](https://doi.org/10.1145/2623330.2623677) | 77,427,422 | | [PAQ](https://github.com/facebookresearch/PAQ) (Question, Answer) pairs | [paper](https://arxiv.org/abs/2102.07033) | 64,371,441 | | [S2ORC](https://github.com/allenai/s2orc) Citation pairs (Titles) | [paper](https://aclanthology.org/2020.acl-main.447/) | 52,603,982 | | [S2ORC](https://github.com/allenai/s2orc) (Title, Abstract) | [paper](https://aclanthology.org/2020.acl-main.447/) | 41,769,185 | | [Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_title_body_jsonl) (Title, Body) pairs | - | 25,368,423 | | [MS MARCO](https://microsoft.github.io/msmarco/) triplets | [paper](https://doi.org/10.1145/3404835.3462804) | 9,144,553 | | **[Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_title_best_voted_answer_jsonl) (Title, Most Upvoted Answer) pairs** | - | 4,784,250 | | **[Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_titlebody_best_voted_answer_jsonl) (Title+Body, Most Upvoted Answer) pairs** | - | 4,551,660 | | [GOOAQ: Open Question Answering with Diverse Answer Types](https://github.com/allenai/gooaq) | [paper](https://arxiv.org/pdf/2104.08727.pdf) | 3,012,496 | | **[Amazon QA](https://huggingface.co/datasets/sentence-transformers/embedding-training-data)** | - | 2,507,114 | | [Code Search](https://huggingface.co/datasets/code_search_net) | - | 1,375,067 | | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 1,198,260 | | **[AG News]((Title, Description) pairs of news articles from the AG News dataset)** | - | 1,157,745 | | [COCO](https://cocodataset.org/#home) Image captions | [paper](https://link.springer.com/chapter/10.1007%2F978-3-319-10602-1_48) | 828,395| | [SPECTER](https://github.com/allenai/specter) citation triplets | [paper](https://doi.org/10.18653/v1/2020.acl-main.207) | 684,100 | | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Question, Answer) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 681,164 | | [Yahoo Answers](https://www.kaggle.com/soumikrakshit/yahoo-answers-dataset) (Title, Question) | [paper](https://proceedings.neurips.cc/paper/2015/hash/250cf8b51c773f3f8dc8b4be867a9a02-Abstract.html) | 659,896 | | **[CC News](https://huggingface.co/datasets/sentence-transformers/embedding-training-data) (Title, article) pairs** | - | 614,664 | | **[NPR](https://huggingface.co/datasets/sentence-transformers/embedding-training-data) (Title, Body) pairs** | - | 594,384 | | [SearchQA](https://huggingface.co/datasets/search_qa) | [paper](https://arxiv.org/abs/1704.05179) | 582,261 | | **[MS Marco](https://microsoft.github.io/msmarco/) (Query, Answer Passage) pairs** | [paper](https://doi.org/10.1145/3404835.3462804) | 532,751 | | [Stack Exchange](https://docs.google.com/spreadsheets/d/1vXJrIg38cEaKjOG5y4I4PQwAQFUmCkohbViJ9zj_Emg/edit#gid=0) (Title, Body) pairs | - | 364,000 | | [Eli5](https://huggingface.co/datasets/eli5) | [paper](https://doi.org/10.18653/v1/p19-1346) | 325,475 | | [Flickr 30k](https://shannon.cs.illinois.edu/DenotationGraph/) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/229/33) | 317,695 | | **[CNN & DailyMail](https://huggingface.co/datasets/sentence-transformers/embedding-training-data) (highlight sentences, article) pairs** | - | 311,971 | | [Stack Exchange](https://docs.google.com/spreadsheets/d/1vXJrIg38cEaKjOG5y4I4PQwAQFUmCkohbViJ9zj_Emg/edit#gid=0) Duplicate questions (titles) | - | 304,524 | | AllNLI ([SNLI](https://nlp.stanford.edu/projects/snli/) and [MultiNLI](https://cims.nyu.edu/~sbowman/multinli/) | [paper SNLI](https://doi.org/10.18653/v1/d15-1075), [paper MultiNLI](https://doi.org/10.18653/v1/n18-1101) | 277,230 | | [Stack Exchange](https://docs.google.com/spreadsheets/d/1vXJrIg38cEaKjOG5y4I4PQwAQFUmCkohbViJ9zj_Emg/edit#gid=0) Duplicate questions (bodies) | - | 250,518 | | [Stack Exchange](https://docs.google.com/spreadsheets/d/1vXJrIg38cEaKjOG5y4I4PQwAQFUmCkohbViJ9zj_Emg/edit#gid=0) Duplicate questions (titles+bodies) | - | 250,459 | | **[XSUM](https://huggingface.co/datasets/sentence-transformers/embedding-training-data) (Summary, News Article) pairs** | - | 226,711 | | **[Stack Exchange](https://huggingface.co/datasets/flax-sentence-embeddings/stackexchange_titlebody_best_and_down_voted_answer_jsonl) (Title+Body, Most Upvoted Answer, Most Downvoted Answer) triplets** | - | 216,454 | | [Sentence Compression](https://github.com/google-research-datasets/sentence-compression) | [paper](https://www.aclweb.org/anthology/D13-1155/) | 180,000 | | **[FEVER](https://docs.google.com/spreadsheets/d/1vXJrIg38cEaKjOG5y4I4PQwAQFUmCkohbViJ9zj_Emg/edit#gid=0) training data** | - | 139,051 | | [Wikihow](https://github.com/pvl/wikihow_pairs_dataset) | [paper](https://arxiv.org/abs/1810.09305) | 128,542 | | **[SearchQA](https://huggingface.co/datasets/search_qa) (Question, Top-Snippet)** | [paper](https://arxiv.org/abs/1704.05179) | 117,384 | | [Altlex](https://github.com/chridey/altlex/) | [paper](https://aclanthology.org/P16-1135.pdf) | 112,696 | | **[Quora Question Duplicates](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs)** | - | 103,663 | | [Quora Question Triplets](https://quoradata.quora.com/First-Quora-Dataset-Release-Question-Pairs) | - | 103,663 | | [Simple Wikipedia](https://cs.pomona.edu/~dkauchak/simplification/) | [paper](https://www.aclweb.org/anthology/P11-2117/) | 102,225 | | [Natural Questions (NQ)](https://ai.google.com/research/NaturalQuestions) | [paper](https://transacl.org/ojs/index.php/tacl/article/view/1455) | 100,231 | | [SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/) | [paper](https://aclanthology.org/P18-2124.pdf) | 87,599 | | [TriviaQA](https://huggingface.co/datasets/trivia_qa) | - | 73,346 | | **Total** | | **1,492,453,113** | #### Replication The entire fine-tuning process for this model can be replicated by following the steps outlined in the `Replication.txt` file within this repository. This document explains how to modify the [sentence-transformers](https://www.SBERT.net) library, configure the pre-trained [`mosaic-bert-base-seqlen-2048`](https://huggingface.co/mosaicml/mosaic-bert-base-seqlen-2048) model, load all of the training data, and execute the training script. #### Limitations Due to technical constraints (e.g. limited GPU memory capacity), this model was trained with a smaller batch size of 16, making it so that each step during training was less well-informed than it would have been on a higher performance system. This smaller than ideal hyperparameter value will generally cause the model to be more likely to get stuck in a local minimum and for the parameter configuration to take a longer time to converge to the optimum. In order to counteract this potential risk, we trained the model for a larger number of steps than many of its contemporaries to ensure a greater chance of achieving strong performance, but this is an area which could be improved if further fine-tuning was performed. It is also worth noting that, while this model is able to handle longer input sequences of up to 4096 word pieces, the training dataset used consists of sentence and paragraph pairs and triplets which do not necessarily reach that maximum sequence length. Since the data was not tailored specifically for this larger input size, further fine-tuning may be required to ensure highly accurate embeddings for longer texts of that magnitude. Finally, as stated on https://huggingface.co/datasets/sentence-transformers/reddit-title-body, an additional reminder and warning regarding the Reddit posts data is that one should "Be aware that this dataset is not filtered for biases, hate-speech, spam, racial slurs etc. It depicts the content as it is posted on Reddit." Thus, while we believe this has not induced any pathological behaviors in the model's performance due to its relatively low prevalence of records in the whole dataset of nearly 1.5B sentence pairs and the fact that this model was trained to produce semantic embeddings rather than generative text outputs, it is always important to be aware of vulnerabilities to bias.