| --- |
| library_name: sentence-transformers |
| pipeline_tag: sentence-similarity |
| tags: |
| - feature-extraction |
| - sentence-similarity |
| - mteb |
| - transformers |
| - transformers.js |
| model-index: |
| - name: epoch_0_model |
| results: |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_counterfactual |
| name: MTEB AmazonCounterfactualClassification (en) |
| config: en |
| split: test |
| revision: e8379541af4e31359cca9fbcf4b00f2671dba205 |
| metrics: |
| - type: accuracy |
| value: 76.8507462686567 |
| - type: ap |
| value: 40.592189159090495 |
| - type: f1 |
| value: 71.01634655512476 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_polarity |
| name: MTEB AmazonPolarityClassification |
| config: default |
| split: test |
| revision: e2d317d38cd51312af73b3d32a06d1a08b442046 |
| metrics: |
| - type: accuracy |
| value: 91.51892500000001 |
| - type: ap |
| value: 88.50346762975335 |
| - type: f1 |
| value: 91.50342077459624 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_reviews_multi |
| name: MTEB AmazonReviewsClassification (en) |
| config: en |
| split: test |
| revision: 1399c76144fd37290681b995c656ef9b2e06e26d |
| metrics: |
| - type: accuracy |
| value: 47.364 |
| - type: f1 |
| value: 46.72708080922794 |
| - task: |
| type: Retrieval |
| dataset: |
| type: arguana |
| name: MTEB ArguAna |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 25.178 |
| - type: map_at_10 |
| value: 40.244 |
| - type: map_at_100 |
| value: 41.321999999999996 |
| - type: map_at_1000 |
| value: 41.331 |
| - type: map_at_3 |
| value: 35.016999999999996 |
| - type: map_at_5 |
| value: 37.99 |
| - type: mrr_at_1 |
| value: 25.605 |
| - type: mrr_at_10 |
| value: 40.422000000000004 |
| - type: mrr_at_100 |
| value: 41.507 |
| - type: mrr_at_1000 |
| value: 41.516 |
| - type: mrr_at_3 |
| value: 35.23 |
| - type: mrr_at_5 |
| value: 38.15 |
| - type: ndcg_at_1 |
| value: 25.178 |
| - type: ndcg_at_10 |
| value: 49.258 |
| - type: ndcg_at_100 |
| value: 53.776 |
| - type: ndcg_at_1000 |
| value: 53.995000000000005 |
| - type: ndcg_at_3 |
| value: 38.429 |
| - type: ndcg_at_5 |
| value: 43.803 |
| - type: precision_at_1 |
| value: 25.178 |
| - type: precision_at_10 |
| value: 7.831 |
| - type: precision_at_100 |
| value: 0.979 |
| - type: precision_at_1000 |
| value: 0.1 |
| - type: precision_at_3 |
| value: 16.121 |
| - type: precision_at_5 |
| value: 12.29 |
| - type: recall_at_1 |
| value: 25.178 |
| - type: recall_at_10 |
| value: 78.307 |
| - type: recall_at_100 |
| value: 97.866 |
| - type: recall_at_1000 |
| value: 99.57300000000001 |
| - type: recall_at_3 |
| value: 48.364000000000004 |
| - type: recall_at_5 |
| value: 61.451 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/arxiv-clustering-p2p |
| name: MTEB ArxivClusteringP2P |
| config: default |
| split: test |
| revision: a122ad7f3f0291bf49cc6f4d32aa80929df69d5d |
| metrics: |
| - type: v_measure |
| value: 45.93034494751465 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/arxiv-clustering-s2s |
| name: MTEB ArxivClusteringS2S |
| config: default |
| split: test |
| revision: f910caf1a6075f7329cdf8c1a6135696f37dbd53 |
| metrics: |
| - type: v_measure |
| value: 36.64579480054327 |
| - task: |
| type: Reranking |
| dataset: |
| type: mteb/askubuntudupquestions-reranking |
| name: MTEB AskUbuntuDupQuestions |
| config: default |
| split: test |
| revision: 2000358ca161889fa9c082cb41daa8dcfb161a54 |
| metrics: |
| - type: map |
| value: 60.601310529222054 |
| - type: mrr |
| value: 75.04484896451656 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/biosses-sts |
| name: MTEB BIOSSES |
| config: default |
| split: test |
| revision: d3fb88f8f02e40887cd149695127462bbcf29b4a |
| metrics: |
| - type: cos_sim_pearson |
| value: 88.57797718095814 |
| - type: cos_sim_spearman |
| value: 86.47064499110101 |
| - type: euclidean_pearson |
| value: 87.4559602783142 |
| - type: euclidean_spearman |
| value: 86.47064499110101 |
| - type: manhattan_pearson |
| value: 87.7232764230245 |
| - type: manhattan_spearman |
| value: 86.91222131777742 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/banking77 |
| name: MTEB Banking77Classification |
| config: default |
| split: test |
| revision: 0fd18e25b25c072e09e0d92ab615fda904d66300 |
| metrics: |
| - type: accuracy |
| value: 84.5422077922078 |
| - type: f1 |
| value: 84.47657456950589 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/biorxiv-clustering-p2p |
| name: MTEB BiorxivClusteringP2P |
| config: default |
| split: test |
| revision: 65b79d1d13f80053f67aca9498d9402c2d9f1f40 |
| metrics: |
| - type: v_measure |
| value: 38.48953561974464 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/biorxiv-clustering-s2s |
| name: MTEB BiorxivClusteringS2S |
| config: default |
| split: test |
| revision: 258694dd0231531bc1fd9de6ceb52a0853c6d908 |
| metrics: |
| - type: v_measure |
| value: 32.75995857510105 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackAndroidRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 30.008000000000003 |
| - type: map_at_10 |
| value: 39.51 |
| - type: map_at_100 |
| value: 40.841 |
| - type: map_at_1000 |
| value: 40.973 |
| - type: map_at_3 |
| value: 36.248999999999995 |
| - type: map_at_5 |
| value: 38.096999999999994 |
| - type: mrr_at_1 |
| value: 36.481 |
| - type: mrr_at_10 |
| value: 44.818000000000005 |
| - type: mrr_at_100 |
| value: 45.64 |
| - type: mrr_at_1000 |
| value: 45.687 |
| - type: mrr_at_3 |
| value: 42.036 |
| - type: mrr_at_5 |
| value: 43.782 |
| - type: ndcg_at_1 |
| value: 36.481 |
| - type: ndcg_at_10 |
| value: 45.152 |
| - type: ndcg_at_100 |
| value: 50.449 |
| - type: ndcg_at_1000 |
| value: 52.76499999999999 |
| - type: ndcg_at_3 |
| value: 40.161 |
| - type: ndcg_at_5 |
| value: 42.577999999999996 |
| - type: precision_at_1 |
| value: 36.481 |
| - type: precision_at_10 |
| value: 8.369 |
| - type: precision_at_100 |
| value: 1.373 |
| - type: precision_at_1000 |
| value: 0.186 |
| - type: precision_at_3 |
| value: 18.693 |
| - type: precision_at_5 |
| value: 13.533999999999999 |
| - type: recall_at_1 |
| value: 30.008000000000003 |
| - type: recall_at_10 |
| value: 56.108999999999995 |
| - type: recall_at_100 |
| value: 78.55499999999999 |
| - type: recall_at_1000 |
| value: 93.659 |
| - type: recall_at_3 |
| value: 41.754999999999995 |
| - type: recall_at_5 |
| value: 48.296 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackEnglishRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 30.262 |
| - type: map_at_10 |
| value: 40.139 |
| - type: map_at_100 |
| value: 41.394 |
| - type: map_at_1000 |
| value: 41.526 |
| - type: map_at_3 |
| value: 37.155 |
| - type: map_at_5 |
| value: 38.785 |
| - type: mrr_at_1 |
| value: 38.153 |
| - type: mrr_at_10 |
| value: 46.369 |
| - type: mrr_at_100 |
| value: 47.072 |
| - type: mrr_at_1000 |
| value: 47.111999999999995 |
| - type: mrr_at_3 |
| value: 44.268 |
| - type: mrr_at_5 |
| value: 45.389 |
| - type: ndcg_at_1 |
| value: 38.153 |
| - type: ndcg_at_10 |
| value: 45.925 |
| - type: ndcg_at_100 |
| value: 50.394000000000005 |
| - type: ndcg_at_1000 |
| value: 52.37500000000001 |
| - type: ndcg_at_3 |
| value: 41.754000000000005 |
| - type: ndcg_at_5 |
| value: 43.574 |
| - type: precision_at_1 |
| value: 38.153 |
| - type: precision_at_10 |
| value: 8.796 |
| - type: precision_at_100 |
| value: 1.432 |
| - type: precision_at_1000 |
| value: 0.189 |
| - type: precision_at_3 |
| value: 20.318 |
| - type: precision_at_5 |
| value: 14.395 |
| - type: recall_at_1 |
| value: 30.262 |
| - type: recall_at_10 |
| value: 55.72200000000001 |
| - type: recall_at_100 |
| value: 74.97500000000001 |
| - type: recall_at_1000 |
| value: 87.342 |
| - type: recall_at_3 |
| value: 43.129 |
| - type: recall_at_5 |
| value: 48.336 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackGamingRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 39.951 |
| - type: map_at_10 |
| value: 51.248000000000005 |
| - type: map_at_100 |
| value: 52.188 |
| - type: map_at_1000 |
| value: 52.247 |
| - type: map_at_3 |
| value: 48.211 |
| - type: map_at_5 |
| value: 49.797000000000004 |
| - type: mrr_at_1 |
| value: 45.329 |
| - type: mrr_at_10 |
| value: 54.749 |
| - type: mrr_at_100 |
| value: 55.367999999999995 |
| - type: mrr_at_1000 |
| value: 55.400000000000006 |
| - type: mrr_at_3 |
| value: 52.382 |
| - type: mrr_at_5 |
| value: 53.649 |
| - type: ndcg_at_1 |
| value: 45.329 |
| - type: ndcg_at_10 |
| value: 56.847 |
| - type: ndcg_at_100 |
| value: 60.738 |
| - type: ndcg_at_1000 |
| value: 61.976 |
| - type: ndcg_at_3 |
| value: 51.59 |
| - type: ndcg_at_5 |
| value: 53.915 |
| - type: precision_at_1 |
| value: 45.329 |
| - type: precision_at_10 |
| value: 8.959 |
| - type: precision_at_100 |
| value: 1.187 |
| - type: precision_at_1000 |
| value: 0.134 |
| - type: precision_at_3 |
| value: 22.612 |
| - type: precision_at_5 |
| value: 15.273 |
| - type: recall_at_1 |
| value: 39.951 |
| - type: recall_at_10 |
| value: 70.053 |
| - type: recall_at_100 |
| value: 86.996 |
| - type: recall_at_1000 |
| value: 95.707 |
| - type: recall_at_3 |
| value: 56.032000000000004 |
| - type: recall_at_5 |
| value: 61.629999999999995 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackGisRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 25.566 |
| - type: map_at_10 |
| value: 33.207 |
| - type: map_at_100 |
| value: 34.166000000000004 |
| - type: map_at_1000 |
| value: 34.245 |
| - type: map_at_3 |
| value: 30.94 |
| - type: map_at_5 |
| value: 32.01 |
| - type: mrr_at_1 |
| value: 27.345000000000002 |
| - type: mrr_at_10 |
| value: 35.193000000000005 |
| - type: mrr_at_100 |
| value: 35.965 |
| - type: mrr_at_1000 |
| value: 36.028999999999996 |
| - type: mrr_at_3 |
| value: 32.806000000000004 |
| - type: mrr_at_5 |
| value: 34.021 |
| - type: ndcg_at_1 |
| value: 27.345000000000002 |
| - type: ndcg_at_10 |
| value: 37.891999999999996 |
| - type: ndcg_at_100 |
| value: 42.664 |
| - type: ndcg_at_1000 |
| value: 44.757000000000005 |
| - type: ndcg_at_3 |
| value: 33.123000000000005 |
| - type: ndcg_at_5 |
| value: 35.035 |
| - type: precision_at_1 |
| value: 27.345000000000002 |
| - type: precision_at_10 |
| value: 5.763 |
| - type: precision_at_100 |
| value: 0.859 |
| - type: precision_at_1000 |
| value: 0.108 |
| - type: precision_at_3 |
| value: 13.71 |
| - type: precision_at_5 |
| value: 9.401 |
| - type: recall_at_1 |
| value: 25.566 |
| - type: recall_at_10 |
| value: 50.563 |
| - type: recall_at_100 |
| value: 72.86399999999999 |
| - type: recall_at_1000 |
| value: 88.68599999999999 |
| - type: recall_at_3 |
| value: 37.43 |
| - type: recall_at_5 |
| value: 41.894999999999996 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackMathematicaRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 16.663 |
| - type: map_at_10 |
| value: 23.552 |
| - type: map_at_100 |
| value: 24.538 |
| - type: map_at_1000 |
| value: 24.661 |
| - type: map_at_3 |
| value: 21.085 |
| - type: map_at_5 |
| value: 22.391 |
| - type: mrr_at_1 |
| value: 20.025000000000002 |
| - type: mrr_at_10 |
| value: 27.643 |
| - type: mrr_at_100 |
| value: 28.499999999999996 |
| - type: mrr_at_1000 |
| value: 28.582 |
| - type: mrr_at_3 |
| value: 25.083 |
| - type: mrr_at_5 |
| value: 26.544 |
| - type: ndcg_at_1 |
| value: 20.025000000000002 |
| - type: ndcg_at_10 |
| value: 28.272000000000002 |
| - type: ndcg_at_100 |
| value: 33.353 |
| - type: ndcg_at_1000 |
| value: 36.454 |
| - type: ndcg_at_3 |
| value: 23.579 |
| - type: ndcg_at_5 |
| value: 25.685000000000002 |
| - type: precision_at_1 |
| value: 20.025000000000002 |
| - type: precision_at_10 |
| value: 5.187 |
| - type: precision_at_100 |
| value: 0.897 |
| - type: precision_at_1000 |
| value: 0.13 |
| - type: precision_at_3 |
| value: 10.987 |
| - type: precision_at_5 |
| value: 8.06 |
| - type: recall_at_1 |
| value: 16.663 |
| - type: recall_at_10 |
| value: 38.808 |
| - type: recall_at_100 |
| value: 61.305 |
| - type: recall_at_1000 |
| value: 83.571 |
| - type: recall_at_3 |
| value: 25.907999999999998 |
| - type: recall_at_5 |
| value: 31.214 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackPhysicsRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 27.695999999999998 |
| - type: map_at_10 |
| value: 37.018 |
| - type: map_at_100 |
| value: 38.263000000000005 |
| - type: map_at_1000 |
| value: 38.371 |
| - type: map_at_3 |
| value: 34.226 |
| - type: map_at_5 |
| value: 35.809999999999995 |
| - type: mrr_at_1 |
| value: 32.916000000000004 |
| - type: mrr_at_10 |
| value: 42.067 |
| - type: mrr_at_100 |
| value: 42.925000000000004 |
| - type: mrr_at_1000 |
| value: 42.978 |
| - type: mrr_at_3 |
| value: 39.637 |
| - type: mrr_at_5 |
| value: 41.134 |
| - type: ndcg_at_1 |
| value: 32.916000000000004 |
| - type: ndcg_at_10 |
| value: 42.539 |
| - type: ndcg_at_100 |
| value: 47.873 |
| - type: ndcg_at_1000 |
| value: 50.08200000000001 |
| - type: ndcg_at_3 |
| value: 37.852999999999994 |
| - type: ndcg_at_5 |
| value: 40.201 |
| - type: precision_at_1 |
| value: 32.916000000000004 |
| - type: precision_at_10 |
| value: 7.5840000000000005 |
| - type: precision_at_100 |
| value: 1.199 |
| - type: precision_at_1000 |
| value: 0.155 |
| - type: precision_at_3 |
| value: 17.485 |
| - type: precision_at_5 |
| value: 12.512 |
| - type: recall_at_1 |
| value: 27.695999999999998 |
| - type: recall_at_10 |
| value: 53.638 |
| - type: recall_at_100 |
| value: 76.116 |
| - type: recall_at_1000 |
| value: 91.069 |
| - type: recall_at_3 |
| value: 41.13 |
| - type: recall_at_5 |
| value: 46.872 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackProgrammersRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 24.108 |
| - type: map_at_10 |
| value: 33.372 |
| - type: map_at_100 |
| value: 34.656 |
| - type: map_at_1000 |
| value: 34.768 |
| - type: map_at_3 |
| value: 30.830999999999996 |
| - type: map_at_5 |
| value: 32.204 |
| - type: mrr_at_1 |
| value: 29.110000000000003 |
| - type: mrr_at_10 |
| value: 37.979 |
| - type: mrr_at_100 |
| value: 38.933 |
| - type: mrr_at_1000 |
| value: 38.988 |
| - type: mrr_at_3 |
| value: 35.731 |
| - type: mrr_at_5 |
| value: 36.963 |
| - type: ndcg_at_1 |
| value: 29.110000000000003 |
| - type: ndcg_at_10 |
| value: 38.635000000000005 |
| - type: ndcg_at_100 |
| value: 44.324999999999996 |
| - type: ndcg_at_1000 |
| value: 46.747 |
| - type: ndcg_at_3 |
| value: 34.37 |
| - type: ndcg_at_5 |
| value: 36.228 |
| - type: precision_at_1 |
| value: 29.110000000000003 |
| - type: precision_at_10 |
| value: 6.963 |
| - type: precision_at_100 |
| value: 1.146 |
| - type: precision_at_1000 |
| value: 0.152 |
| - type: precision_at_3 |
| value: 16.400000000000002 |
| - type: precision_at_5 |
| value: 11.552999999999999 |
| - type: recall_at_1 |
| value: 24.108 |
| - type: recall_at_10 |
| value: 49.597 |
| - type: recall_at_100 |
| value: 73.88900000000001 |
| - type: recall_at_1000 |
| value: 90.62400000000001 |
| - type: recall_at_3 |
| value: 37.662 |
| - type: recall_at_5 |
| value: 42.565 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 25.00791666666667 |
| - type: map_at_10 |
| value: 33.287749999999996 |
| - type: map_at_100 |
| value: 34.41141666666667 |
| - type: map_at_1000 |
| value: 34.52583333333333 |
| - type: map_at_3 |
| value: 30.734416666666668 |
| - type: map_at_5 |
| value: 32.137166666666666 |
| - type: mrr_at_1 |
| value: 29.305666666666664 |
| - type: mrr_at_10 |
| value: 37.22966666666666 |
| - type: mrr_at_100 |
| value: 38.066583333333334 |
| - type: mrr_at_1000 |
| value: 38.12616666666667 |
| - type: mrr_at_3 |
| value: 34.92275 |
| - type: mrr_at_5 |
| value: 36.23333333333334 |
| - type: ndcg_at_1 |
| value: 29.305666666666664 |
| - type: ndcg_at_10 |
| value: 38.25533333333333 |
| - type: ndcg_at_100 |
| value: 43.25266666666666 |
| - type: ndcg_at_1000 |
| value: 45.63583333333334 |
| - type: ndcg_at_3 |
| value: 33.777166666666666 |
| - type: ndcg_at_5 |
| value: 35.85 |
| - type: precision_at_1 |
| value: 29.305666666666664 |
| - type: precision_at_10 |
| value: 6.596416666666667 |
| - type: precision_at_100 |
| value: 1.0784166666666668 |
| - type: precision_at_1000 |
| value: 0.14666666666666664 |
| - type: precision_at_3 |
| value: 15.31075 |
| - type: precision_at_5 |
| value: 10.830916666666667 |
| - type: recall_at_1 |
| value: 25.00791666666667 |
| - type: recall_at_10 |
| value: 49.10933333333333 |
| - type: recall_at_100 |
| value: 71.09216666666667 |
| - type: recall_at_1000 |
| value: 87.77725000000001 |
| - type: recall_at_3 |
| value: 36.660916666666665 |
| - type: recall_at_5 |
| value: 41.94149999999999 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackStatsRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 23.521 |
| - type: map_at_10 |
| value: 30.043 |
| - type: map_at_100 |
| value: 30.936000000000003 |
| - type: map_at_1000 |
| value: 31.022 |
| - type: map_at_3 |
| value: 27.926000000000002 |
| - type: map_at_5 |
| value: 29.076999999999998 |
| - type: mrr_at_1 |
| value: 26.227 |
| - type: mrr_at_10 |
| value: 32.822 |
| - type: mrr_at_100 |
| value: 33.61 |
| - type: mrr_at_1000 |
| value: 33.672000000000004 |
| - type: mrr_at_3 |
| value: 30.776999999999997 |
| - type: mrr_at_5 |
| value: 31.866 |
| - type: ndcg_at_1 |
| value: 26.227 |
| - type: ndcg_at_10 |
| value: 34.041 |
| - type: ndcg_at_100 |
| value: 38.394 |
| - type: ndcg_at_1000 |
| value: 40.732 |
| - type: ndcg_at_3 |
| value: 30.037999999999997 |
| - type: ndcg_at_5 |
| value: 31.845000000000002 |
| - type: precision_at_1 |
| value: 26.227 |
| - type: precision_at_10 |
| value: 5.244999999999999 |
| - type: precision_at_100 |
| value: 0.808 |
| - type: precision_at_1000 |
| value: 0.107 |
| - type: precision_at_3 |
| value: 12.679000000000002 |
| - type: precision_at_5 |
| value: 8.773 |
| - type: recall_at_1 |
| value: 23.521 |
| - type: recall_at_10 |
| value: 43.633 |
| - type: recall_at_100 |
| value: 63.126000000000005 |
| - type: recall_at_1000 |
| value: 80.765 |
| - type: recall_at_3 |
| value: 32.614 |
| - type: recall_at_5 |
| value: 37.15 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackTexRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 16.236 |
| - type: map_at_10 |
| value: 22.898 |
| - type: map_at_100 |
| value: 23.878 |
| - type: map_at_1000 |
| value: 24.009 |
| - type: map_at_3 |
| value: 20.87 |
| - type: map_at_5 |
| value: 22.025 |
| - type: mrr_at_1 |
| value: 19.339000000000002 |
| - type: mrr_at_10 |
| value: 26.382 |
| - type: mrr_at_100 |
| value: 27.245 |
| - type: mrr_at_1000 |
| value: 27.33 |
| - type: mrr_at_3 |
| value: 24.386 |
| - type: mrr_at_5 |
| value: 25.496000000000002 |
| - type: ndcg_at_1 |
| value: 19.339000000000002 |
| - type: ndcg_at_10 |
| value: 27.139999999999997 |
| - type: ndcg_at_100 |
| value: 31.944 |
| - type: ndcg_at_1000 |
| value: 35.077999999999996 |
| - type: ndcg_at_3 |
| value: 23.424 |
| - type: ndcg_at_5 |
| value: 25.188 |
| - type: precision_at_1 |
| value: 19.339000000000002 |
| - type: precision_at_10 |
| value: 4.8309999999999995 |
| - type: precision_at_100 |
| value: 0.845 |
| - type: precision_at_1000 |
| value: 0.128 |
| - type: precision_at_3 |
| value: 10.874 |
| - type: precision_at_5 |
| value: 7.825 |
| - type: recall_at_1 |
| value: 16.236 |
| - type: recall_at_10 |
| value: 36.513 |
| - type: recall_at_100 |
| value: 57.999 |
| - type: recall_at_1000 |
| value: 80.512 |
| - type: recall_at_3 |
| value: 26.179999999999996 |
| - type: recall_at_5 |
| value: 30.712 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackUnixRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 24.11 |
| - type: map_at_10 |
| value: 31.566 |
| - type: map_at_100 |
| value: 32.647 |
| - type: map_at_1000 |
| value: 32.753 |
| - type: map_at_3 |
| value: 29.24 |
| - type: map_at_5 |
| value: 30.564999999999998 |
| - type: mrr_at_1 |
| value: 28.265 |
| - type: mrr_at_10 |
| value: 35.504000000000005 |
| - type: mrr_at_100 |
| value: 36.436 |
| - type: mrr_at_1000 |
| value: 36.503 |
| - type: mrr_at_3 |
| value: 33.349000000000004 |
| - type: mrr_at_5 |
| value: 34.622 |
| - type: ndcg_at_1 |
| value: 28.265 |
| - type: ndcg_at_10 |
| value: 36.192 |
| - type: ndcg_at_100 |
| value: 41.388000000000005 |
| - type: ndcg_at_1000 |
| value: 43.948 |
| - type: ndcg_at_3 |
| value: 31.959 |
| - type: ndcg_at_5 |
| value: 33.998 |
| - type: precision_at_1 |
| value: 28.265 |
| - type: precision_at_10 |
| value: 5.989 |
| - type: precision_at_100 |
| value: 0.9650000000000001 |
| - type: precision_at_1000 |
| value: 0.13 |
| - type: precision_at_3 |
| value: 14.335 |
| - type: precision_at_5 |
| value: 10.112 |
| - type: recall_at_1 |
| value: 24.11 |
| - type: recall_at_10 |
| value: 46.418 |
| - type: recall_at_100 |
| value: 69.314 |
| - type: recall_at_1000 |
| value: 87.397 |
| - type: recall_at_3 |
| value: 34.724 |
| - type: recall_at_5 |
| value: 39.925 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackWebmastersRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 22.091 |
| - type: map_at_10 |
| value: 29.948999999999998 |
| - type: map_at_100 |
| value: 31.502000000000002 |
| - type: map_at_1000 |
| value: 31.713 |
| - type: map_at_3 |
| value: 27.464 |
| - type: map_at_5 |
| value: 28.968 |
| - type: mrr_at_1 |
| value: 26.482 |
| - type: mrr_at_10 |
| value: 34.009 |
| - type: mrr_at_100 |
| value: 35.081 |
| - type: mrr_at_1000 |
| value: 35.138000000000005 |
| - type: mrr_at_3 |
| value: 31.785000000000004 |
| - type: mrr_at_5 |
| value: 33.178999999999995 |
| - type: ndcg_at_1 |
| value: 26.482 |
| - type: ndcg_at_10 |
| value: 35.008 |
| - type: ndcg_at_100 |
| value: 41.272999999999996 |
| - type: ndcg_at_1000 |
| value: 43.972 |
| - type: ndcg_at_3 |
| value: 30.804 |
| - type: ndcg_at_5 |
| value: 33.046 |
| - type: precision_at_1 |
| value: 26.482 |
| - type: precision_at_10 |
| value: 6.462 |
| - type: precision_at_100 |
| value: 1.431 |
| - type: precision_at_1000 |
| value: 0.22899999999999998 |
| - type: precision_at_3 |
| value: 14.360999999999999 |
| - type: precision_at_5 |
| value: 10.474 |
| - type: recall_at_1 |
| value: 22.091 |
| - type: recall_at_10 |
| value: 45.125 |
| - type: recall_at_100 |
| value: 72.313 |
| - type: recall_at_1000 |
| value: 89.503 |
| - type: recall_at_3 |
| value: 33.158 |
| - type: recall_at_5 |
| value: 39.086999999999996 |
| - task: |
| type: Retrieval |
| dataset: |
| type: BeIR/cqadupstack |
| name: MTEB CQADupstackWordpressRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 19.883 |
| - type: map_at_10 |
| value: 26.951000000000004 |
| - type: map_at_100 |
| value: 27.927999999999997 |
| - type: map_at_1000 |
| value: 28.022000000000002 |
| - type: map_at_3 |
| value: 24.616 |
| - type: map_at_5 |
| value: 25.917 |
| - type: mrr_at_1 |
| value: 21.996 |
| - type: mrr_at_10 |
| value: 29.221000000000004 |
| - type: mrr_at_100 |
| value: 30.024 |
| - type: mrr_at_1000 |
| value: 30.095 |
| - type: mrr_at_3 |
| value: 26.833000000000002 |
| - type: mrr_at_5 |
| value: 28.155 |
| - type: ndcg_at_1 |
| value: 21.996 |
| - type: ndcg_at_10 |
| value: 31.421 |
| - type: ndcg_at_100 |
| value: 36.237 |
| - type: ndcg_at_1000 |
| value: 38.744 |
| - type: ndcg_at_3 |
| value: 26.671 |
| - type: ndcg_at_5 |
| value: 28.907 |
| - type: precision_at_1 |
| value: 21.996 |
| - type: precision_at_10 |
| value: 5.009 |
| - type: precision_at_100 |
| value: 0.799 |
| - type: precision_at_1000 |
| value: 0.11199999999999999 |
| - type: precision_at_3 |
| value: 11.275 |
| - type: precision_at_5 |
| value: 8.059 |
| - type: recall_at_1 |
| value: 19.883 |
| - type: recall_at_10 |
| value: 43.132999999999996 |
| - type: recall_at_100 |
| value: 65.654 |
| - type: recall_at_1000 |
| value: 84.492 |
| - type: recall_at_3 |
| value: 30.209000000000003 |
| - type: recall_at_5 |
| value: 35.616 |
| - task: |
| type: Retrieval |
| dataset: |
| type: climate-fever |
| name: MTEB ClimateFEVER |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 17.756 |
| - type: map_at_10 |
| value: 30.378 |
| - type: map_at_100 |
| value: 32.537 |
| - type: map_at_1000 |
| value: 32.717 |
| - type: map_at_3 |
| value: 25.599 |
| - type: map_at_5 |
| value: 28.372999999999998 |
| - type: mrr_at_1 |
| value: 41.303 |
| - type: mrr_at_10 |
| value: 53.483999999999995 |
| - type: mrr_at_100 |
| value: 54.106 |
| - type: mrr_at_1000 |
| value: 54.127 |
| - type: mrr_at_3 |
| value: 50.315 |
| - type: mrr_at_5 |
| value: 52.396 |
| - type: ndcg_at_1 |
| value: 41.303 |
| - type: ndcg_at_10 |
| value: 40.503 |
| - type: ndcg_at_100 |
| value: 47.821000000000005 |
| - type: ndcg_at_1000 |
| value: 50.788 |
| - type: ndcg_at_3 |
| value: 34.364 |
| - type: ndcg_at_5 |
| value: 36.818 |
| - type: precision_at_1 |
| value: 41.303 |
| - type: precision_at_10 |
| value: 12.463000000000001 |
| - type: precision_at_100 |
| value: 2.037 |
| - type: precision_at_1000 |
| value: 0.26 |
| - type: precision_at_3 |
| value: 25.798 |
| - type: precision_at_5 |
| value: 19.896 |
| - type: recall_at_1 |
| value: 17.756 |
| - type: recall_at_10 |
| value: 46.102 |
| - type: recall_at_100 |
| value: 70.819 |
| - type: recall_at_1000 |
| value: 87.21799999999999 |
| - type: recall_at_3 |
| value: 30.646 |
| - type: recall_at_5 |
| value: 38.022 |
| - task: |
| type: Retrieval |
| dataset: |
| type: dbpedia-entity |
| name: MTEB DBPedia |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 9.033 |
| - type: map_at_10 |
| value: 20.584 |
| - type: map_at_100 |
| value: 29.518 |
| - type: map_at_1000 |
| value: 31.186000000000003 |
| - type: map_at_3 |
| value: 14.468 |
| - type: map_at_5 |
| value: 17.177 |
| - type: mrr_at_1 |
| value: 69.75 |
| - type: mrr_at_10 |
| value: 77.025 |
| - type: mrr_at_100 |
| value: 77.36699999999999 |
| - type: mrr_at_1000 |
| value: 77.373 |
| - type: mrr_at_3 |
| value: 75.583 |
| - type: mrr_at_5 |
| value: 76.396 |
| - type: ndcg_at_1 |
| value: 58.5 |
| - type: ndcg_at_10 |
| value: 45.033 |
| - type: ndcg_at_100 |
| value: 49.071 |
| - type: ndcg_at_1000 |
| value: 56.056 |
| - type: ndcg_at_3 |
| value: 49.936 |
| - type: ndcg_at_5 |
| value: 47.471999999999994 |
| - type: precision_at_1 |
| value: 69.75 |
| - type: precision_at_10 |
| value: 35.775 |
| - type: precision_at_100 |
| value: 11.594999999999999 |
| - type: precision_at_1000 |
| value: 2.062 |
| - type: precision_at_3 |
| value: 52.5 |
| - type: precision_at_5 |
| value: 45.300000000000004 |
| - type: recall_at_1 |
| value: 9.033 |
| - type: recall_at_10 |
| value: 26.596999999999998 |
| - type: recall_at_100 |
| value: 54.607000000000006 |
| - type: recall_at_1000 |
| value: 76.961 |
| - type: recall_at_3 |
| value: 15.754999999999999 |
| - type: recall_at_5 |
| value: 20.033 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/emotion |
| name: MTEB EmotionClassification |
| config: default |
| split: test |
| revision: 4f58c6b202a23cf9a4da393831edf4f9183cad37 |
| metrics: |
| - type: accuracy |
| value: 48.345000000000006 |
| - type: f1 |
| value: 43.4514918068706 |
| - task: |
| type: Retrieval |
| dataset: |
| type: fever |
| name: MTEB FEVER |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 71.29100000000001 |
| - type: map_at_10 |
| value: 81.059 |
| - type: map_at_100 |
| value: 81.341 |
| - type: map_at_1000 |
| value: 81.355 |
| - type: map_at_3 |
| value: 79.74799999999999 |
| - type: map_at_5 |
| value: 80.612 |
| - type: mrr_at_1 |
| value: 76.40299999999999 |
| - type: mrr_at_10 |
| value: 84.615 |
| - type: mrr_at_100 |
| value: 84.745 |
| - type: mrr_at_1000 |
| value: 84.748 |
| - type: mrr_at_3 |
| value: 83.776 |
| - type: mrr_at_5 |
| value: 84.343 |
| - type: ndcg_at_1 |
| value: 76.40299999999999 |
| - type: ndcg_at_10 |
| value: 84.981 |
| - type: ndcg_at_100 |
| value: 86.00999999999999 |
| - type: ndcg_at_1000 |
| value: 86.252 |
| - type: ndcg_at_3 |
| value: 82.97 |
| - type: ndcg_at_5 |
| value: 84.152 |
| - type: precision_at_1 |
| value: 76.40299999999999 |
| - type: precision_at_10 |
| value: 10.446 |
| - type: precision_at_100 |
| value: 1.1199999999999999 |
| - type: precision_at_1000 |
| value: 0.116 |
| - type: precision_at_3 |
| value: 32.147999999999996 |
| - type: precision_at_5 |
| value: 20.135 |
| - type: recall_at_1 |
| value: 71.29100000000001 |
| - type: recall_at_10 |
| value: 93.232 |
| - type: recall_at_100 |
| value: 97.363 |
| - type: recall_at_1000 |
| value: 98.905 |
| - type: recall_at_3 |
| value: 87.893 |
| - type: recall_at_5 |
| value: 90.804 |
| - task: |
| type: Retrieval |
| dataset: |
| type: fiqa |
| name: MTEB FiQA2018 |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 18.667 |
| - type: map_at_10 |
| value: 30.853 |
| - type: map_at_100 |
| value: 32.494 |
| - type: map_at_1000 |
| value: 32.677 |
| - type: map_at_3 |
| value: 26.91 |
| - type: map_at_5 |
| value: 29.099000000000004 |
| - type: mrr_at_1 |
| value: 37.191 |
| - type: mrr_at_10 |
| value: 46.171 |
| - type: mrr_at_100 |
| value: 47.056 |
| - type: mrr_at_1000 |
| value: 47.099000000000004 |
| - type: mrr_at_3 |
| value: 44.059 |
| - type: mrr_at_5 |
| value: 45.147 |
| - type: ndcg_at_1 |
| value: 37.191 |
| - type: ndcg_at_10 |
| value: 38.437 |
| - type: ndcg_at_100 |
| value: 44.62 |
| - type: ndcg_at_1000 |
| value: 47.795 |
| - type: ndcg_at_3 |
| value: 35.003 |
| - type: ndcg_at_5 |
| value: 36.006 |
| - type: precision_at_1 |
| value: 37.191 |
| - type: precision_at_10 |
| value: 10.586 |
| - type: precision_at_100 |
| value: 1.688 |
| - type: precision_at_1000 |
| value: 0.22699999999999998 |
| - type: precision_at_3 |
| value: 23.302 |
| - type: precision_at_5 |
| value: 17.006 |
| - type: recall_at_1 |
| value: 18.667 |
| - type: recall_at_10 |
| value: 45.367000000000004 |
| - type: recall_at_100 |
| value: 68.207 |
| - type: recall_at_1000 |
| value: 87.072 |
| - type: recall_at_3 |
| value: 32.129000000000005 |
| - type: recall_at_5 |
| value: 37.719 |
| - task: |
| type: Retrieval |
| dataset: |
| type: hotpotqa |
| name: MTEB HotpotQA |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 39.494 |
| - type: map_at_10 |
| value: 66.223 |
| - type: map_at_100 |
| value: 67.062 |
| - type: map_at_1000 |
| value: 67.11500000000001 |
| - type: map_at_3 |
| value: 62.867 |
| - type: map_at_5 |
| value: 64.994 |
| - type: mrr_at_1 |
| value: 78.987 |
| - type: mrr_at_10 |
| value: 84.585 |
| - type: mrr_at_100 |
| value: 84.773 |
| - type: mrr_at_1000 |
| value: 84.77900000000001 |
| - type: mrr_at_3 |
| value: 83.592 |
| - type: mrr_at_5 |
| value: 84.235 |
| - type: ndcg_at_1 |
| value: 78.987 |
| - type: ndcg_at_10 |
| value: 73.64 |
| - type: ndcg_at_100 |
| value: 76.519 |
| - type: ndcg_at_1000 |
| value: 77.51 |
| - type: ndcg_at_3 |
| value: 68.893 |
| - type: ndcg_at_5 |
| value: 71.585 |
| - type: precision_at_1 |
| value: 78.987 |
| - type: precision_at_10 |
| value: 15.529000000000002 |
| - type: precision_at_100 |
| value: 1.7770000000000001 |
| - type: precision_at_1000 |
| value: 0.191 |
| - type: precision_at_3 |
| value: 44.808 |
| - type: precision_at_5 |
| value: 29.006999999999998 |
| - type: recall_at_1 |
| value: 39.494 |
| - type: recall_at_10 |
| value: 77.643 |
| - type: recall_at_100 |
| value: 88.825 |
| - type: recall_at_1000 |
| value: 95.321 |
| - type: recall_at_3 |
| value: 67.211 |
| - type: recall_at_5 |
| value: 72.519 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/imdb |
| name: MTEB ImdbClassification |
| config: default |
| split: test |
| revision: 3d86128a09e091d6018b6d26cad27f2739fc2db7 |
| metrics: |
| - type: accuracy |
| value: 85.55959999999999 |
| - type: ap |
| value: 80.7246500384617 |
| - type: f1 |
| value: 85.52336485065454 |
| - task: |
| type: Retrieval |
| dataset: |
| type: msmarco |
| name: MTEB MSMARCO |
| config: default |
| split: dev |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 23.631 |
| - type: map_at_10 |
| value: 36.264 |
| - type: map_at_100 |
| value: 37.428 |
| - type: map_at_1000 |
| value: 37.472 |
| - type: map_at_3 |
| value: 32.537 |
| - type: map_at_5 |
| value: 34.746 |
| - type: mrr_at_1 |
| value: 24.312 |
| - type: mrr_at_10 |
| value: 36.858000000000004 |
| - type: mrr_at_100 |
| value: 37.966 |
| - type: mrr_at_1000 |
| value: 38.004 |
| - type: mrr_at_3 |
| value: 33.188 |
| - type: mrr_at_5 |
| value: 35.367 |
| - type: ndcg_at_1 |
| value: 24.312 |
| - type: ndcg_at_10 |
| value: 43.126999999999995 |
| - type: ndcg_at_100 |
| value: 48.642 |
| - type: ndcg_at_1000 |
| value: 49.741 |
| - type: ndcg_at_3 |
| value: 35.589 |
| - type: ndcg_at_5 |
| value: 39.515 |
| - type: precision_at_1 |
| value: 24.312 |
| - type: precision_at_10 |
| value: 6.699 |
| - type: precision_at_100 |
| value: 0.9450000000000001 |
| - type: precision_at_1000 |
| value: 0.104 |
| - type: precision_at_3 |
| value: 15.153 |
| - type: precision_at_5 |
| value: 11.065999999999999 |
| - type: recall_at_1 |
| value: 23.631 |
| - type: recall_at_10 |
| value: 64.145 |
| - type: recall_at_100 |
| value: 89.41 |
| - type: recall_at_1000 |
| value: 97.83500000000001 |
| - type: recall_at_3 |
| value: 43.769000000000005 |
| - type: recall_at_5 |
| value: 53.169 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/mtop_domain |
| name: MTEB MTOPDomainClassification (en) |
| config: en |
| split: test |
| revision: d80d48c1eb48d3562165c59d59d0034df9fff0bf |
| metrics: |
| - type: accuracy |
| value: 93.4108527131783 |
| - type: f1 |
| value: 93.1415880261038 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/mtop_intent |
| name: MTEB MTOPIntentClassification (en) |
| config: en |
| split: test |
| revision: ae001d0e6b1228650b7bd1c2c65fb50ad11a8aba |
| metrics: |
| - type: accuracy |
| value: 77.24806201550388 |
| - type: f1 |
| value: 60.531916308197175 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_intent |
| name: MTEB MassiveIntentClassification (en) |
| config: en |
| split: test |
| revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7 |
| metrics: |
| - type: accuracy |
| value: 73.71553463349024 |
| - type: f1 |
| value: 71.70753174900791 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/amazon_massive_scenario |
| name: MTEB MassiveScenarioClassification (en) |
| config: en |
| split: test |
| revision: 7d571f92784cd94a019292a1f45445077d0ef634 |
| metrics: |
| - type: accuracy |
| value: 77.79757901815736 |
| - type: f1 |
| value: 77.83719850433258 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/medrxiv-clustering-p2p |
| name: MTEB MedrxivClusteringP2P |
| config: default |
| split: test |
| revision: e7a26af6f3ae46b30dde8737f02c07b1505bcc73 |
| metrics: |
| - type: v_measure |
| value: 33.74193296622113 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/medrxiv-clustering-s2s |
| name: MTEB MedrxivClusteringS2S |
| config: default |
| split: test |
| revision: 35191c8c0dca72d8ff3efcd72aa802307d469663 |
| metrics: |
| - type: v_measure |
| value: 30.64257594108566 |
| - task: |
| type: Reranking |
| dataset: |
| type: mteb/mind_small |
| name: MTEB MindSmallReranking |
| config: default |
| split: test |
| revision: 3bdac13927fdc888b903db93b2ffdbd90b295a69 |
| metrics: |
| - type: map |
| value: 30.811018518883625 |
| - type: mrr |
| value: 31.910376577445003 |
| - task: |
| type: Retrieval |
| dataset: |
| type: nfcorpus |
| name: MTEB NFCorpus |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 5.409 |
| - type: map_at_10 |
| value: 13.093 |
| - type: map_at_100 |
| value: 16.256999999999998 |
| - type: map_at_1000 |
| value: 17.617 |
| - type: map_at_3 |
| value: 9.555 |
| - type: map_at_5 |
| value: 11.428 |
| - type: mrr_at_1 |
| value: 45.201 |
| - type: mrr_at_10 |
| value: 54.179 |
| - type: mrr_at_100 |
| value: 54.812000000000005 |
| - type: mrr_at_1000 |
| value: 54.840999999999994 |
| - type: mrr_at_3 |
| value: 51.909000000000006 |
| - type: mrr_at_5 |
| value: 53.519000000000005 |
| - type: ndcg_at_1 |
| value: 43.189 |
| - type: ndcg_at_10 |
| value: 35.028 |
| - type: ndcg_at_100 |
| value: 31.226 |
| - type: ndcg_at_1000 |
| value: 39.678000000000004 |
| - type: ndcg_at_3 |
| value: 40.596 |
| - type: ndcg_at_5 |
| value: 38.75 |
| - type: precision_at_1 |
| value: 44.582 |
| - type: precision_at_10 |
| value: 25.974999999999998 |
| - type: precision_at_100 |
| value: 7.793 |
| - type: precision_at_1000 |
| value: 2.036 |
| - type: precision_at_3 |
| value: 38.493 |
| - type: precision_at_5 |
| value: 33.994 |
| - type: recall_at_1 |
| value: 5.409 |
| - type: recall_at_10 |
| value: 16.875999999999998 |
| - type: recall_at_100 |
| value: 30.316 |
| - type: recall_at_1000 |
| value: 60.891 |
| - type: recall_at_3 |
| value: 10.688 |
| - type: recall_at_5 |
| value: 13.832 |
| - task: |
| type: Retrieval |
| dataset: |
| type: nq |
| name: MTEB NQ |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 36.375 |
| - type: map_at_10 |
| value: 51.991 |
| - type: map_at_100 |
| value: 52.91400000000001 |
| - type: map_at_1000 |
| value: 52.93600000000001 |
| - type: map_at_3 |
| value: 48.014 |
| - type: map_at_5 |
| value: 50.381 |
| - type: mrr_at_1 |
| value: 40.759 |
| - type: mrr_at_10 |
| value: 54.617000000000004 |
| - type: mrr_at_100 |
| value: 55.301 |
| - type: mrr_at_1000 |
| value: 55.315000000000005 |
| - type: mrr_at_3 |
| value: 51.516 |
| - type: mrr_at_5 |
| value: 53.435 |
| - type: ndcg_at_1 |
| value: 40.759 |
| - type: ndcg_at_10 |
| value: 59.384 |
| - type: ndcg_at_100 |
| value: 63.157 |
| - type: ndcg_at_1000 |
| value: 63.654999999999994 |
| - type: ndcg_at_3 |
| value: 52.114000000000004 |
| - type: ndcg_at_5 |
| value: 55.986000000000004 |
| - type: precision_at_1 |
| value: 40.759 |
| - type: precision_at_10 |
| value: 9.411999999999999 |
| - type: precision_at_100 |
| value: 1.153 |
| - type: precision_at_1000 |
| value: 0.12 |
| - type: precision_at_3 |
| value: 23.329 |
| - type: precision_at_5 |
| value: 16.256999999999998 |
| - type: recall_at_1 |
| value: 36.375 |
| - type: recall_at_10 |
| value: 79.053 |
| - type: recall_at_100 |
| value: 95.167 |
| - type: recall_at_1000 |
| value: 98.82 |
| - type: recall_at_3 |
| value: 60.475 |
| - type: recall_at_5 |
| value: 69.327 |
| - task: |
| type: Retrieval |
| dataset: |
| type: quora |
| name: MTEB QuoraRetrieval |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 70.256 |
| - type: map_at_10 |
| value: 83.8 |
| - type: map_at_100 |
| value: 84.425 |
| - type: map_at_1000 |
| value: 84.444 |
| - type: map_at_3 |
| value: 80.906 |
| - type: map_at_5 |
| value: 82.717 |
| - type: mrr_at_1 |
| value: 80.97999999999999 |
| - type: mrr_at_10 |
| value: 87.161 |
| - type: mrr_at_100 |
| value: 87.262 |
| - type: mrr_at_1000 |
| value: 87.263 |
| - type: mrr_at_3 |
| value: 86.175 |
| - type: mrr_at_5 |
| value: 86.848 |
| - type: ndcg_at_1 |
| value: 80.97999999999999 |
| - type: ndcg_at_10 |
| value: 87.697 |
| - type: ndcg_at_100 |
| value: 88.959 |
| - type: ndcg_at_1000 |
| value: 89.09899999999999 |
| - type: ndcg_at_3 |
| value: 84.83800000000001 |
| - type: ndcg_at_5 |
| value: 86.401 |
| - type: precision_at_1 |
| value: 80.97999999999999 |
| - type: precision_at_10 |
| value: 13.261000000000001 |
| - type: precision_at_100 |
| value: 1.5150000000000001 |
| - type: precision_at_1000 |
| value: 0.156 |
| - type: precision_at_3 |
| value: 37.01 |
| - type: precision_at_5 |
| value: 24.298000000000002 |
| - type: recall_at_1 |
| value: 70.256 |
| - type: recall_at_10 |
| value: 94.935 |
| - type: recall_at_100 |
| value: 99.274 |
| - type: recall_at_1000 |
| value: 99.928 |
| - type: recall_at_3 |
| value: 86.602 |
| - type: recall_at_5 |
| value: 91.133 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/reddit-clustering |
| name: MTEB RedditClustering |
| config: default |
| split: test |
| revision: 24640382cdbf8abc73003fb0fa6d111a705499eb |
| metrics: |
| - type: v_measure |
| value: 56.322692497613104 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/reddit-clustering-p2p |
| name: MTEB RedditClusteringP2P |
| config: default |
| split: test |
| revision: 282350215ef01743dc01b456c7f5241fa8937f16 |
| metrics: |
| - type: v_measure |
| value: 61.895813503775074 |
| - task: |
| type: Retrieval |
| dataset: |
| type: scidocs |
| name: MTEB SCIDOCS |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 4.338 |
| - type: map_at_10 |
| value: 10.767 |
| - type: map_at_100 |
| value: 12.537999999999998 |
| - type: map_at_1000 |
| value: 12.803999999999998 |
| - type: map_at_3 |
| value: 7.788 |
| - type: map_at_5 |
| value: 9.302000000000001 |
| - type: mrr_at_1 |
| value: 21.4 |
| - type: mrr_at_10 |
| value: 31.637999999999998 |
| - type: mrr_at_100 |
| value: 32.688 |
| - type: mrr_at_1000 |
| value: 32.756 |
| - type: mrr_at_3 |
| value: 28.433000000000003 |
| - type: mrr_at_5 |
| value: 30.178 |
| - type: ndcg_at_1 |
| value: 21.4 |
| - type: ndcg_at_10 |
| value: 18.293 |
| - type: ndcg_at_100 |
| value: 25.274 |
| - type: ndcg_at_1000 |
| value: 30.284 |
| - type: ndcg_at_3 |
| value: 17.391000000000002 |
| - type: ndcg_at_5 |
| value: 15.146999999999998 |
| - type: precision_at_1 |
| value: 21.4 |
| - type: precision_at_10 |
| value: 9.48 |
| - type: precision_at_100 |
| value: 1.949 |
| - type: precision_at_1000 |
| value: 0.316 |
| - type: precision_at_3 |
| value: 16.167 |
| - type: precision_at_5 |
| value: 13.22 |
| - type: recall_at_1 |
| value: 4.338 |
| - type: recall_at_10 |
| value: 19.213 |
| - type: recall_at_100 |
| value: 39.562999999999995 |
| - type: recall_at_1000 |
| value: 64.08 |
| - type: recall_at_3 |
| value: 9.828000000000001 |
| - type: recall_at_5 |
| value: 13.383000000000001 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sickr-sts |
| name: MTEB SICK-R |
| config: default |
| split: test |
| revision: a6ea5a8cab320b040a23452cc28066d9beae2cee |
| metrics: |
| - type: cos_sim_pearson |
| value: 82.42568163642142 |
| - type: cos_sim_spearman |
| value: 78.5797159641342 |
| - type: euclidean_pearson |
| value: 80.22151260811604 |
| - type: euclidean_spearman |
| value: 78.5797151953878 |
| - type: manhattan_pearson |
| value: 80.21224215864788 |
| - type: manhattan_spearman |
| value: 78.55641478381344 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts12-sts |
| name: MTEB STS12 |
| config: default |
| split: test |
| revision: a0d554a64d88156834ff5ae9920b964011b16384 |
| metrics: |
| - type: cos_sim_pearson |
| value: 85.44020710812569 |
| - type: cos_sim_spearman |
| value: 78.91631735081286 |
| - type: euclidean_pearson |
| value: 81.64188964182102 |
| - type: euclidean_spearman |
| value: 78.91633286881678 |
| - type: manhattan_pearson |
| value: 81.69294748512496 |
| - type: manhattan_spearman |
| value: 78.93438558002656 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts13-sts |
| name: MTEB STS13 |
| config: default |
| split: test |
| revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca |
| metrics: |
| - type: cos_sim_pearson |
| value: 84.27165426412311 |
| - type: cos_sim_spearman |
| value: 85.40429140249618 |
| - type: euclidean_pearson |
| value: 84.7509580724893 |
| - type: euclidean_spearman |
| value: 85.40429140249618 |
| - type: manhattan_pearson |
| value: 84.76488289321308 |
| - type: manhattan_spearman |
| value: 85.4256793698708 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts14-sts |
| name: MTEB STS14 |
| config: default |
| split: test |
| revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375 |
| metrics: |
| - type: cos_sim_pearson |
| value: 83.138851760732 |
| - type: cos_sim_spearman |
| value: 81.64101363896586 |
| - type: euclidean_pearson |
| value: 82.55165038934942 |
| - type: euclidean_spearman |
| value: 81.64105257080502 |
| - type: manhattan_pearson |
| value: 82.52802949883335 |
| - type: manhattan_spearman |
| value: 81.61255430718158 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts15-sts |
| name: MTEB STS15 |
| config: default |
| split: test |
| revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3 |
| metrics: |
| - type: cos_sim_pearson |
| value: 86.0654695484029 |
| - type: cos_sim_spearman |
| value: 87.20408521902229 |
| - type: euclidean_pearson |
| value: 86.8110651362115 |
| - type: euclidean_spearman |
| value: 87.20408521902229 |
| - type: manhattan_pearson |
| value: 86.77984656478691 |
| - type: manhattan_spearman |
| value: 87.1719947099227 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/sts16-sts |
| name: MTEB STS16 |
| config: default |
| split: test |
| revision: 4d8694f8f0e0100860b497b999b3dbed754a0513 |
| metrics: |
| - type: cos_sim_pearson |
| value: 83.77823915496512 |
| - type: cos_sim_spearman |
| value: 85.43566325729779 |
| - type: euclidean_pearson |
| value: 84.5396956658821 |
| - type: euclidean_spearman |
| value: 85.43566325729779 |
| - type: manhattan_pearson |
| value: 84.5665398848169 |
| - type: manhattan_spearman |
| value: 85.44375870303232 |
| - 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: 87.20030208471798 |
| - type: cos_sim_spearman |
| value: 87.20485505076539 |
| - type: euclidean_pearson |
| value: 88.10588324368722 |
| - type: euclidean_spearman |
| value: 87.20485505076539 |
| - type: manhattan_pearson |
| value: 87.92324770415183 |
| - type: manhattan_spearman |
| value: 87.0571314561877 |
| - 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: 63.06093161604453 |
| - type: cos_sim_spearman |
| value: 64.2163140357722 |
| - type: euclidean_pearson |
| value: 65.27589680994006 |
| - type: euclidean_spearman |
| value: 64.2163140357722 |
| - type: manhattan_pearson |
| value: 65.45904383711101 |
| - type: manhattan_spearman |
| value: 64.55404716679305 |
| - task: |
| type: STS |
| dataset: |
| type: mteb/stsbenchmark-sts |
| name: MTEB STSBenchmark |
| config: default |
| split: test |
| revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831 |
| metrics: |
| - type: cos_sim_pearson |
| value: 84.32976164578706 |
| - type: cos_sim_spearman |
| value: 85.54302197678368 |
| - type: euclidean_pearson |
| value: 85.26307149193056 |
| - type: euclidean_spearman |
| value: 85.54302197678368 |
| - type: manhattan_pearson |
| value: 85.26647282029371 |
| - type: manhattan_spearman |
| value: 85.5316135265568 |
| - task: |
| type: Reranking |
| dataset: |
| type: mteb/scidocs-reranking |
| name: MTEB SciDocsRR |
| config: default |
| split: test |
| revision: d3c5e1fc0b855ab6097bf1cda04dd73947d7caab |
| metrics: |
| - type: map |
| value: 81.44675968318754 |
| - type: mrr |
| value: 94.92741826075158 |
| - task: |
| type: Retrieval |
| dataset: |
| type: scifact |
| name: MTEB SciFact |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 56.34400000000001 |
| - type: map_at_10 |
| value: 65.927 |
| - type: map_at_100 |
| value: 66.431 |
| - type: map_at_1000 |
| value: 66.461 |
| - type: map_at_3 |
| value: 63.529 |
| - type: map_at_5 |
| value: 64.818 |
| - type: mrr_at_1 |
| value: 59.333000000000006 |
| - type: mrr_at_10 |
| value: 67.54599999999999 |
| - type: mrr_at_100 |
| value: 67.892 |
| - type: mrr_at_1000 |
| value: 67.917 |
| - type: mrr_at_3 |
| value: 65.778 |
| - type: mrr_at_5 |
| value: 66.794 |
| - type: ndcg_at_1 |
| value: 59.333000000000006 |
| - type: ndcg_at_10 |
| value: 70.5 |
| - type: ndcg_at_100 |
| value: 72.688 |
| - type: ndcg_at_1000 |
| value: 73.483 |
| - type: ndcg_at_3 |
| value: 66.338 |
| - type: ndcg_at_5 |
| value: 68.265 |
| - type: precision_at_1 |
| value: 59.333000000000006 |
| - type: precision_at_10 |
| value: 9.3 |
| - type: precision_at_100 |
| value: 1.053 |
| - type: precision_at_1000 |
| value: 0.11199999999999999 |
| - type: precision_at_3 |
| value: 25.889 |
| - type: precision_at_5 |
| value: 16.866999999999997 |
| - type: recall_at_1 |
| value: 56.34400000000001 |
| - type: recall_at_10 |
| value: 82.789 |
| - type: recall_at_100 |
| value: 92.767 |
| - type: recall_at_1000 |
| value: 99 |
| - type: recall_at_3 |
| value: 71.64399999999999 |
| - type: recall_at_5 |
| value: 76.322 |
| - task: |
| type: PairClassification |
| dataset: |
| type: mteb/sprintduplicatequestions-pairclassification |
| name: MTEB SprintDuplicateQuestions |
| config: default |
| split: test |
| revision: d66bd1f72af766a5cc4b0ca5e00c162f89e8cc46 |
| metrics: |
| - type: cos_sim_accuracy |
| value: 99.75742574257426 |
| - type: cos_sim_ap |
| value: 93.52081548447406 |
| - type: cos_sim_f1 |
| value: 87.33850129198966 |
| - type: cos_sim_precision |
| value: 90.37433155080214 |
| - type: cos_sim_recall |
| value: 84.5 |
| - type: dot_accuracy |
| value: 99.75742574257426 |
| - type: dot_ap |
| value: 93.52081548447406 |
| - type: dot_f1 |
| value: 87.33850129198966 |
| - type: dot_precision |
| value: 90.37433155080214 |
| - type: dot_recall |
| value: 84.5 |
| - type: euclidean_accuracy |
| value: 99.75742574257426 |
| - type: euclidean_ap |
| value: 93.52081548447406 |
| - type: euclidean_f1 |
| value: 87.33850129198966 |
| - type: euclidean_precision |
| value: 90.37433155080214 |
| - type: euclidean_recall |
| value: 84.5 |
| - type: manhattan_accuracy |
| value: 99.75841584158415 |
| - type: manhattan_ap |
| value: 93.4975678585854 |
| - type: manhattan_f1 |
| value: 87.26708074534162 |
| - type: manhattan_precision |
| value: 90.45064377682404 |
| - type: manhattan_recall |
| value: 84.3 |
| - type: max_accuracy |
| value: 99.75841584158415 |
| - type: max_ap |
| value: 93.52081548447406 |
| - type: max_f1 |
| value: 87.33850129198966 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/stackexchange-clustering |
| name: MTEB StackExchangeClustering |
| config: default |
| split: test |
| revision: 6cbc1f7b2bc0622f2e39d2c77fa502909748c259 |
| metrics: |
| - type: v_measure |
| value: 64.31437036686651 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/stackexchange-clustering-p2p |
| name: MTEB StackExchangeClusteringP2P |
| config: default |
| split: test |
| revision: 815ca46b2622cec33ccafc3735d572c266efdb44 |
| metrics: |
| - type: v_measure |
| value: 33.25569319007206 |
| - task: |
| type: Reranking |
| dataset: |
| type: mteb/stackoverflowdupquestions-reranking |
| name: MTEB StackOverflowDupQuestions |
| config: default |
| split: test |
| revision: e185fbe320c72810689fc5848eb6114e1ef5ec69 |
| metrics: |
| - type: map |
| value: 49.90474939720706 |
| - type: mrr |
| value: 50.568115503777264 |
| - task: |
| type: Summarization |
| dataset: |
| type: mteb/summeval |
| name: MTEB SummEval |
| config: default |
| split: test |
| revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c |
| metrics: |
| - type: cos_sim_pearson |
| value: 29.866828641244712 |
| - type: cos_sim_spearman |
| value: 30.077555055873866 |
| - type: dot_pearson |
| value: 29.866832988572266 |
| - type: dot_spearman |
| value: 30.077555055873866 |
| - task: |
| type: Retrieval |
| dataset: |
| type: trec-covid |
| name: MTEB TRECCOVID |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 0.232 |
| - type: map_at_10 |
| value: 2.094 |
| - type: map_at_100 |
| value: 11.971 |
| - type: map_at_1000 |
| value: 28.158 |
| - type: map_at_3 |
| value: 0.688 |
| - type: map_at_5 |
| value: 1.114 |
| - type: mrr_at_1 |
| value: 88 |
| - type: mrr_at_10 |
| value: 93.4 |
| - type: mrr_at_100 |
| value: 93.4 |
| - type: mrr_at_1000 |
| value: 93.4 |
| - type: mrr_at_3 |
| value: 93 |
| - type: mrr_at_5 |
| value: 93.4 |
| - type: ndcg_at_1 |
| value: 84 |
| - type: ndcg_at_10 |
| value: 79.923 |
| - type: ndcg_at_100 |
| value: 61.17 |
| - type: ndcg_at_1000 |
| value: 53.03 |
| - type: ndcg_at_3 |
| value: 84.592 |
| - type: ndcg_at_5 |
| value: 82.821 |
| - type: precision_at_1 |
| value: 88 |
| - type: precision_at_10 |
| value: 85 |
| - type: precision_at_100 |
| value: 63.019999999999996 |
| - type: precision_at_1000 |
| value: 23.554 |
| - type: precision_at_3 |
| value: 89.333 |
| - type: precision_at_5 |
| value: 87.2 |
| - type: recall_at_1 |
| value: 0.232 |
| - type: recall_at_10 |
| value: 2.255 |
| - type: recall_at_100 |
| value: 14.823 |
| - type: recall_at_1000 |
| value: 49.456 |
| - type: recall_at_3 |
| value: 0.718 |
| - type: recall_at_5 |
| value: 1.175 |
| - task: |
| type: Retrieval |
| dataset: |
| type: webis-touche2020 |
| name: MTEB Touche2020 |
| config: default |
| split: test |
| revision: None |
| metrics: |
| - type: map_at_1 |
| value: 2.547 |
| - type: map_at_10 |
| value: 11.375 |
| - type: map_at_100 |
| value: 18.194 |
| - type: map_at_1000 |
| value: 19.749 |
| - type: map_at_3 |
| value: 5.825 |
| - type: map_at_5 |
| value: 8.581 |
| - type: mrr_at_1 |
| value: 32.653 |
| - type: mrr_at_10 |
| value: 51.32 |
| - type: mrr_at_100 |
| value: 51.747 |
| - type: mrr_at_1000 |
| value: 51.747 |
| - type: mrr_at_3 |
| value: 47.278999999999996 |
| - type: mrr_at_5 |
| value: 48.605 |
| - type: ndcg_at_1 |
| value: 29.592000000000002 |
| - type: ndcg_at_10 |
| value: 28.151 |
| - type: ndcg_at_100 |
| value: 39.438 |
| - type: ndcg_at_1000 |
| value: 50.769 |
| - type: ndcg_at_3 |
| value: 30.758999999999997 |
| - type: ndcg_at_5 |
| value: 30.366 |
| - type: precision_at_1 |
| value: 32.653 |
| - type: precision_at_10 |
| value: 25.714 |
| - type: precision_at_100 |
| value: 8.041 |
| - type: precision_at_1000 |
| value: 1.555 |
| - type: precision_at_3 |
| value: 33.333 |
| - type: precision_at_5 |
| value: 31.837 |
| - type: recall_at_1 |
| value: 2.547 |
| - type: recall_at_10 |
| value: 18.19 |
| - type: recall_at_100 |
| value: 49.538 |
| - type: recall_at_1000 |
| value: 83.86 |
| - type: recall_at_3 |
| value: 7.329 |
| - type: recall_at_5 |
| value: 11.532 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/toxic_conversations_50k |
| name: MTEB ToxicConversationsClassification |
| config: default |
| split: test |
| revision: d7c0de2777da35d6aae2200a62c6e0e5af397c4c |
| metrics: |
| - type: accuracy |
| value: 71.4952 |
| - type: ap |
| value: 14.793362635531409 |
| - type: f1 |
| value: 55.204635551516915 |
| - task: |
| type: Classification |
| dataset: |
| type: mteb/tweet_sentiment_extraction |
| name: MTEB TweetSentimentExtractionClassification |
| config: default |
| split: test |
| revision: d604517c81ca91fe16a244d1248fc021f9ecee7a |
| metrics: |
| - type: accuracy |
| value: 61.5365025466893 |
| - type: f1 |
| value: 61.81742556334845 |
| - task: |
| type: Clustering |
| dataset: |
| type: mteb/twentynewsgroups-clustering |
| name: MTEB TwentyNewsgroupsClustering |
| config: default |
| split: test |
| revision: 6125ec4e24fa026cec8a478383ee943acfbd5449 |
| metrics: |
| - type: v_measure |
| value: 49.05531070301185 |
| - task: |
| type: PairClassification |
| dataset: |
| type: mteb/twittersemeval2015-pairclassification |
| name: MTEB TwitterSemEval2015 |
| config: default |
| split: test |
| revision: 70970daeab8776df92f5ea462b6173c0b46fd2d1 |
| metrics: |
| - type: cos_sim_accuracy |
| value: 86.51725576682364 |
| - type: cos_sim_ap |
| value: 75.2292304265163 |
| - type: cos_sim_f1 |
| value: 69.54022988505749 |
| - type: cos_sim_precision |
| value: 63.65629110039457 |
| - type: cos_sim_recall |
| value: 76.62269129287598 |
| - type: dot_accuracy |
| value: 86.51725576682364 |
| - type: dot_ap |
| value: 75.22922386081054 |
| - type: dot_f1 |
| value: 69.54022988505749 |
| - type: dot_precision |
| value: 63.65629110039457 |
| - type: dot_recall |
| value: 76.62269129287598 |
| - type: euclidean_accuracy |
| value: 86.51725576682364 |
| - type: euclidean_ap |
| value: 75.22925730473472 |
| - type: euclidean_f1 |
| value: 69.54022988505749 |
| - type: euclidean_precision |
| value: 63.65629110039457 |
| - type: euclidean_recall |
| value: 76.62269129287598 |
| - type: manhattan_accuracy |
| value: 86.52321630804077 |
| - type: manhattan_ap |
| value: 75.20608115037336 |
| - type: manhattan_f1 |
| value: 69.60000000000001 |
| - type: manhattan_precision |
| value: 64.37219730941705 |
| - type: manhattan_recall |
| value: 75.75197889182058 |
| - type: max_accuracy |
| value: 86.52321630804077 |
| - type: max_ap |
| value: 75.22925730473472 |
| - type: max_f1 |
| value: 69.60000000000001 |
| - task: |
| type: PairClassification |
| dataset: |
| type: mteb/twitterurlcorpus-pairclassification |
| name: MTEB TwitterURLCorpus |
| config: default |
| split: test |
| revision: 8b6510b0b1fa4e4c4f879467980e9be563ec1cdf |
| metrics: |
| - type: cos_sim_accuracy |
| value: 89.34877944657896 |
| - type: cos_sim_ap |
| value: 86.71257569277373 |
| - type: cos_sim_f1 |
| value: 79.10386355986088 |
| - type: cos_sim_precision |
| value: 76.91468470434214 |
| - type: cos_sim_recall |
| value: 81.4213119802895 |
| - type: dot_accuracy |
| value: 89.34877944657896 |
| - type: dot_ap |
| value: 86.71257133133368 |
| - type: dot_f1 |
| value: 79.10386355986088 |
| - type: dot_precision |
| value: 76.91468470434214 |
| - type: dot_recall |
| value: 81.4213119802895 |
| - type: euclidean_accuracy |
| value: 89.34877944657896 |
| - type: euclidean_ap |
| value: 86.71257651501476 |
| - type: euclidean_f1 |
| value: 79.10386355986088 |
| - type: euclidean_precision |
| value: 76.91468470434214 |
| - type: euclidean_recall |
| value: 81.4213119802895 |
| - type: manhattan_accuracy |
| value: 89.35848177901967 |
| - type: manhattan_ap |
| value: 86.69330615469126 |
| - type: manhattan_f1 |
| value: 79.13867741453949 |
| - type: manhattan_precision |
| value: 76.78881807647741 |
| - type: manhattan_recall |
| value: 81.63689559593472 |
| - type: max_accuracy |
| value: 89.35848177901967 |
| - type: max_ap |
| value: 86.71257651501476 |
| - type: max_f1 |
| value: 79.13867741453949 |
| license: apache-2.0 |
| language: |
| - en |
| new_version: nomic-ai/nomic-embed-text-v1.5 |
| --- |
| |
|
|
| # nomic-embed-text-v1: A Reproducible Long Context (8192) Text Embedder |
|
|
| [Blog](https://www.nomic.ai/blog/posts/nomic-embed-text-v1) | [Technical Report](https://arxiv.org/abs/2402.01613) | [AWS SageMaker](https://aws.amazon.com/marketplace/seller-profile?id=seller-tpqidcj54zawi) | [Atlas Embedding and Unstructured Data Analytics Platform](https://atlas.nomic.ai) |
|
|
| `nomic-embed-text-v1` is 8192 context length text encoder that surpasses OpenAI text-embedding-ada-002 and text-embedding-3-small performance on short and long context tasks. |
|
|
| # Performance Benchmarks |
|
|
| | Name | SeqLen | MTEB | LoCo | Jina Long Context | Open Weights | Open Training Code | Open Data | |
| | :-------------------------------:| :----- | :-------- | :------: | :---------------: | :-----------: | :----------------: | :---------- | |
| | nomic-embed-text-v1 | 8192 | **62.39** |**85.53** | 54.16 | ✅ | ✅ | ✅ | |
| | jina-embeddings-v2-base-en | 8192 | 60.39 | 85.45 | 51.90 | ✅ | ❌ | ❌ | |
| | text-embedding-3-small | 8191 | 62.26 | 82.40 | **58.20** | ❌ | ❌ | ❌ | |
| | text-embedding-ada-002 | 8191 | 60.99 | 52.7 | 55.25 | ❌ | ❌ | ❌ | |
|
|
|
|
| **Exciting Update!**: `nomic-embed-text-v1` is now multimodal! [nomic-embed-vision-v1](https://huggingface.co/nomic-ai/nomic-embed-vision-v1) is aligned to the embedding space of `nomic-embed-text-v1`, meaning any text embedding is multimodal! |
|
|
| ## Usage |
|
|
| **Important**: the text prompt *must* include a *task instruction prefix*, instructing the model which task is being performed. |
|
|
| For example, if you are implementing a RAG application, you embed your documents as `search_document: <text here>` and embed your user queries as `search_query: <text here>`. |
|
|
| **Notice**: From transformers v5.5.0 and sentence transformers v5.3.0, `trust_remote_code=True` will no longer be necessary. This will only be possible with the text-only series as of now. |
|
|
| ## Task instruction prefixes |
|
|
| ### `search_document` |
| |
| #### Purpose: embed texts as documents from a dataset |
| |
| This prefix is used for embedding texts as documents, for example as documents for a RAG index. |
| |
| ```python |
| from sentence_transformers import SentenceTransformer |
|
|
| model = SentenceTransformer("nomic-ai/nomic-embed-text-v1") |
| sentences = ['search_document: TSNE is a dimensionality reduction algorithm created by Laurens van Der Maaten'] |
| embeddings = model.encode(sentences) |
| print(embeddings) |
| ``` |
| |
| ### `search_query` |
|
|
| #### Purpose: embed texts as questions to answer |
|
|
| This prefix is used for embedding texts as questions that documents from a dataset could resolve, for example as queries to be answered by a RAG application. |
|
|
| ```python |
| from sentence_transformers import SentenceTransformer |
| |
| model = SentenceTransformer("nomic-ai/nomic-embed-text-v1") |
| sentences = ['search_query: Who is Laurens van Der Maaten?'] |
| embeddings = model.encode(sentences) |
| print(embeddings) |
| ``` |
|
|
| ### `clustering` |
|
|
| #### Purpose: embed texts to group them into clusters |
|
|
| This prefix is used for embedding texts in order to group them into clusters, discover common topics, or remove semantic duplicates. |
|
|
| ```python |
| from sentence_transformers import SentenceTransformer |
| |
| model = SentenceTransformer("nomic-ai/nomic-embed-text-v1") |
| sentences = ['clustering: the quick brown fox'] |
| embeddings = model.encode(sentences) |
| print(embeddings) |
| ``` |
|
|
| ### `classification` |
|
|
| #### Purpose: embed texts to classify them |
|
|
| This prefix is used for embedding texts into vectors that will be used as features for a classification model |
|
|
| ```python |
| from sentence_transformers import SentenceTransformer |
| |
| model = SentenceTransformer("nomic-ai/nomic-embed-text-v1") |
| sentences = ['classification: the quick brown fox'] |
| embeddings = model.encode(sentences) |
| print(embeddings) |
| ``` |
|
|
| ### Sentence Transformers |
| ```python |
| from sentence_transformers import SentenceTransformer |
| |
| model = SentenceTransformer("nomic-ai/nomic-embed-text-v1") |
| sentences = ['search_query: What is TSNE?', 'search_query: Who is Laurens van der Maaten?'] |
| embeddings = model.encode(sentences) |
| print(embeddings) |
| ``` |
|
|
| ### Transformers |
|
|
| ```python |
| import torch |
| import torch.nn.functional as F |
| from transformers import AutoTokenizer, AutoModel |
| |
| def mean_pooling(model_output, attention_mask): |
| token_embeddings = model_output[0] |
| input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() |
| return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9) |
| |
| sentences = ['search_query: What is TSNE?', 'search_query: Who is Laurens van der Maaten?'] |
| |
| tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased') |
| model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1') |
| model.eval() |
| |
| encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt') |
| |
| with torch.no_grad(): |
| model_output = model(**encoded_input) |
| |
| embeddings = mean_pooling(model_output, encoded_input['attention_mask']) |
| embeddings = F.normalize(embeddings, p=2, dim=1) |
| print(embeddings) |
| ``` |
|
|
| The model natively supports scaling of the sequence length past 2048 tokens. To do so, |
|
|
| ```diff |
| - tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased') |
| + tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased', model_max_length=8192) |
| |
| |
| - model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1') |
| + rope_parameters = {"rope_theta": 1000.0, "rope_type": "dynamic", "factor": 2.0} |
| + model = AutoModel.from_pretrained('nomic-ai/nomic-embed-text-v1', rope_parameters=rope_parameters) |
| ``` |
|
|
| ### Transformers.js |
|
|
| ```js |
| import { pipeline } from '@xenova/transformers'; |
| |
| // Create a feature extraction pipeline |
| const extractor = await pipeline('feature-extraction', 'nomic-ai/nomic-embed-text-v1', { |
| quantized: false, // Comment out this line to use the quantized version |
| }); |
| |
| // Compute sentence embeddings |
| const texts = ['search_query: What is TSNE?', 'search_query: Who is Laurens van der Maaten?']; |
| const embeddings = await extractor(texts, { pooling: 'mean', normalize: true }); |
| console.log(embeddings); |
| ``` |
|
|
| ## Nomic API |
|
|
| The easiest way to get started with Nomic Embed is through the Nomic Embedding API. |
|
|
| Generating embeddings with the `nomic` Python client is as easy as |
|
|
| ```python |
| from nomic import embed |
| |
| output = embed.text( |
| texts=['Nomic Embedding API', '#keepAIOpen'], |
| model='nomic-embed-text-v1', |
| task_type='search_document' |
| ) |
| |
| print(output) |
| ``` |
|
|
| For more information, see the [API reference](https://docs.nomic.ai/reference/endpoints/nomic-embed-text) |
|
|
|
|
| ## Training |
| Click the Nomic Atlas map below to visualize a 5M sample of our contrastive pretraining data! |
|
|
| [](https://atlas.nomic.ai/map/nomic-text-embed-v1-5m-sample) |
|
|
| We train our embedder using a multi-stage training pipeline. Starting from a long-context [BERT model](https://huggingface.co/nomic-ai/nomic-bert-2048), |
| the first unsupervised contrastive stage trains on a dataset generated from weakly related text pairs, such as question-answer pairs from forums like StackExchange and Quora, title-body pairs from Amazon reviews, and summarizations from news articles. |
|
|
| In the second finetuning stage, higher quality labeled datasets such as search queries and answers from web searches are leveraged. Data curation and hard-example mining is crucial in this stage. |
|
|
| For more details, see the Nomic Embed [Technical Report](https://static.nomic.ai/reports/2024_Nomic_Embed_Text_Technical_Report.pdf) and corresponding [blog post](https://blog.nomic.ai/posts/nomic-embed-text-v1). |
|
|
| Training data to train the models is released in its entirety. For more details, see the `contrastors` [repository](https://github.com/nomic-ai/contrastors) |
|
|
|
|
| # Join the Nomic Community |
|
|
| - Nomic: [https://nomic.ai](https://nomic.ai) |
| - Discord: [https://discord.gg/myY5YDR8z8](https://discord.gg/myY5YDR8z8) |
| - Twitter: [https://twitter.com/nomic_ai](https://twitter.com/nomic_ai) |
|
|
|
|
| # Citation |
|
|
| If you find the model, dataset, or training code useful, please cite our work |
|
|
| ```bibtex |
| @misc{nussbaum2024nomic, |
| title={Nomic Embed: Training a Reproducible Long Context Text Embedder}, |
| author={Zach Nussbaum and John X. Morris and Brandon Duderstadt and Andriy Mulyar}, |
| year={2024}, |
| eprint={2402.01613}, |
| archivePrefix={arXiv}, |
| primaryClass={cs.CL} |
| } |
| ``` |
